(upbeat music) >> Good afternoon, fellow cloud nerds, and welcome back to theCUBE's live coverage of AWS re:Invent, here in a fabulous Sin City, Las Vegas, Nevada. My name is Savannah Peterson, joined by my fabulous co-host, John Furrier. John, how you feeling? >> Great, feeling good Just getting going. Day one of four more, three more days after today. >> Woo! Yeah. >> So much conversation. Talking about business transformation as cloud goes next level- >> Hot topic here for sure. >> Next generation. Data's classic is still around, but the next gen cloud's here, it's changing the game. Lot more AI, machine learning, a lot more business value. I think it's going to be exciting. Next segment's going to be awesome. >> It feels like one of those years where there's just a ton of momentum. I don't think it's just because we're back in person at scale, you can see the literally thousands of people behind us while we're here on set conducting these interviews. Our bold and brave guests, just like the two we have here, combating the noise, the libations, and everything else going on on the show floor. Please help me welcome Mike from Science Logic and Ajay from Zebrium. Gentlemen, welcome to the show floor. >> Thank you. >> Thank you Savannah. It's great to be here. >> How you feeling? Are you feeling the buzz, Mike? Feeling the energy? >> It's tough to not feel and hear the buzz, Savannah >> Savannah: Yeah. (all laughing) >> John: Can you hear me? >> Savannah: Yeah, yeah, yeah. Can you hear me now? What about you, Ajay? How's it feel to be here? >> Yeah, this is high energy. I'm really happy it's bounced back from COVID. I was a little concerned about attendance. This is hopping. >> Yeah, I feel it. It just, you can definitely feel the energy, the sense of community. We're all here for the right reasons. So I know that, I want to set the stage for everyone watching, Zebrium was recently acquired by Science Logic. Mike, can you tell us a little bit about that and what it means for the company? >> Mike: Sure, sure. Well, first of all, science logic, as you may know, has been in the monitoring space for a long time now, and what- >> Savannah: 20 years I believe. >> Yeah. >> Savannah: Just about. >> And what we've seen is a shift from kind of monitoring infrastructure, to monitoring these increasingly complex modern cloud native applications, right? And so this is part of a journey that we've been on at Science Logic to really modernize how enterprises of all sizes manage their IT estate. Okay? So, managing, now workloads that are increasingly in the public cloud, outside the four walls of the enterprise, workloads that are increasingly complex. They're microservices based, they're container based. >> Mhmm. >> Mike: And the rate of change, just because of things like CICD, and agile development has also increased the complexity in the typical IT environment. So all these things have conspired to make the traditional tools and processes of managing IT and IT applications much more difficult. They just don't scale. One of the things that we've seen recently, Savannah is this shift in sort of moving to cloud native applications, right? >> Huge shift. >> Mike: Today it only incorporates about roughly 25% of the typical IT portfolio, but most of the projections we've seen indicate that that's going to invert in about three years. 75% of applications will be what I call cloud native. And so this really requires different technologies to understand what's going on with those applications. And so Zebrium interested us when we were looking at partners at the beginning of this year as they have a super innovative approach to understanding really what's going on with any cloud native application. And they really distill, they separate the complexity out of the equation and they used machine learning to tremendous effect to rapidly understand the root cause of an application failure. And so I was introduced to Ajay, beginning of this year, actually. It feels like it's been a long time now. But we've been on this journey together throughout 2022, and we're thrilled to have Zebrium now, part of the Science Logic family. >> Ajay, Zebrium saves people a lot of time. Obviously, I've worked with developers and seen that struggle when things break, shortening that time to recovery and understanding is so critical. Can you tell us a little bit about what's under the hood and how the ML works to make that happen? >> Ajay: Yeah. So the goal is to figure out not just that something went wrong, but what went wrong. >> Savannah: Right. >> And we took, you know, based on a couple of decades of experience from my co-founders- >> Savannah: Casual couple of decades, came into went into this product just to call that out. Yeah, great. >> Exactly. It took some general learnings about the nature of software and when software breaks, what tends to happen, you tend to see unusual things happen, and they lead to bad things happening. It's very simple. >> Yes. >> It turns out- >> Savannah: Mutations lead to bad things happening, generally speaking. >> So what Zebrium's really good at is identifying those rare things accurately and then figuring out how they connect, or correlate to the bad things, the errors, the warnings, the alerts. So the machine learning has many stages to it, but at its heart it's classifying the event, catalog of any application stack, figuring out what's rare, and when things start to break it's telling you this cluster of events is both unusual, and unlikely to be random, and it's very likely the root cause report for the problem you're trying to solve. We then added some nice enhancements, such as correlation with knowledge spaces in, on the public internet. If someone's ever solved that problem before, we're able to find a match, and pull that back into our platform. But the at the heart, it was a technology that can find rare events and find the connections with other events. >> John: Yeah, and this is the theme of re:Invent this year, data, the role of data, solving end-to-end complexities. One, you mentioned that. Two, I think the Mike, your point about developers and the CICD pipeline is where DevOps is. That is what IT now is. So, if you take digital transformation to its conclusion, or its path and continue it, IT is DevOps. So the developers are actually doing the IT in their coding, hence the shift to autonomous IT. >> Mike: Right, right. Now, those other functions at IT used to be a department, not anymore, or they still are, so, but they'll go away, is security and data teams. You're starting to see the formation of- >> Mike: Yep. >> New replacements to IT as a function to support the developers who are building the applications that will be the company. >> That's right. Yeah. >> John: I mean that's, and do you agree with that statement? >> Yeah, I really do. And you know, collectively independent of whether it's like traditional IT, or it's DevOps, or whatever it is, the enterprise as a whole needs to understand how the infrastructure is deployed, the health of that infrastructure, and more importantly the applications that are hosted in the infrastructure. How are they doing? What's the health? And what we are seeing, and what we're trying to facilitate at Science Logic is really changed the lens of IT, from being low level compute, storage, and networking, to looking at everything through a services lens, looking at the services being delivered by IT, back to the business, and understanding things through a services lens. And Zebrium really compliments that mission that we've been on, by providing, cause a lot of cases, service equal equal application, and they can provide that kind of very real time view of service health in, you know, kind of the IT- >> And automation is beautiful there too, because, as you get into some of the scale- >> Yeah >> Ajay's. understanding how to do this fast is a key component. >> Yeah. So scale, you, you've pinpointed one of the dimensions that makes AI really important when it comes to troubleshooting. The humans just can't scale as fast as data, nor can they keep up with complexity of modern applications. And the third element that we feel is really important is the velocity with which people are now rolling out changes. People develop new features within hours, push them out to production. And in a world like that, the human has just no ability or time to understand what's normal, what's bad, to update their alert rules. And you need a machine, or an AI technology, to go help you with that. And that's basically what we're about. >> So this is where AI Ops comes in, right? Perfectly. Yeah. >> Yeah. You know, and John started to allude to it earlier, but having the insight on what's going on, we believe is only half of the equation, right? Once you understand what's going on, you naturally want to take action to remediate it or optimize it. And we believe automation should not be an exercise that's left to the reader. >> Yeah. >> As a lot of traditional platforms have done. Instead, we have a very robust, no-code, low-code automation built into our platform that allows you to take action in context with what you're seeing right then and there with the service. >> John: Yeah. Essentially monitoring, a term you use observability, some used as a fancy word today, is critical in all operating environments. So if we, if we kind of holistically, hey we're a distributed computing system, aka cloud, you got to track stuff at scale and you got to understand what it, what the impact is from a systems perspective. There's consequences to understanding what goes wrong. So as you look at that, what's the challenge for customers to do that? Because that seems to be the hard part as they lift and shift to the cloud, run their apps on the cloud, now they got to go take it to the next level, which is more developer velocity, faster productivity, and secure. >> Yeah. >> I mean, that seems to be the table stakes now. >> Yeah. >> How are companies forming around that? Are they there yet? Are they halfway there? Are they, where are they in the progression of, one, are they changing? And if so- >> Yeah that's a great question. I mean, I think whether it's an IT use case or a security use case, you can't manage what you don't know about. So visibility, discoverability, understanding what's going on, in a lot of ways that's the really hard problem to solve. And traditionally, we've approached that by like, harvesting data off of all these machines and devices in the infrastructure. But as we've seen with Zebrium and with related machine learning technologies, there's multiple ways of gaining insight as to what's going on. Once you have the insight be it an IT issue, like a service outage, or a security vulnerability, then you can take action. And the idea is you want to make that action as seamless as possible. But I think to answer your question, John, enterprises are still kind of getting their heads around how can we break down all the silos that have built up over the last decade or two, internally, and get visibility across the estate that really matters. And I think that's the real challenge. >> And I mean, and, at the velocity that applications are growing, just looking at our notes here, number of applications scaling from 64 million in 2017 to 147 million in 2021. That goes to what you were talking about, even with those other metrics earlier, 582 million by 2026 is what Morgan Stanley predicts. So, not only do we need to get out of silos we need to be able to see everything all the time, all at once, from the past legacy, as well as as we extend at scale. How are you thinking about that, Ajay? You're now with a big partner as an umbrella. What's next for you all? How, how are you going to help people solve problems faster? >> Yeah, so one of the attractions to the Zebrium team about Science Logic, aside from the team, and the culture, was the product portfolio was so complimentary. As Mike mentioned, you need visibility, you need mapping from low level building blocks to business services. And the end, at the end of the spectrum, once you know something's wrong you need to be able to take action automatically. And again, Science Logic has a very strong product, set of product capabilities and automated actions. What we bring to the table is the middle layer, which is from visibility, understanding what went wrong, figuring out the root cause. So to us, it was really exciting to be a very nice tuck in into this broader platform where we helped complete the story. >> Savannah: Yeah, that's, that's exciting. >> John: Should we do the Insta challenge? >> I was just getting ready to do that. You go for it John. You go ahead and kick it off. >> So we have this little tradition now, Instagram real, short and sweet. If you were going to see yourself on Instagram, what would be the Instagram reel of why this year's re:Invent is so important, and why people should pay attention to what's going on right now in the industry, or your company? >> Well, I think partly what Ajay was saying it's good to be back, right? So seeing just the energy and being back in 3D, you know en mass, is awesome again. It really is. >> Yeah. >> Mike: But, you know, I think this is where it's happening. We are at an inflection point of our industry and we're seeing a sea change in the way that applications and software delivered to businesses, to enterprises. And it's happening right here. This is the nexus of it. And so we're thrilled to be here as a part of all this, and excited about the future. >> All right, Ajay- >> Well done. He passes >> Your Instagram reel. >> Knowing what's happening in the broader economy, in the business context, it's, it feels even more important that companies like us are working on technologies that empower the same number of people to do more. Because it may not be realistic to just add on more headcount given what's going on in the world. But your deliverables and your roadmaps aren't slowing down. So, still the same amount of complexity, the same growth rates, but you're going to have to deal with all of that with fewer resources and be smarter about it. So, the approaches we're taking feel very much off the moment, you know, given what's going on in the real world. >> I love it. I love it. I've got, I've got kind of a finger to the wind, potentially hardball question for you here to close it out. But, given that you both have your finger really on the pulse right here, what percentage of current IT operations do you think will eventually be automated by AI and ML? Or AI ops? >> Well, I think a large percentage of traditional IT operations, and I'm talking about, you know, network operating center type of, you know, checking heartbeat monitors of compute storage and networking health. I think a lot of those things are going to be automated, right? Machine learning, just because of the scale. You can't scale, you can't hire enough NOC engineers to scale that kind of complexity. But I think IT talents, and what they're going to be focusing on is going shift, and they're going to be focusing on different parts. And I believe a lot of IT is going to be a much more of an enabler for the business, versus just managing things when they go wrong. So that's- >> All right. >> That's what I believe is part of the change. >> That's your, all right Ajay what about your hot take? >> Knowing how error-prone predictions are, (all laughing) I'll caveat my with- >> Savannah: We're allowing for human error here. >> I could be wildly wrong, but if I had to guess, you know, in 10 years you know, as much as 50% of the tasks will be automated. >> Mike: Oh, you- >> I love it. >> Mike: You threw a number out there. >> I love it. I love that he put his finger out- >> You got to see, you got to say the matrix. We're all going to be part of the matrix. >> Well, you know- >> And Star Trek- >> Skynet >> We can only turn back to this footage in a few years and quote you exactly when you have the, you know Mackenzie Research or the Morgan Stanley research that we've been mentioning here tonight and say that you've called it accurately. So I appreciate that. Ajay, it was wonderful to have you here. Congratulations on the acquisition. Thank you. Mike, thank you so much for being here on the Science Logic side, and congratulations to the team on 20 years. That's very exciting. John. Thank you. >> I try, I tried. Thank you. >> You try, you succeed. And thank you to all of our fabulous viewers out there at home. Be sure and tweet us at theCUBE. Say hello, Furrier, Sav is savvy. Let us know what you're thinking of AWS re:Invent where we are live from Las Vegas all week. You're watching theCUBE, the leader in high tech coverage. My name's Savannah Peterson, and we'll see you soon. (upbeat music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante at our inaugural super cloud 22 event we further refined the concept of a super cloud iterating on the definition the salient attributes and some examples of what is and what is not a super cloud welcome to this week's wikibon cube insights powered by etr you know snowflake has always been what we feel is one of the strongest examples of a super cloud and in this breaking analysis from our studios in palo alto we unpack our interview with benoit de javille co-founder and president of products at snowflake and we test our super cloud definition on the company's data cloud platform and we're really looking forward to your feedback first let's examine how we defl find super cloudant very importantly one of the goals of super cloud 22 was to get the community's input on the definition and iterate on previous work super cloud is an emerging computing architecture that comprises a set of services which are abstracted from the underlying primitives of hyperscale clouds we're talking about services such as compute storage networking security and other native tooling like machine learning and developer tools to create a global system that spans more than one cloud super cloud as shown on this slide has five essential properties x number of deployment models and y number of service models we're looking for community input on x and y and on the first point as well so please weigh in and contribute now we've identified these five essential elements of a super cloud let's talk about these first the super cloud has to run its services on more than one cloud leveraging the cloud native tools offered by each of the cloud providers the builder of the super cloud platform is responsible for optimizing the underlying primitives of each cloud and optimizing for the specific needs be it cost or performance or latency or governance data sharing security etc but those primitives must be abstracted such that a common experience is delivered across the clouds for both users and developers the super cloud has a metadata intelligence layer that can maximize efficiency for the specific purpose of the super cloud i.e the purpose that the super cloud is intended for and it does so in a federated model and it includes what we call a super pass this is a prerequisite that is a purpose-built component and enables ecosystem partners to customize and monetize incremental services while at the same time ensuring that the common experiences exist across clouds now in terms of deployment models we'd really like to get more feedback on this piece but here's where we are so far based on the feedback we got at super cloud 22. we see three deployment models the first is one where a control plane may run on one cloud but supports data plane interactions with more than one other cloud the second model instantiates the super cloud services on each individual cloud and within regions and can support interactions across more than one cloud with a unified interface connecting those instantiations those instances to create a common experience and the third model superimposes its services as a layer or in the case of snowflake they call it a mesh on top of the cloud on top of the cloud providers region or regions with a single global instantiation a single global instantiation of those services which spans multiple cloud providers this is our understanding from a comfort the conversation with benoit dejaville as to how snowflake approaches its solutions and for now we're going to park the service models we need to more time to flesh that out and we'll propose something shortly for you to comment on now we peppered benoit dejaville at super cloud 22 to test how the snowflake data cloud aligns to our concepts and our definition let me also say that snowflake doesn't use the term data cloud they really want to respect and they want to denigrate the importance of their hyperscale partners nor do we but we do think the hyperscalers today anyway are building or not building what we call super clouds but they are but but people who bar are building super clouds are building on top of hyperscale clouds that is a prerequisite so here are the questions that we tested with snowflake first question how does snowflake architect its data cloud and what is its deployment model listen to deja ville talk about how snowflake has architected a single system play the clip there are several ways to do this you know uh super cloud as as you name them the way we we we picked is is to create you know one single system and that's very important right the the the um [Music] there are several ways right you can instantiate you know your solution uh in every region of a cloud and and you know potentially that region could be a ws that region could be gcp so you are indeed a multi-cloud solution but snowflake we did it differently we are really creating cloud regions which are superposed on top of the cloud provider you know region infrastructure region so we are building our regions but but where where it's very different is that each region of snowflake is not one in instantiation of our service our service is global by nature we can move data from one region to the other when you land in snowflake you land into one region but but you can grow from there and you can you know exist in multiple clouds at the same time and that's very important right it's not one single i mean different instantiation of a system is one single instantiation which covers many cloud regions and many cloud providers snowflake chose the most advanced level of our three deployment models dodgeville talked about too presumably so it could maintain maximum control and ensure that common experience like the iphone model next we probed about the technical enablers of the data cloud listen to deja ville talk about snow grid he uses the term mesh and then this can get confusing with the jamaicani's data mesh concept but listen to benoit's explanation well as i said you know first we start by building you know snowflake regions we have today furry region that spawn you know the world so it's a worldwide worldwide system with many regions but all these regions are connected together they are you know meshed together with our technology we name it snow grid and that makes it hard because you know regions you know azure region can talk to a ws region or gcp regions and and as a as a user of our cloud you you don't see really these regional differences that you know regions are in different you know potentially clown when you use snowflake you can exist your your presence as an organization can be in several regions several clouds if you want geographic and and and both geographic and cloud provider so i can share data irrespective of the the cloud and i'm in the snowflake data cloud is that correct i can do that today exactly and and that's very critical right what we wanted is to remove data silos and and when you instantiate a system in one single region and that system is locked in that region you cannot communicate with other parts of the world you are locking the data in one region right and we didn't want to do that we wanted you know data to be distributed the way customer wants it to be distributed across the world and potentially sharing data at world scale now maybe there are many ways to skin the other cat meaning perhaps if a platform does instantiate in multiple places there are ways to share data but this is how snowflake chose to approach the problem next question how do you deal with latency in this big global system this is really important to us because while snowflake has some really smart people working as engineers and and the like we don't think they've solved for the speed of light problem the best people working on it as we often joke listen to benoit deja ville's comments on this topic so yes and no the the way we do it it's very expensive to do that because generally if you want to join you know data which is in which are in different regions and different cloud it's going to be very expensive because you need to move you know data every time you join it so the way we do it is that you replicate the subset of data that you want to access from one region from other regions so you can create this data mesh but data is replicated to make it very cheap and very performant too and is the snow grid does that have the metadata intelligence yes to actually can you describe that a little bit yeah snow grid is both uh a way to to exchange you know metadata about so each region of snowflake knows about all the other regions of snowflake every time we create a new region diary you know the metadata is distributed over our data cloud not only you know region knows all the regions but knows you know every organization that exists in our clouds where this organization is where data can be replicated by this organization and then of course it's it's also used as a way to uh uh exchange data right so you can exchange you know beta by scale of data size and we just had i was just receiving an email from one of our customers who moved more than four petabytes of data cross-region cross you know cloud providers in you know few days and you know it's a lot of data so it takes you know some time to move but they were able to do that online completely online and and switch over you know to the diff to the other region which is failover is very important also so yes and no probably means typically no he says yes and no probably means no so it sounds like snowflake is selectively pulling small amounts of data and replicating it where necessary but you also heard him talk about the metadata layer which is one of the essential aspects of super cloud okay next we dug into security it's one of the most important issues and we think one of the hardest parts related to deploying super cloud so we've talked about how the cloud has become the first line of defense for the cso but now with multi-cloud you have multiple first lines of defense and that means multiple shared responsibility models and multiple tool sets from different cloud providers and an expanded threat surface so listen to benoit's explanation here please play the clip this is a great question uh security has always been the most important aspect of snowflake since day one right this is the question that every customer of ours has you know how you can you guarantee the security of my data and so we secure data really tightly in region we have several layers of security it starts by by encrypting it every data at rest and that's very important a lot of customers are not doing that right you hear these attacks for example on on cloud you know where someone left you know their buckets uh uh open and then you know you can access the data because it's a non-encrypted uh so we are encrypting everything at rest we are encrypting everything in transit so a region is very secure now you know you never from one region you never access data from another region in snowflake that's why also we replicate data now the replication of that data across region or the metadata for that matter is is really highly secure so snow grits ensure that everything is encrypted everything is you know we have multiple you know encryption keys and it's you know stored in hardware you know secure modules so we we we built you know snow grids such that it's secure and it allows very secure movement of data so when we heard this explanation we immediately went to the lowest common denominator question meaning when you think about how aws for instance deals with data in motion or data and rest it might be different from how another cloud provider deals with it so how does aws uh uh uh differences for example in the aws maturity model for various you know cloud capabilities you know let's say they've got a faster nitro or graviton does it do do you have to how does snowflake deal with that do they have to slow everything else down like imagine a caravan cruising you know across the desert so you know every truck can keep up let's listen it's a great question i mean of course our software is abstracting you know all the cloud providers you know infrastructure so that when you run in one region let's say aws or azure it doesn't make any difference as far as the applications are concerned and and this abstraction of course is a lot of work i mean really really a lot of work because it needs to be secure it needs to be performance and you know every cloud and it has you know to expose apis which are uniform and and you know cloud providers even though they have potentially the same concept let's say blob storage apis are completely different the way you know these systems are secure it's completely different the errors that you can get and and the retry you know mechanism is very different from one cloud to the other performance is also different we discovered that when we were starting to port our software and and and you know we had to completely rethink how to leverage blob storage in that cloud versus that cloud because just of performance too so we had you know for example to you know stripe data so all this work is work that's you know you don't need as an application because our vision really is that applications which are running in our data cloud can you know be abstracted of all this difference and and we provide all the services all the workload that this application need whether it's transactional access to data analytical access to data you know managing you know logs managing you know metrics all of these is abstracted too such that they are not you know tied to one you know particular service of one cloud and and distributing this application across you know many regions many cloud is very seamless so from that answer we know that snowflake takes care of everything but we really don't understand the performance implications in you know in that specific case but we feel pretty certain that the promises that snowflake makes around governance and security within their data sharing construct construct will be kept now another criterion that we've proposed for super cloud is a super pass layer to create a common developer experience and an enabler for ecosystem partners to monetize please play the clip let's listen we build it you know a custom build because because as you said you know what exists in one cloud might not exist in another cloud provider right so so we have to build you know on this all these this components that modern application mode and that application need and and and and that you know goes to machine learning as i say transactional uh analytical system and the entire thing so such that they can run in isolation basically and the objective is the developer experience will be identical across those clouds yes right the developers doesn't need to worry about cloud provider and actually our system we have we didn't talk about it but the marketplace that we have which allows actually to deliver we're getting there yeah okay now we're not going to go deep into ecosystem today we've talked about snowflakes strengths in this regard but snowflake they pretty much ticked all the boxes on our super cloud attributes and definition we asked benoit dejaville to confirm that this is all shipping and available today and he also gave us a glimpse of the future play the clip and we are still developing it you know the transactional you know unistore as we call it was announced in last summit so so they are still you know working properly but but but that's the vision right and and and that's important because we talk about the infrastructure right you mentioned a lot about storage and compute but it's not only that right when you think about application they need to use the transactional database they need to use an analytical system they need to use you know machine learning so you need to provide also all these services which are consistent across all the cloud providers so you can hear deja ville talking about expanding beyond taking advantage of the core infrastructure storage and networking et cetera and bringing intelligence to the data through machine learning and ai so of course there's more to come and there better be at this company's valuation despite the recent sharp pullback in a tightening fed environment okay so i know it's cliche but everyone's comparing snowflakes and data bricks databricks has been pretty vocal about its open source posture compared to snowflakes and it just so happens that we had aligotsy on at super cloud 22 as well he wasn't in studio he had to do remote because i guess he's presenting at an investor conference this week so we had to bring him in remotely now i didn't get to do this interview john furrier did but i listened to it and captured this clip about how data bricks sees super cloud and the importance of open source take a listen to goatzee yeah i mean let me start by saying we just we're big fans of open source we think that open source is a force in software that's going to continue for you know decades hundreds of years and it's going to slowly replace all proprietary code in its way we saw that you know it could do that with the most advanced technology windows you know proprietary operating system very complicated got replaced with linux so open source can pretty much do anything and what we're seeing with the data lake house is that slowly the open source community is building a replacement for the proprietary data warehouse you know data lake machine learning real-time stack in open source and we're excited to be part of it for us delta lake is a very important project that really helps you standardize how you lay out your data in the cloud and with it comes a really important protocol called delta sharing that enables you in an open way actually for the first time ever share large data sets between organizations but it uses an open protocol so the great thing about that is you don't need to be a database customer you don't even like databricks you just need to use this open source project and you can now securely share data sets between organizations across clouds and it actually does so really efficiently just one copy of the data so you don't have to copy it if you're within the same cloud so the implication of ellie gotzi's comments is that databricks with delta sharing as john implied is playing a long game now i don't know if enough about the databricks architecture to comment in detail i got to do more research there so i reached out to my two analyst friends tony bear and sanji mohan to see what they thought because they cover these companies pretty closely here's what tony bear said quote i've viewed the divergent lake house strategies of data bricks and snowflake in the context of their roots prior to delta lake databrick's prime focus was the compute not the storage layer and more specifically they were a compute engine not a database snowflake approached from the opposite end of the pool as they originally fit the mold of the classic database company rather than a specific compute engine per se the lake house pushes both companies outside of their original comfort zones data bricks to storage snowflake to compute engine so it makes perfect sense for databricks to embrace the open source narrative at the storage layer and for snowflake to continue its walled garden approach but in the long run their strategies are already overlapping databricks is not a 100 open source company its practitioner experience has always been proprietary and now so is its sql query engine likewise snowflake has had to open up with the support of iceberg for open data lake format the question really becomes how serious snowflake will be in making iceberg a first-class citizen in its environment that is not necessarily officially branding a lake house but effectively is and likewise can databricks deliver the service levels associated with walled gardens through a more brute force approach that relies heavily on the query engine at the end of the day those are the key requirements that will matter to data bricks and snowflake customers end quote that was some deep thought by by tony thank you for that sanjay mohan added the following quote open source is a slippery slope people buy mobile phones based on open source android but it's not fully open similarly databricks delta lake was not originally fully open source and even today its photon execution engine is not we are always going to live in a hybrid world snowflake and databricks will support whatever model works best for them and their customers the big question is do customers care as deeply about which vendor has a higher degree of openness as we technology people do i believe customers evaluation criteria is far more nuanced than just to decipher each vendor's open source claims end quote okay so i had to ask dodgeville about their so-called wall garden approach and what their strategy is with apache iceberg here's what he said iceberg is is very important so just to to give some context iceberg is an open you know table format right which was you know first you know developed by netflix and netflix you know put it open source in the apache community so we embrace that's that open source standard because because it's widely used by by many um many you know companies and also many companies have you know really invested a lot of effort in building you know big data hadoop solution or data like solution and they want to use snowflake and they couldn't really use snowflake because all their data were in open you know formats so we are embracing icebergs to help these companies move through the cloud but why we have been relentless with direct access to data direct access to data is a little bit of a problem for us and and the reason is when you direct access to data now you have direct access to storage now you have to understand for example the specificity of one cloud versus the other so as soon as you start to have direct access to data you lose your you know your cloud diagnostic layer you don't access data with api when you have direct access to data it's very hard to secure data because you need to grant access direct access to tools which are not you know protected and you see a lot of you know hacking of of data you know because of that so so that was not you know direct access to data is not serving well our customers and that's why we have been relented to do that because it's it's cr it's it's not cloud diagnostic it's it's you you have to code that you have to you you you need a lot of intelligence while apis access so we want open apis that's that's i guess the way we embrace you know openness is is by open api versus you know you access directly data here's my take snowflake is hedging its bets because enough people care about open source that they have to have some open data format options and it's good optics and you heard benoit deja ville talk about the risks of directly accessing the data and the complexities it brings now is that maybe a little fud against databricks maybe but same can be said for ollie's comments maybe flooding the proprietaryness of snowflake but as both analysts pointed out open is a spectrum hey i remember unix used to equal open systems okay let's end with some etr spending data and why not compare snowflake and data bricks spending profiles this is an xy graph with net score or spending momentum on the y-axis and pervasiveness or overlap in the data set on the x-axis this is data from the january survey when snowflake was holding above 80 percent net score off the charts databricks was also very strong in the upper 60s now let's fast forward to this next chart and show you the july etr survey data and you can see snowflake has come back down to earth now remember anything above 40 net score is highly elevated so both companies are doing well but snowflake is well off its highs and data bricks has come down somewhat as well databricks is inching to the right snowflake rocketed to the right post its ipo and as we know databricks wasn't able to get to ipo during the covet bubble ali gotzi is at the morgan stanley ceo conference this week they got plenty of cash to withstand a long-term recession i'm told and they've started the message that they're a billion dollars in annualized revenue i'm not sure exactly what that means i've seen some numbers on their gross margins i'm not sure what that means i've seen some numbers on their net retention revenue or net revenue retention again i'll reserve judgment until we see an s1 but it's clear both of these companies have momentum and they're out competing in the market well as always be the ultimate arbiter different philosophies perhaps is it like democrats and republicans well it could be but they're both going after a solving data problem both companies are trying to help customers get more value out of their data and both companies are highly valued so they have to perform for their investors to paraphrase ralph nader the similarities may be greater than the differences okay that's it for today thanks to the team from palo alto for this awesome super cloud studio build alex myerson and ken shiffman are on production in the palo alto studios today kristin martin and sheryl knight get the word out to our community rob hoff is our editor-in-chief over at siliconangle thanks to all please check out etr.ai for all the survey data remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcasts i publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante at siliconangle.com or dm me at devellante or comment on my linkedin posts and please as i say etr has got some of the best survey data in the business we track it every quarter and really excited to be partners with them this is dave vellante for the cube insights powered by etr thanks for watching and we'll see you next time on breaking analysis [Music] you

(upbeat music) >> Welcome back to Supercloud22. This is an open community event, and it's dedicated to tracking the future of cloud in the 2020s. Supercloud is a term that we use to describe an architectural abstraction layer that hides the underlying complexities of the individual cloud primitives and APIs and creates a common experience for developers and users irrespective of where data is physically stored or on which cloud platform it lives. We're now going to explore the nuances of going to market in a world where data architectures span on premises across multiple clouds and are increasingly stretching out to the edge. Paula Hansen is the President and Chief Revenue Officer at Alteryx. And the reason we asked her to join us for Supercloud22 is because first of all, Alteryx is a company that is building a form of Supercloud in our view. If you have data in a bunch of different places and you need to pull in different data sets together, you might want to filter it or blend it, cleanse it, shape it, enrich it with other data, analyze it, report it out to your colleagues. Alteryx allows you to do that and automate that life cycle. And in our view is working to break down the data silos across clouds, hence Supercloud. Now, the other reason we invited Paula to the program is because she's a rockstar female in tech, and since day one at theCube, we've celebrated great women in tech, and in this case, a woman of data, Paula Hansen, welcome to the program. >> Thank you, Dave. I am absolutely thrilled to be here. >> Okay, we're going to focus on customers, their challenges and going to market in this cross cloud, multi-cloud, Supercloud world. First, Paula, what's changing in your view in the way that customers are innovating with data in the 2020s? >> Well, I think we've all learned very clearly over these last two years that the global pandemic has altered life and business as we know it. And now we're in an interesting time from a macroeconomic perspective as well. And so what we've seen is that every company in every industry has had to pivot and think about how they meet redefined customer expectations and an ever evolving competitive landscape. There really isn't an industry that wasn't reshaped in some way over the last couple of years. And we've been fortunate to work with companies in all industries that have adapted to this ever changing environment by leveraging Alteryx to help accelerate their digital transformations. Companies know that they need to unlock the full potential of their data to be able to move quickly to pivot and to respond to their customer's needs, as well as manage their businesses most efficiently. So I think nothing tells that story better than sharing a customer example with you, Dave. We love to share stories of our very innovative customers. And so the one that I'll share with you today in regards to this is Delta Airlines, who we're all very familiar with. And of course Delta's goal is to always keep their airplanes in the air flying passengers and getting people to their destinations efficiently. So they focus on the maintenance of their aircraft as a necessary part of running their business and they need to manage their maintenance stops and the maintenance of their aircraft very efficiently and effectively. So we work with them. They leverage our platform to automate all the processes for their aircraft maintenance centers. And so they've built out a fully automated reporting system on our platform leveraging tons of data. And this gives their service managers and their aircraft technicians foresight into what's happening with their scheduling and their maintenance processes. So this ensures that they've got the right technicians in the service center when the aircrafts land and that everything across that process is fully in place. And previously because of data silos and just complexity of data, this process would've taken them many many hours in each independent service center, and now leveraging Alteryx and the power of analytics and bringing all the data together. Those centers can do this process in just minutes and get their planes back in the air efficiently and delivering on their promises to their customers. So that's just one of many examples that we have in terms of the way the Alteryx analytics automation helps customers in this new age and helping to really unlock the power of their data. >> You know, Paul, that's an interesting example. Because in a previous life I worked with some airlines and people maybe don't realize this but, aircraft maintenance is the mission critical application for carriers. It's not the booking system. Because we've been there before, we show you there's a problem when you're booking or sometimes it's unfortunate, but people they get de booked. But the aircraft maintenance is the one that matters the most and that keeps planes in the air. So we hear all the time, you just mention it. About data silos and how problematic they are. So, specifically how are you seeing customers thinking about busting the data silos? >> Yeah, that's right, it's a big topic right now. Because companies realize that business processes that they run their business with, is very cross-functional in nature and requires data across every department in the enterprise. And you can't keep data locked in one department. So if you think of business processes like pay to procure or quote to cash, these are business processes that companies in every industry run their business. And that requires them to get data from multiple departments and bring all of that data together seamlessly to make the best business decisions that they can make. So what our platform does is, and is really well known for, is being very easy for users number one, and then number two, being really great at getting access to data quickly and easily from all those data silos, really, regardless of where it is. We talk about being everywhere. And when we say that we mean, whether it's on-prem, in your legacy applications and databases, or whether it's in the cloud with of course, all the multiple cloud platforms and modern cloud data warehouses. Regardless of where it is, we have the ability to bring that data together across hundreds of different data sources, bring it together to help drive insights and ultimately help our customers make better decisions, take action, and deliver on the business outcomes that they all are trying to drive within their respective industries. And what's- >> You know- >> Go ahead. >> Please carry on. >> Well, I was just going to say that what I do think has really sort of a tipping point in the last six months in particular is that executives themselves are really demanding of their organizations, this democratization of data. And the breaking down of the silos and empowering all of the employees across their enterprise regardless of how sophisticated they are with analytics to participate in the analytic opportunity. So we've seen some really cool things of late where executives, CEOs, chief financial officers, chief data officers are sponsoring events within their organizations to break down these silos and encourage their employees to come together on this democratization opportunity of democratization of data and analytics. And there's a shortage of data scientists on top of this. So there's no way that you're going to be able to hire enough data scientists to make sense of all this data running around your enterprise. So we believe with our platform we empower people regardless of their skillset. And so we see executives sponsoring these hackathons within their environments to bring together people to brainstorm and ideate on use cases, to share examples of how they leverage our platform and leverage the data within their organization to make better decisions. And it's really quite cool. Companies like Stanley Black & Decker, Ingersoll Rand, Inchcape PLC, these are all companies that the executive team has sponsored these hackathon events and seen really powerful things come out of it. As an example Ingersoll Rand sponsored their Alteryx hackathon with all of their data workers across various different functions where the data exists. And they focused on both top line revenue use cases as well as bottom line efficiency cases. And one of the outcomes was a use case that helped with their distribution center in north America and bringing all the data together across their various applications to reduce the amount of over ordering and under ordering of parts and more effectively manage their inventory within that distribution center. So, really cool to see this is now an executive level board level conversation. >> Very cool, a hackathon bringing people together for collaboration. A couple things that you said I want to comment on. Again, one of the reasons why we invited you guys to come on is, when you think about on-prem data and anybody who follows theCube and my breaking analysis program, knows we're big fans of Zhamak Dehghani's concept of data mesh. And data mesh is supposed to be inclusive. It doesn't matter if it's an S3 bucket, Oracle data base, or data warehouse, or data lake, that's just a note on the data mesh. And so it should be inclusive and Supercloud should include on-prem data to the extent that you can make that experience consistent. We have a lot of technical sessions here at Supercloud22, we're focusing now and go to market and the ecosystem. And we live in a world of multiple partners exploding ecosystems. And a lot of times it's co-opetition. So Paula, when you joined Alteryx you brought a proven go to market discipline to the company. Alignment with the customer, playbooks, best practice of sales, et cetera. And we've seen the results. It's a big reason why Mark Anderson and the board promoted you to president just after 10 months. Summarize how you approached the situation at Alteryx when you joined last spring. >> Yeah, I think first we were really intentional about what part of the market, what type of enterprises get the most benefit from the innovation that we deliver? And it's really clear that it's large enterprises. That the more complex a company is, most likely the more data they have and oftentimes the more decentralized that data is. And they're also really all trying to figure out how to remain competitive by leveraging that data. So, the first thing we did was be very intentional that we're focused on the enterprise and building out all of the capability required to be able to serve the enterprise. Of course, essential to all of that is having a platform capability because enterprises require that. So, with Suresh Vittal our Chief Product Officer, he's been fantastic in building out an end to end analytic platform that serves a wide range of analytic capabilities to a wide range of users. And then of course has this flexibility to operate both on-prem and in the cloud which is very important. Because we see this hybrid environment in this multicloud environment being something that is important to our customers. The second thing that I was really focused on was understanding how do you have those conversations with customers when they all are in maybe different types of backgrounds? So the way that you work with a business analyst in the office of finance or supply chain or sales and marketing, is different than the way that you serve a data scientist or a data engineer in IT. The way that you talk to a business owner who wants not to really understand the workflow level of data but wants to understand the insights of data, that's a different conversation. When you want to have a conversation of analytics for all or democratization of analytics at the executive level with the chief data officer or a CIO, that's a whole different conversation. And so we've built very specific sales plays to be able to have those conversations bring the relevant information to the relevant person so that we're really making sure that we explain the value proposition of the platform. Fully understand their world, their language and can work with them to deliver the value to them. And then the third thing that we did, was really heavily invest in our partnerships and you referenced this day. It's a a broad ecosystem out there. And we know that we have to integrate into that broad data ecosystem. and be a good partner to serve our customers. So, we've invested both in technology integration as well as go to market strategies with cloud data warehouse companies like Snowflake and Databricks, or RPA companies like UiPath and Blue Prism, as well as a wide range of other application and all of the cloud platforms because that's what our customers expect from us. So that's been a really important sort of third pillar of our strategy in making sure that from a go to market perspective, we understand where we fit in the ecosystem and how we collectively deliver on value to our joint customers. >> So that's super helpful. What I'm taking away from this is you didn't come to it with a generic playbook. Frank Lyman always talks about situation leadership. You assess the situation and applied that and a great example of partners is Snowflake and Databricks, these sort of opposites, but trying to solve similar problems. So you've got to be inclusive of all that. So we're trying to sort of squint through this Paula and say, okay, are there nuances and best practices beyond some of the the things that you just described that are unique to what we call Supercloud? Are there observations you can make with respect to what's different in this post isolation economy? Specifically in managing remote employees and of course remote partners, working with these complex ecosystems and the rise of this multi-cloud world, is it different or is it same wine new bottle? >> Well, I think it's both common from the on-prem or pre-cloud world, but there's also some differences as well. So what's common is that companies still expect innovation from us and still want us to be able to serve a wide range of skill sets. So our belief is that regardless of the skill set that you have, you can participate in the analytics opportunity for your company and unlocking the potential of your data. So we've been very focused since our inception to build out a platform that really serves this wide range of capabilities across the enterprise space. What's perhaps changed more or continues to evolve in this cloud world is just the flexibility that's required. You have to be everywhere. You have to be able to serve users wherever they are and be able to live in a multi-cloud or super cloud world. So when I think of cloud, I think it just unlocks a whole bigger opportunity for Alteryx and for companies that want to become analytic leaders. Because now you have users all over the globe, many of them looking for web-based analytic solutions. And of course these enterprises are all in various places on their journey to cloud and they want a partner and a platform that operates in all of those environments, which is what we do at Alteryx. So, I think it's an exciting time. I think that it's still very early in the analytic market and what companies are going to do to leverage their data to drive their transformation. And we're really excited to be a part of it. >> So last question is, I said up front we always like to celebrate women in tech. How'd you get into tech.? You've got a background, you've got somewhat of a technical background of being technical sales. And then of course rose up throughout your career and now have a leadership position. I called you a woman of data. How'd you get into it? Where'd you find the love of data? Give us the background and help us inspire some of the young women out there. >> Oh, well, but I'm super passionate about inspiring young women and thinking about the future next generation of women that can participate in technology and in data specifically. I grew up loving math and science. I went to school and got an electrical engineering degree but my passion around technology hasn't been just around technology for technology's sake, my passion around technology is what can it enable? What can it do? What are the outcomes that technology makes possible? And that's why data is so attractive because data makes amazing things possible. I shared some of those examples with you earlier but it not only can we have effect with data in businesses and enterprise, but governments globally now are realizing the ability for data to really have broad societal impact. And so I think that that speaks to women many times. Is that what does technology enable? What are the outcomes? What are the stories and examples that we can all share and be inspired by and feel good and and inspired to be a part of a broader opportunity that technology and data specifically enables? So that's what drives me. And those are the conversations that I have with the women that I speak with in all ages all the way down to K through 12 to inspire them to have a career in technology. >> Awesome, the more people in STEM the better, and the more women in our industry the better. Paula Hansen, thanks so much for coming in the program. Appreciate it. >> Thank you, Dave. >> Okay, keep it right there for more coverage from Supercloud 22, you're watching theCube. (upbeat music)

(upbeat music) >> Okay, welcome back everyone, Cube coverage live on the floor in the Moscone center in San Francisco, California. I'm John Furrier host of the Cube. AWS summit 2022 is here in San Francisco, we're back in live events. Of course, Amazon summit in New York city is coming, Amazon summit this summer we'll be there as well. We've got a great guest Mike Miller, GN of AI devices at AWS always one of my favorite interviews. We've got a little prop here, we got the car, DeepRacer, very popular at the events. Mike, welcome to the Cube. Good to see you. >> Hey John, thank you for having me. It's really exciting to be back and chat with you a little bit about DeepRacer. >> Well I want to get into the prop in a second, not the prop, the product. >> Yeah. >> So DeepRacer program, you got the race track here. Just explain what it is real quick, we'll get that out of the way. >> Absolutely so, well, you know that AI, AWS is passionate about making AI and ML more accessible to developers of all skill levels. So DeepRacer is one of our tools to do that. So DeepRacer is a 3D cloud-based racing simulator, a 1/18th scale autonomously driven car and a league to add a little spicy competition into it. So developers can start with the cloud-based simulator where they're introduced to reinforcement learning which basically teaches the, our car to drive around a track through trial and error and of course you're in a virtual simulator so it's easy for it to make mistakes and restart. Then once that model is trained, it's downloaded to the car which then can drive around a track autonomously, kind of making its own way and of course we track lap time and your successful lap completions and all of that data feeds into our league to try to top the leaderboard and win prizes. >> This is the ultimate gamification tool. (chuckles) >> Absolutely >> Making it fun to learn about machine learning. All right, let's get into the car, let's get into the showcase of the car. show everyone what's going on. >> Absolutely. So this is our 1/18th scale autonomously driven car. It's built off of a monster truck chassis so you can see it's got four wheel drive, it's got steering in the front, we've got a camera on the front. So the camera is the, does the sensing to the compute board that's driven by an Intel atom a processor on the, on the vehicle, that allows it to make sense of the in front of it and then decide where it wants to drive. So you take the car, you download your trained model to it and then it races around the track. >> So the front is the camera. >> The front is the camera, that's correct. >> Okay, So... >> So it's a little bit awkward but we needed to give it plenty of room here so that I can actually see the track in front of it. >> John: It needs eyes. >> Yep. That's exactly right. >> Awesome. >> Yes. >> And so I got to buy that if I'm a developer. >> So, developers can start in two ways, they can use our virtual racing experience and so there's no hardware cost for that, but once you want the experience, the hands on racing, then the car is needed but if you come to one of our AWS summits, like here in San Francisco or anywhere else around the world we have one or more tracks set up and you can get hands on, you can bring the model that you trained at home download it to a car and see it race around the track. >> So use a car here. You guys are not renting cars, but you're letting people use the cars. >> Absolutely. >> Can I build my own car or does it have to be assembled by AWS? >> Yeah, we, we sell it as a, as a kit that's already assembled because we've got the specific compute board in there, that Intel processor and all of the software that's already built on there that knows how to drive around the track. >> That's awesome, so talk about the results. What's going on? What's the feedback from developers? Obviously it's a nerd dream, people like race cars, people love formula one now, all the racing there. IOT is always an IOT opportunity as well. >> Absolutely, and as you said, gamification, right? And so what we found and what we thought we would find was that adding in those sort of ease of learning so we make it the on-ramp to machine learning very easy. So developers of all skill levels can take advantage of this, but we also make it fun by kind of gamifying it. We have different challenges every month, we have a leader board so you can see how you rank against your peers and actually we have split our league into two, there's an open division which is more designed for novices so you'll get rewarded for just participating and then we have a pro league. So if you're one of the top performers in the open league each month, you graduate and you get to race against the big boys in the pro leagues. >> What's the purse? >> Oh, the, (John laughing) we definitely have cash and prizes that happen, both every month. We have prizes cause we do races every month and those winners of those races all get qualified to race at the championship, which of course happens in Las Vegas at re:Invent. So we bring all the winners to re:Invent and they all race against each other for the grand prize the big trophy and the, and the, and the cash prize. >> Well, you know, I'm a big fan of what you guys are doing so I'm kind of obviously biased on this whole program but you got to look at trend of what's going on in eSports and the online engagement is off the charts, are there plans to kind of make this more official and bigger? Is there traction there or is this just all part of the Amazon goodness, love that you guys give back? I mean, obviously it's got traction. >> Yeah. I mean, the thing that's interesting about eSports is the number of young people who are getting into it and what we saw over the last couple years is that, there were a lot of students who were adopting DeepRacer but there were some hurdles, you know, it wasn't really designed for them. So what we did was we made some changes and at the beginning of this year we launched a student focused DeepRacer program. So they get both free training every month, they get free educational materials and their own private league so they know students can race against other students, as part of that league. >> John: Yeah. >> So that was really our first step in kind of thinking about those users and what do we need to do to cater to their kind of unique needs? >> Tell about some of the power dynamics or the, or not power dynamics, the group dynamics around teams and individuals, can I play as an individual? Do I, do I have to be on a team? Can I do teams? How does that look? How do you think about those things? >> Yeah, absolutely. Great, great question. The primary way to compete is individually. Now we do have an offering that allows companies to use DeepRacer to excite and engage their own employees and this is where operating as a team and collaborating with your coworkers comes into play so, if, if I may there's, you know, Accenture and JPMC are a couple big customers of ours, really strong partners. >> John: Yeah. >> Who've been able to take advantage of DeepRacer to educate their workforce. So Accenture ran a 24 hour round the, round the globe race a couple years ago, encouraging their employees to collaborate and form teams to race and then this past year JPMC, had over 3000 of their builders participate over a three month period where they ran a private league and they went on to win the top two spots, first place and second place. >> John: Yeah. >> At reinvent last year. >> It reminds me the NASCAR and all these like competitions, the owners have multiple cars on the race. Do you guys at re:Invent have to start cutting people like, only two submissions or is it free for all? >> Well, you have to qualify to get to the races at re:invent so it's very, it's very cutthroat leading up to that point. We've got winners of our monthly virtual contests, the winners like of the summit races will also get invited. So it's interesting, this dynamic, you'll have some people who won virtual races, some people who won physical races, all competing together. >> And do you guys have a name for the final cup or is it like what's the, what's the final, how do you guys talk about the prizes and the... >> It's, it's the DeepRacer Championship Cup of course. >> John: Of course. (laughter) >> Big silver cup, you get to hoist it and... >> Are the names inscribed in it, is it like the Stanley cup or is it just one. >> It's a unique one, so you get to hold onto it each year. The champion gets their own version of the cup. >> It's a lot of fun. I think it's really kind of cool. What's the benefits for a student? Talk about the student ones. >> Yeah. Yeah. >> So I'm a student I'm learning machine learning, what's in it for me is a career path and the fund's obvious, I see that. >> Yeah absolutely. You know, the, for students, it's a hands on way that's a very easy on-ramp to machine learning and you know, one of the things, as I mentioned we're passionate about making it accessible to all. Well, when we mean all we were really do mean all. So, we've got a couple partners who are passionate about the same thing, right? Which is how do we, if, if AI and ML is going to transform our world and solve our most challenging problems, how can we get the right minds from all walks of life and all backgrounds to learn machine learning and get engaged? So with two of our partners, so with Udacity and with Intel we launched a $10 million AWS, AI and ML scholarship program and we built it around DeepRacer. So not only can students who are college and high school students, age 16 and over can use DeepRacer, can learn about machine learning and then get qualified to win one of several thousand scholarships. >> Any other promotions going on that people should know about? >> Yeah, one, one final one is, so we talked about enterprises like JPMC and Accenture, so we've got a promotion that we just started yesterday. So if you are an enterprise and you want to host a DeepRacer event at your company to excite your employees and get 'em collaborating more, if you have over 50 employees participating, we're going to give you up to a hundred thousand dollars in AWS credits, to offset the costs of running your DeepRacer event at your, at your company so >> That's real money. >> Yeah. Real, real, real exciting I think for companies now to pick up DeepRacer. >> So, I mean, honestly, I know Andy Jassy, I have many sports car conversations with him. He's a sports guy, he's now the CEO of Amazon, gets to go all the sporting events, NFL. I wish I could bring the Cube there but, we'll stick with with cloud for now. You got to look at the purse kind of thing. I'm interested in like the whole economic point of cause I mean, forget the learning for side for a second which is by the way awesome. This is great competition. You got leader boards, you got regional activities, you got a funneling system laddering up to the final output. >> And we've really done a decent job and, and of adding capabilities into that user experience to make it more engaging. You can see the countries that the different competitors are from, you can see how the lap times change over time, you know, we give awards as I mentioned, the two divisions now. So if you're not super competitive, we'll reward you for just participating in that open league but if you want to get competitive, we'll even better rewards monthly in the Pro League. >> Do you guys have any conversations internally like, this is getting too big, we might have to outsource it or you keep it in inside the fold? (laughter) >> We, we love DeepRacer and it's so much fun running this, >> You see where I'm going with this. You see where I'm going with this right? The Cube might want to take this over. >> Hey. >> And you know >> We're always looking for partners and sponsors who can help us make it bigger so, absolutely. >> It's a good business opportunity. I just love it. Congratulations, great stuff. What's the big learning in this, you know, as a as an executive, you look back you got GM, AI super important and, and I think it is great community, communal activity as well. What's the learning, what have you learned from this over the years besides that it's working but like what's the big takeaway? >> Yeah, I mean. We've got such a wide range of developers and builders who are customers that we need to provide a variety of opportunities for people to get hands on and there's no better way to learn a complex technology like AI and ML than getting hands on and seeing, you know, physically the result of the AI and I think that's been the biggest learning, is that just having the hands on and the sort of element of watching what it does, just light bulbs go off. When, when developers look at this and they start piecing the, the puzzle pieces together, how they can benefit. >> So I have to ask the question that might be on other peoples minds, maybe it's not, maybe I'm just thinking really dark here but gamers love to hack and they love cheat codes, they love to get, you know, get into the system, any attempts to do a little hacking to win the, the the game, have you guys, is there, you know? >> Well, well, you know, last year we, we we released an open source version of the vehicle so that people could start using it as a platform to explore and do that kind of hacking and give them an opportunity build on top of it. >> So using mods, mods modules, we can mod out on this thing. >> Yeah, absolutely. If you go to deepracer.com, we have sort of extensions page there, and you can see, somebody mounted a Nerf cannon onto the top of this, somebody built a computer vision model that could recognize you know, rodents and this thing would kind of drive to scare 'em, all kinds of fun topics. >> So it's a feature, not a bug. >> Absolutely. >> Open it up. >> Yeah. >> And also on transparency, if you have the source code out there you guys can have some review. >> Yeah. The whole idea is like, let's see what developers, >> It's really not hackable. It's not hackable. >> Yeah, I mean, for the, if you think about it when we do the races, we bring the cars ourselves, the only way a developer interacts is by giving us their trained models so... >> And you, do you guys review the models? Nothing to review, right? >> Yeah. There's nothing really to review. It's all about, you know, there, there was a model that we saw one time where the car went backwards and then went forwards across the finish line but we, we, we gently told them, well that's really not a valid way to race. >> That was kind of a hack, not really a hack. That was a hack hack. (laughter) That was just a growth hack. >> Exactly, but everybody just has a lot of fun with it across the board. >> Mike, great, thanks for coming on. Love the prop. Thanks for bringing the car on, looks great. Success every year. I want to see the purse, you know, big up to $1,000,000 you know, the masters, you know, tournament. >> Someday. (John chuckles) >> You guys.. >> Thank you for having me John. >> DeepRacer again, Fun Start has a great way to train people on machine learning, IOT device, turns into a league of its own. Great stuff for people to learn, especially students and people in companies, but the competitive juices flowing. That's what it's all about, having fun, learning. It's the Cube here in San Francisco. Stay with us for more coverage after this short break. (gentle music)

(upbeat music playing) >> Welcome to theCUBE's coverage of Couchbase ConnectONLINE Mary Roth, VP of Engineering Operations with Couchbase is here for Couchbase ConnectONLINE. Mary. Great to see you. Thanks for coming on remotely for this segment. >> Thank you very much. It's great to be here. >> Love the fire in the background, a little fireside chat here, kind of happening, but I want to get into it because, Engineering and Operations with the pandemic has really kind of shown that, engineers and developers have been good, working remotely for a while, but for the most part it's impacted companies in general, across the organizations. How did the Couchbase engineering team adapt to the remote work? >> Great question. And I actually think the Couchbase team responded very well to this new model of working imposed by the pandemic. And I have a unique perspective on the Couchbase journey. I joined in February, 2020 after 20 plus years at IBM, which had embraced a hybrid, in-office remote work model many years earlier. So in my IBM career, I live four minutes away from my research lab in Almaden Valley, but IBM is a global company with headquarters on the East Coast, and so throughout my career, I often found myself on phone calls with people around the globe at 5:00 AM in the morning, I quickly learned and quickly adapted to a hybrid model. I'd go into the office to collaborate and have in-person meetings when needed. But if I was on the phone at 5:00 AM in the morning, I didn't feel the need to get up at 4:30 AM to go in. I just worked from home and I discovered I could be more productive there, doing think time work, and I really only needed the in-person time for collaboration. This hybrid model allowed me to have a great career at IBM and raise my two daughters at the same time. So when I joined Couchbase, I joined a company that was all about being in-person and instead of a four minute commute, it was going to be an hour or more commute for me each way. This was going to be a really big transition for me, but I was excited enough by Couchbase and what it offered, that I decided to give it a try. Well, that was February, 2020. I showed up early in the morning on March 10th, 2020 for an early morning meeting in-person only to learn that I was one of the only few people that didn't get the memo. We were switching to a remote working model. And so over the last year, I have had the ability to watch Couchbase and other companies pivot to make this remote working model possible and not only possible, but effective. And I'm really happy to see the results. A remote work model does have its challenges, that's for sure, but it also has its benefits, better work-life balance and more time to interact with family members during the day and more quiet time just to think. We just did a retrospective on a major product release, Couchbase server 7.0, that we did over the past 18 months. And one of the major insights by the leadership team is that working from home actually made people more effective. I don't think a full remote model is the right approach going forward, but a hybrid model that IBM adopted many years ago and that I was able to participate in for most of my career, I believe is a healthier and more productive approach. >> Well, great story. I love the come back and now you take leverage of all the best practices from the IBM days, but how did they, your team and the Couchbase engineering team react? And were there any best practices or key learnings that you guys pulled out of that? >> The initial reaction was not good. I mean, as I mentioned, it was a culture based on in-person, people had to be in in-person meetings. So it took a while to get used to it, but there was a forcing function, right? We had to work remotely. That was the only option. And so people made it work. I think the advancement of virtual meeting technology really helps a lot. Over earlier days in my career where I had just bad phone connections, that was very difficult. But with the virtual meetings that you have, where you can actually see people and interact, I think is really quite helpful. And probably the key. >> What's the DNA of the company there? I mean, every company's got the DNA, Intel's Moore's Law, and what's the engineering culture at Couchbase like, if you could describe it. >> The engineering culture at Couchbase is very familiar to me. We are at our heart, a database company, and I grew up in the database world, which has a very unique culture based on two values, merit and mentorship. And we also focus on something that I like to call growing the next generation. Now database technology started in the late sixties, early seventies, with a few key players and institutions. These key players were extremely bright and they tackled and solved really hard problems with elegant solutions, long before anybody knew they were going to be necessary. Now, those original key players, people like Jim Gray, Bruce Lindsay, Don Chamberlin, Pat Selinger, David Dewitt, Michael Stonebraker. They just love solving hard problems. And they wanted to share that elegance with a new generation. And so they really focused on growing the next generation of leaders, which became the Mike Carey's and the Mohan's and the Lagerhaus's of the world. And that culture grew over multiple generations with the previous generation cultivating, challenging, and advocating for the next, I was really lucky to grow up in that culture. And I've advanced my career as a result, as being part of it. The reason I joined Couchbase is because I see that culture alive and well here. Our two fundamental values on the engineering side, are merit and mentorship. >> One of the things I want to get your thoughts on, on the database questions. I remember, back in the old glory days, you mentioned some of those luminaries, you know, there wasn't many database geeks out there, there was kind of a small community, now, as databases are everywhere. So you see, there's no one database that has rule in the world, but you starting to see a pattern of database, kinds of things are emerging, more databases than ever before, they are on the internet, they are on the cloud, there are none the edge. It's essentially, we're living in a large distributed computing environment. So now it's cool to be in databases because they're everywhere. (laughing) So, I mean, this is kind of where we are at. What's your reaction to that? >> You're absolutely right. There used to be a few small vendors and a few key technologies and it's grown over the years, but the fundamental problems are the same, data integrity, performance and scalability in the face of distributed systems. Those were all the hard problems that those key leaders solved back in the sixties and seventies. They're not new problems. They're still there. And they did a lot of the fundamental work that you can apply and reapply in different scenarios and situations. >> That's pretty exciting. I love that. I love the different architectures that are emerging and allows for more creativity for application developers. And this becomes like the key thing we're seeing right now, driving the business and a big conversation here at the, at the event is the powering of these modern applications that need low latency. There's no more, not many spinning disks anymore. It's all in RAM, all these kinds of different memory, you got centralization, you got all kinds of new constructs. How do you make sense of it all? How do you talk to customers? What's the main core thing happening right now? If you had to describe it. >> Yeah, it depends on the type of customer you're talking to. We have focused primarily on the enterprise market and in that market, there are really fundamental issues. Information for these enterprises is key. It's their core asset that they have and they understand very well that they need to protect it and make it available more quickly. I started as a DBA at Morgan Stanley, back, right out of college. And at the time I think it was, it probably still is, but at the time it was the best run IT shop that I'd ever seen in my life. The fundamental problems that we had to solve to get information from one stock exchange to another, to get it to the SEC are the same problems that we're solving today. Back then we were working on mainframes and over high-speed Datacom links. Today, it's the same kind of problem. It's just the underlying infrastructure has changed. >> Yeah, the key, there has been a big supporter of women in tech. We've done thousands of interviews and why I got you. I want to ask you if you don't mind, career advice that you give women who are starting out in the field of engineering, computer science. What do you wish you knew when you started your career? And if you could be that person now, what would you say? >> Yeah, well, a lot of things I wish I knew then that I know now, but I think there are two key aspects to a successful career in engineering. I actually got started as a math major and the reason I became a math major is a little convoluted. As a girl, I was told we were bad at math. And so for some reason I decided that I had to major in it. That's actually how I got my start, but I've had a great career. And I think there are really two key aspects. First, is that it is a discipline in which respect is gained through merit. As I had mentioned earlier, engineers are notoriously detail-oriented and most are, perfectionists. They love elegant, well thought-out solutions and give respect when they see one. So understanding this can be a very important advantage if you're always prepared and you always bring your A-game to every debate, every presentation, every conversation, you have build up respect among your team, simply through merit. While that may mean that you need to be prepared to defend every point early on, say, in your graduate career or when you're starting, over time others will learn to trust your judgment and begin to intuitively follow your lead just by reputation. The reverse is also true. If you don't bring your A-game and you don't come prepared to debate, you will quickly lose respect. And that's particularly true if you're a woman. So if you don't know your stuff, don't engage in the debate until you do. >> That's awesome advice. >> That's... >> All right, continue. >> Thank you. So my second piece of advice that I wish I could give my younger self is to understand the roles of leaders and influencers in your career and the importance of choosing and purposely working with each. I like to break it down into three types of influencers, managers, mentors, and advocates. So that first group are the people in your management chain. It's your first line manager, your director, your VP, et cetera. Their role in your career is to help you measure short-term success. And particularly with how that success aligns with their goals and the company's goals. But it's important to understand that they are not your mentors and they may not have a direct interest in your long-term career success. I like to think of them as, say, you're sixth grade math teacher. You know, you getting an A in the class and advancing to seventh grade. They own you for that. But whether you get that basketball scholarship to college or getting to Harvard or become a CEO, they have very little influence over that. So a mentor is someone who does have a shared interest in your long-term success, maybe by your relationship with him or her, or because by helping you shape your career and achieve your own success, you help advance their goals. Whether it be the company success or helping more women achieve leadership positions or getting more kids into college on a basketball scholarship, whatever it is, they have some long-term goal that aligns with helping you with your career. And they give great advice. But that mentor is not enough because they're often outside the sphere of influence in your current position. And while they can offer great advice and coaching, they may not be able to help you directly advance. That's the role of the third type of influencer. Somebody that I call an advocate. An advocate is someone that's in a position to directly influence your advancement and champion you and your capabilities to others. They are in influential positions and others place great value in their opinions. Advocates stay with you throughout your career, and they'll continue to support you and promote you wherever you are and wherever they are, whether that's the same organization or not. They're the ones who, when a leadership position opens up will say, I think Mary's the right person to take on that challenge, or we need to move in a new direction, I think Mary's the right person to lead that effort. Now advocates are the most important people to identify early on and often in your career. And they're often the most overlooked. People early on often pay too much attention and rely on their management chain for advancement. Managers change on a dime, but mentors and advocates are there for you for the long haul. And that's one of the unique things about the database culture. Those set of advocates were just there already because they had focused on building the next generation. So I consider, you know, Mike Carey as my father and Mike Stonebraker as my grandfather, and Jim Gray as my great-grandfather and they're always there to advocate for me. >> That's like a schema and a database. You got to have it all right there, kind of teed up. Beautiful. (laughing) Great advice. >> Exactly. >> Thank you for that. That was really a masterclass. And that's going to be great advice for folks, really trying to figure out how to play the cards they have and the situation, and to double down or move and find other opportunities. So great stuff there. I do have to ask you Mary, thanks for coming on the technical side and the product side. Couchbase Capella was launched in conjunction with the event. What is the bottom line for that as, as an Operations and Engineering, built the products and rolled it out. What's the main top line message for about that product? >> Yeah. Well, we're very excited about the release of Capella and what it brings to the table is that it's a fully managed and automated database cloud offering so that customers can focus on development and building and improving their applications and reducing the time to market without having to worry about the hard problems underneath, and the operational database management efforts that come with it. As I mentioned earlier, I started my career as a DBA and it was one of the most sought after and highly paid positions in IT because operating a database required so much work. So with Capella, what we're seeing is, taking that job away from me. I'm not going to be able to apply for a DBA tomorrow. >> That's great stuff. Well, great. Thanks for coming. I really appreciate it. Congratulations on the company and the public offering this past summer in July and thanks for that great commentary and insight on theCUBE here. Thank you. >> Thank you very much. >> Okay. Mary Roth, VP of Engineering Operations at Couchbase part of Couchbase ConnectONLINE. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music playing)

>>And welcome to the cubes coverage of Couchbase connect online, Mary Roth, VP of engineering operations with couch basis here for Couchbase connect online. Mary. Great to see you. Thanks for coming on remotely for this segment. >>Thank you very much. It's great to be here. >>Love the fire in the background, a little fireside chat here, kind of happening, but I want to get into shooting, you know, engineering and operations with the pandemic has really kind of shown that, you know, engineers and developers have been good working remotely for a while, but for the most part it's impacted companies in general, across the organizations. How did the Couchbase engineering team adapt to the remote work? >>Uh, great question. Um, and I actually think the Couchbase team responded very well to this new model of working imposed by the pandemic. And I have a unique perspective on the couch space journey. I joined in February, 2020 after 20 plus years at IBM, which had embraced a hybrid in-office rewrote remote work model many years earlier. So in my IBM career, I live four minutes away from my research lab in almond and valley, but IBM is a global company with headquarters on the east coast and SU. So throughout my career, I often found myself on phone calls with people around the globe at 5:00 AM in the morning, I quickly learned and quickly adopted to a hybrid model. I'd go into the office to collaborate and have in-person meetings when needed. But if I was on the phone at >> 5: 00 AM in the morning, um, I didn't feel the need to get up at 4:30 AM to go in. >>I just worked from home and I discovered I could be more productive. They're doing think time work. And I really only needed the in-person time for collaboration. These hybrid model allowed me to have a great career at IBM and raise my two daughters at the same time. So when I joined Couchbase I joined a company that was all about being in-person and instead of a four minute commute, it was going to be an hour or more commute for me each way. This was going to be a really big transition for me, but I was excited enough by couch facing what it offered that I decided to give it a try. Well, that was February, 2020. I showed up early in the morning on March 10th, 2020 for an early morning meeting in person only to learn that I was one of the only few people that didn't get the memo. >>We were switching to a remote remote working model. And so over the last year, I have had the ability to watch cow's face and other companies pivot to make this remote working model possible and not only possible, but effective. And I'm really happy to see the results. Our remote work model does have its challenges that's for sure, but it also has its benefits better work-life balance and more time to interact with family members during the day and more quiet time, just to think we just did a retrospective on a major product release Couchbase server 7.0 that we did over the past 18 months. And one of the major insights by the leadership team is that working from home actually made people more effective. I don't think a full remote model is the right approach going forward, but a hybrid model that IBM adopted many years ago and that I was able to participate in for most of my career, I believe is a healthier and more productive approach. >>Well, great story. I love the, um, the, uh, you come back and now you take leverage all the best practices from the IBM days, but how did the, your team and the Couchbase engineering team react and were there any best practices or key learnings that you guys pulled out of that, >>Uh, the, the initial reaction was not good. I mean, as I mentioned, it was a culture based on in-person people had to be in person in person meetings. So it took a while to get used to it, but the, there was a forcing function, right? We had to work remotely. That was the only option. And so people made it work. I think the advancement of virtual meeting technology really, really helps a lot over earlier days in my career where I had just bad phone connections, that was very difficult. But with the virtual meetings that you have, where you can actually see people and interact, I think is really quite helpful. >>What's the DNA of the culture. What's the DNA. Every company's got the DNA entails Moore's law. Um, and at what's the engineering culture at Couchbase like if you could describe it. >>Uh, the engineering culture at Couchbase is very familiar to me. We are at our heart, a database company, and I grew up in the database world, which has a very unique culture based on two values, merit and mentorship. And we also focus on something that I like to call growing. The next generation. Now database technology started in the late sixties, early seventies with a few key players and institutions. These key players were extremely bright and they tackle it and solve really hard problems with elegant solutions long before anybody knew they were going to be necessary. Now, those original key players, people like Jim gray, Bruce Lindsey, Don Chamberlin, pat Salinger, David Dewitt, Michael Stonebraker. They just love solving hard problems. And they wanted to share that elegance with a new generation. And so they really focused on growing the next generation of leaders, which became the Mike caries and the Mohans and the lower houses of the world. And that culture grew over multiple generations with the previous generation cultivating, challenging and advocating for the next, I was really lucky to grow up in that culture. And I've advanced my career as a result, as being part of it. The reason I joined Couchbase is because I see that culture alive and well, here are two fundamental values on the engineering side, our merit and mentorship. >>One of the things I want to get your thoughts on, on the database questions. I remember, you know, back in the old glory days, you mentioned some of those luminaries, you know, there wasn't many database geeks out there, Zuri kind of small community now is databases are everywhere. So you see there's no one database that's ruling the world, but you starting to see a pattern of database kinds of things, and more emerging, more databases than ever before. They're on the internet, they're on the cloud. There are none the edge it's essentially we're living in a large distributed computing environment. So now it's cool to be in databases cause they're everywhere. So, I mean, this is kind of where we're at. What's your reaction to that? >>Uh, you're absolutely right there. There used to be a, a few small vendors and a few key technologies and it's grown over the years, but the fundamental problems are the same data, integrity, performance and scalability. And in the face of district distributed systems, those were all the hard problems that those key leaders solve back in the sixties and seventies. They're not, they're not new problems. They're still there. And they did a lot of the fundamental work that you can apply and reapply in different scenarios and situations. >>It's pretty exciting. I love that. I love the different architectures that are emerging and allows for more creativity for application developers. And this becomes like the key thing we're seeing right now, driving the business and a big conversation here at the, at the event is the powering, these modern applications that need low latency. There's no more, not many spinning disks anymore. It's all in Ram, all these kinds of different memory, you got decentralization and all kinds of new constructs. How do you make sense of it all? How do you talk to customers? What's the, what's the, what's the main core thing happening right now? If you had to describe it? >>Yeah, it depends on the type of customer you're talking to. Um, we have focused primarily on the enterprise market and in that market, there are really fundamental issues. Information for, for these enterprises is key. It's their core asset that they have and they understand very well that they need to protect it and make it available more quickly. I started as a DBA at Morgan Stanley back, um, right out of college. And at the time I think it was, it probably still is, but at the time it was the best run it shop that I'd ever seen in my life. The fundamental problems that we had to solve to get information from one stock exchange to another, to get it to the sec, um, are the same problems that we're solving today. Back then we were working on mainframes and over high-speed data comm links today, it's the same kind of problem. It's just the underlying infrastructure has changed. >>You know, the key has been a big supporter of women in tech. We've done thousands of interviews on why I got you. I want to ask you, uh, if you don't mind, um, career advice that you give women who are starting out in the field of engineering, computer science, what do you wish you knew when you started your career? And you could be that person now, what would you say? >>Yeah, well, there are a lot of things I wish I knew then, uh, that I know now, but I think there are two key aspects to a successful career in engineering. I actually got started as a math major and the reason I, I became a math major is a little convoluted. Is it as a girl, I was told we were bad at math. And so for some reason I decided that I had to major in it. That's actually how I got my start. Um, but I've had a great career and I think there are really two key aspects first. And is that it is a discipline in which respect is gained through merit. As I had mentioned earlier, engineers are notoriously detail oriented and most of our perfectionist, they love elegant, well thought out solutions and give respect when they see one. So understanding this can be a very important advantage if you're always prepared and you always bring your a game to every debate, every presentation, every conversation you have build up respect among your team, simply through merit. While that may mean that you need to be prepared to defend every point early on say, in your graduate career or when you're starting over time, others will learn to trust your judgment and begin to intuitively follow your lead just by reputation. The reverse is also true. If you don't bring your a game and you don't come prepared to debate, you will quickly lose respect. And that's particularly true if you're a woman. So if you don't know your stuff, don't engage in the debate until you do. That's awesome. >>That's >>Fine. Continue. Thank you. So my second piece of advice that I wish I could give my younger self is to understand the roles of leaders and influencers in your career and the importance of choosing and purposely working with each. I like to break it down into three types of influencers, managers, mentors, and advocates. So that first group are the people in your management chain. It's your first line manager, your director, your VP, et cetera. Their role in your career is to help you measure short-term success. And particularly with how that success aligns with their goals and the company's goals. But it's important to understand that they are not your mentors and they may not have a direct interest in your long-term career success. I like to think of them as say, you're sixth grade math teacher. You know, you're getting an a in the class and advancing to seventh grade. >>They own you for that. Um, but whether you get that basketball scholarship to college or getting to Harvard or become a CEO, they have very little influence over that. So a mentor is someone who does have a shared interest in your longterm success, maybe by your relationship with him or her, or because by helping you shape your career and achieve your own success, you help advance their goals. Whether it be the company success or helping more women achieve, we do put sip positions or getting more kids into college, on a basketball scholarship, whatever it is, they have some long-term goal that aligns with helping you with your career. And they gave great advice. But that mentor is not enough because they're often outside of the sphere of influence in your current position. And while they can offer great advice and coaching, they may not be able to help you directly advance. >>That's the role of the third type of influencer. Somebody that I call an advocate, an advocate is someone that's in a position to directly influence your advancement and champion you and your capabilities to others. They are in influential positions and others place, great value in their opinions. Advocates stay with you throughout your career, and they'll continue to support you and promote you wherever you are and wherever they are, whether that's the same organization or not. They're the ones who, when a leadership position opens up will say, I think Mary's the right person to take on that challenge, or we need to move in a new direction. I think Mary's the right person to lead that effort. Now advocates are the most important people to identify early on and often in your career. And they're often the most overlooked people early on, often pay too much attention and rely on their management chain for advanced managers, change on a dime, but mentors and advocates are there for you for the long haul. And that's one of the unique things about the database culture. Those set of advocates were just there already because they had focused on building the next generation. So I consider, you know, Mike Carey is my father and Mike Stonebraker is my grandfather. And Jim gray is my great-grandfather and they're always there to advocate for me. >>That's like a scheme and a database. You got to have it all white. They're kind of teed up. Beautiful, great advice. >>Thank you for that. That was really a masterclass. And that's going to be great advice for folks really trying to figure out how to play the cards they have a and the situation and to double down or move and find other opportunities. So great stuff there. I do have to ask you Maira, thanks for coming on the technical side and the product side Couchbase Capella was launched, uh, in conjunction with the event. What is, what is the bottom line for that as, as an operations and engineering, you know, built the products and roll it out. What's the main top line message for about that product? >>Yeah, well, we're very excited about the release of Capella and what it brings to the table is that it's a fully managed in an automated database cloud offering so that customers can focus on development and building and improving their applications and reducing the time to market without having to worry about the hard problems underneath and the operational database management efforts that come with it. Uh, as I mentioned earlier, I started my career as a UVA and it was one of the most sought after and highly paid positions in it because operating a database required so much work. So with Capella, what we're seeing is, you know, taking that job away from me, I'm not going to be able to apply for a DBA tomorrow. >>That's great stuff. Well, great. Thanks for coming. I really appreciate congratulations on the company and public offering this past summer in July and thanks for that great commentary and insight on the QPR. Thank you. >>Thank you very much. >>Okay. Mary Ross, VP of engineering operations at Couchbase part of Couchbase connect online. I'm John furry host of the cube. Thanks for watching.

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

>> Hi, I'm Natalie Ehrlich and welcome to the AWS startup showcase presented by The Cube. We have an amazing lineup of great guests who will share their insights on the latest innovations and solutions and leveraging cloud scale data in AI, security and life sciences. And now we're joined by the co-founders and co-CEOs of The Cube, Dave Vellante and John Furrier. Thank you gentlemen for joining me. >> Hey Natalie. >> Hey Natalie. >> How are you doing. Hey John. >> Well, I'd love to get your insights here, let's kick it off and what are you looking forward to. >> Dave, I think one of the things that we've been doing on the cube for 11 years is looking at the signal in the marketplace. I wanted to focus on this because AI is cutting across all industries. So we're seeing that with cybersecurity and life sciences, it's the first time we've had a life sciences track in the showcase, which is amazing because it shows that growth of the cloud scale. So I'm super excited by that. And I think that's going to showcase some new business models and of course the keynotes Ali Ghodsi, who's the CEO Data bricks pushing a billion dollars in revenue, clear validation that startups can go from zero to a billion dollars in revenues. So that should be really interesting. And of course the top venture capitalists coming in to talk about what the enterprise dynamics are all about. And what about you, Dave? >> You know, I thought it was an interesting mix and choice of startups. When you think about, you know, AI security and healthcare, and I've been thinking about that. Healthcare is the perfect industry, it is ripe for disruption. If you think about healthcare, you know, we all complain how expensive it is not transparent. There's a lot of discussion about, you know, can everybody have equal access that certainly with COVID the staff is burned out. There's a real divergence and diversity of the quality of healthcare and you know, it all results in patients not being happy, and I mean, if you had to do an NPS score on the patients and healthcare will be pretty low, John, you know. So when I think about, you know, AI and security in the context of healthcare in cloud, I ask questions like when are machines going to be able to better meet or make better diagnoses than doctors? And that's starting. I mean, it's really in assistance putting into play today. But I think when you think about cheaper and more accurate image analysis, when you think about the overall patient experience and trust and personalized medicine, self-service, you know, remote medicine that we've seen during the COVID pandemic, disease tracking, language translation, I mean, there are so many things where the cloud and data, and then it can help. And then at the end of it, it's all about, okay, how do I authenticate? How do I deal with privacy and personal information and tamper resistance? And that's where the security play comes in. So it's a very interesting mix of startups. I think that I'm really looking forward to hearing from... >> You know Natalie one of the things we talked about, some of these companies, Dave, we've talked a lot of these companies and to me the business model innovations that are coming out of two factors, the pandemic is kind of coming to an end so that accelerated and really showed who had the right stuff in my opinion. So you were either on the wrong side or right side of history when it comes to the pandemic and as we look back, as we come out of it with clear growth in certain companies and certain companies that adopted let's say cloud. And the other one is cloud scale. So the focus of these startup showcases is really to focus on how startups can align with the enterprise buyers and create the new kind of refactoring business models to go from, you know, a re-pivot or refactoring to more value. And the other thing that's interesting is that the business model isn't just for the good guys. If you look at say ransomware, for instance, the business model of hackers is gone completely amazing too. They're kicking it but in terms of revenue, they have their own they're well-funded machines on how to extort cash from companies. So there's a lot of security issues around the business model as well. So to me, the business model innovation with cloud-scale tech, with the pandemic forcing function, you've seen a lot of new kinds of decision-making in enterprises. You seeing how enterprise buyers are changing their decision criteria, and frankly their existing suppliers. So if you're an old guard supplier, you're going to be potentially out because if you didn't deliver during the pandemic, this is the issue that everyone's talking about. And it's kind of not publicized in the press very much, but this is actually happening. >> Well thank you both very much for joining me to kick off our AWS startup showcase. Now we're going to go to our very special guest Ali Ghodsi and John Furrier will seat with him for a fireside chat and Dave and I will see you on the other side. >> Okay, Ali great to see you. Thanks for coming on our AWS startup showcase, our second edition, second batch, season two, whatever we want to call it it's our second version of this new series where we feature, you know, the hottest startups coming out of the AWS ecosystem. And you're one of them, I've been there, but you're not a startup anymore, you're here pushing serious success on the revenue side and company. Congratulations and great to see you. >> Likewise. Thank you so much, good to see you again. >> You know I remember the first time we chatted on The Cube, you weren't really doing much software revenue, you were really talking about the new revolution in data. And you were all in on cloud. And I will say that from day one, you were always adamant that it was cloud cloud scale before anyone was really talking about it. And at that time it was on premises with Hadoop and those kinds of things. You saw that early. I remember that conversation, boy, that bet paid out great. So congratulations. >> Thank you so much. >> So I've got to ask you to jump right in. Enterprises are making decisions differently now and you are an example of that company that has gone from literally zero software sales to pushing a billion dollars as it's being reported. Certainly the success of Data bricks has been written about, but what's not written about is the success of how you guys align with the changing criteria for the enterprise customer. Take us through that and these companies here are aligning the same thing and enterprises want to change. They want to be in the right side of history. What's the success formula? >> Yeah. I mean, basically what we always did was look a few years out, the how can we help these enterprises, future proof, what they're trying to achieve, right? They have, you know, 30 years of legacy software and, you know baggage, and they have compliance and regulations, how do we help them move to the future? So we try to identify those kinds of secular trends that we think are going to maybe you see them a little bit right now, cloud was one of them, but it gets more and more and more. So we identified those and there were sort of three or four of those that we kind of latched onto. And then every year the passes, we're a little bit more right. Cause it's a secular trend in the market. And then eventually, it becomes a force that you can't kind of fight anymore. >> Yeah. And I just want to put a plug for your clubhouse talks with Andreessen Horowitz. You're always on clubhouse talking about, you know, I won't say the killer instinct, but being a CEO in a time where there's so much change going on, you're constantly under pressure. It's a lonely job at the top, I know that, but you've made some good calls. What was some of the key moments that you can point to, where you were like, okay, the wave is coming in now, we'd better get on it. What were some of those key decisions? Cause a lot of these startups want to be in your position, and a lot of buyers want to take advantage of the technology that's coming. They got to figure it out. What was some of those key inflection points for you? >> So if you're just listening to what everybody's saying, you're going to miss those trends. So then you're just going with the stream. So, Juan you mentioned that cloud. Cloud was a thing at the time, we thought it's going to be the thing that takes over everything. Today it's actually multi-cloud. So multi-cloud is a thing, it's more and more people are thinking, wow, I'm paying a lot's to the cloud vendors, do I want to buy more from them or do I want to have some optionality? So that's one. Two, open. They're worried about lock-in, you know, lock-in has happened for many, many decades. So they want open architectures, open source, open standards. So that's the second one that we bet on. The third one, which you know, initially wasn't sort of super obvious was AI and machine learning. Now it's super obvious, everybody's talking about it. But when we started, it was kind of called artificial intelligence referred to robotics, and machine learning wasn't a term that people really knew about. Today, it's sort of, everybody's doing machine learning and AI. So betting on those future trends, those secular trends as we call them super critical. >> And one of the things that I want to get your thoughts on is this idea of re-platforming versus refactoring. You see a lot being talked about in some of these, what does that even mean? It's people trying to figure that out. Re-platforming I get the cloud scale. But as you look at the cloud benefits, what do you say to customers out there and enterprises that are trying to use the benefits of the cloud? Say data for instance, in the middle of how could they be thinking about refactoring? And how can they make a better selection on suppliers? I mean, how do you know it used to be RFP, you deliver these speeds and feeds and you get selected. Now I think there's a little bit different science and methodology behind it. What's your thoughts on this refactoring as a buyer? What do I got to do? >> Well, I mean let's start with you said RFP and so on. Times have changed. Back in the day, you had to kind of sign up for something and then much later you're going to get it. So then you have to go through this arduous process. In the cloud, would pay us to go model elasticity and so on. You can kind of try your way to it. You can try before you buy. And you can use more and more. You can gradually, you don't need to go in all in and you know, say we commit to 50,000,000 and six months later to find out that wow, this stuff has got shelf where it doesn't work. So that's one thing that has changed it's beneficial. But the second thing is, don't just mimic what you had on prem in the cloud. So that's what this refactoring is about. If you had, you know, Hadoop data lake, now you're just going to have an S3 data lake. If you had an on-prem data warehouse now you just going to have a cloud data warehouse. You're just repeating what you did on prem in the cloud, architected for the future. And you know, for us, the most important thing that we say is that this lake house paradigm is a cloud native way of organizing your data. That's different from how you would do things on premises. So think through what's the right way of doing it in the cloud. Don't just try to copy paste what you had on premises in the cloud. >> It's interesting one of the things that we're observing and I'd love to get your reaction to this. Dave a lot** and I have been reporting on it is, two personas in the enterprise are changing their organization. One is I call IT ops or there's an SRE role developing. And the data teams are being dismantled and being kind of sprinkled through into other teams is this notion of data, pipelining being part of workflows, not just the department. Are you seeing organizational shifts in how people are organizing their resources, their human resources to take advantage of say that the data problems that are need to being solved with machine learning and whatnot and cloud-scale? >> Yeah, absolutely. So you're right. SRE became a thing, lots of DevOps people. It was because when the cloud vendors launched their infrastructure as a service to stitch all these things together and get it all working you needed a lot of devOps people. But now things are maturing. So, you know, with vendors like Data bricks and other multi-cloud vendors, you can actually get much higher level services where you don't need to necessarily have lots of lots of DevOps people that are themselves trying to stitch together lots of services to make this work. So that's one trend. But secondly, you're seeing more data teams being sort of completely ubiquitous in these organizations. Before it used to be you have one data team and then we'll have data and AI and we'll be done. ' It's a one and done. But that's not how it works. That's not how Google, Facebook, Twitter did it, they had data throughout the organization. Every BU was empowered. It's sales, it's marketing, it's finance, it's engineering. So how do you embed all those data teams and make them actually run fast? And you know, there's this concept of a data mesh which is super important where you can actually decentralize and enable all these teams to focus on their domains and run super fast. And that's really enabled by this Lake house paradigm in the cloud that we're talking about. Where you're open, you're basing it on open standards. You have flexibility in the data types and how they're going to store their data. So you kind of provide a lot of that flexibility, but at the same time, you have sort of centralized governance for it. So absolutely things are changing in the market. >> Well, you're just the professor, the masterclass right here is amazing. Thanks for sharing that insight. You're always got to go out of date and that's why we have you on here. You're amazing, great resource for the community. Ransomware is a huge problem, it's now the government's focus. We're being attacked and we don't know where it's coming from. This business models around cyber that's expanding rapidly. There's real revenue behind it. There's a data problem. It's not just a security problem. So one of the themes in all of these startup showcases is data is ubiquitous in the value propositions. One of them is ransomware. What's your thoughts on ransomware? Is it a data problem? Does cloud help? Some are saying that cloud's got better security with ransomware, then say on premise. What's your vision of how you see this ransomware problem being addressed besides the government taking over? >> Yeah, that's a great question. Let me start by saying, you know, we're a data company, right? And if you say you're a data company, you might as well just said, we're a privacy company, right? It's like some people say, well, what do you think about privacy? Do you guys even do privacy? We're a data company. So yeah, we're a privacy company as well. Like you can't talk about data without talking about privacy. With every customer, with every enterprise. So that's obviously top of mind for us. I do think that in the cloud, security is much better because, you know, vendors like us, we're investing so much resources into security and making sure that we harden the infrastructure and, you know, by actually having all of this infrastructure, we can monitor it, detect if something is, you know, an attack is happening, and we can immediately sort of stop it. So that's different from when it's on prem, you have kind of like the separated duties where the software vendor, which would have been us, doesn't really see what's happening in the data center. So, you know, there's an IT team that didn't develop the software is responsible for the security. So I think things are much better now. I think we're much better set up, but of course, things like cryptocurrencies and so on are making it easier for people to sort of hide. There decentralized networks. So, you know, the attackers are getting more and more sophisticated as well. So that's definitely something that's super important. It's super top of mind. We're all investing heavily into security and privacy because, you know, that's going to be super critical going forward. >> Yeah, we got to move that red line, and figure that out and get more intelligence. Decentralized trends not going away it's going to be more of that, less of the centralized. But centralized does come into play with data. It's a mix, it's not mutually exclusive. And I'll get your thoughts on this. Architectural question with, you know, 5G and the edge coming. Amazon's got that outpost stringent, the wavelength, you're seeing mobile world Congress coming up in this month. The focus on processing data at the edge is a huge issue. And enterprises are now going to be commercial part of that. So architecture decisions are being made in enterprises right now. And this is a big issue. So you mentioned multi-cloud, so tools versus platforms. Now I'm an enterprise buyer and there's no more RFPs. I got all this new choices for startups and growing companies to choose from that are cloud native. I got all kinds of new challenges and opportunities. How do I build my architecture so I don't foreclose a future opportunity. >> Yeah, as I said, look, you're actually right. Cloud is becoming even more and more something that everybody's adopting, but at the same time, there is this thing that the edge is also more and more important. And the connectivity between those two and making sure that you can really do that efficiently. My ask from enterprises, and I think this is top of mind for all the enterprise architects is, choose open because that way you can avoid locking yourself in. So that's one thing that's really, really important. In the past, you know, all these vendors that locked you in, and then you try to move off of them, they were highly innovative back in the day. In the 80's and the 90's, there were the best companies. You gave them all your data and it was fantastic. But then because you were locked in, they didn't need to innovate anymore. And you know, they focused on margins instead. And then over time, the innovation stopped and now you were kind of locked in. So I think openness is really important. I think preserving optionality with multi-cloud because we see the different clouds have different strengths and weaknesses and it changes over time. All right. Early on AWS was the only game that either showed up with much better security, active directory, and so on. Now Google with AI capabilities, which one's going to win, which one's going to be better. Actually, probably all three are going to be around. So having that optionality that you can pick between the three and then artificial intelligence. I think that's going to be the key to the future. You know, you asked about security earlier. That's how people detect zero day attacks, right? You ask about the edge, same thing there, that's where the predictions are going to happen. So make sure that you invest in AI and artificial intelligence very early on because it's not something you can just bolt on later on and have a little data team somewhere that then now you have AI and it's one and done. >> All right. Great insight. I've got to ask you, the folks may or may not know, but you're a professor at Berkeley as well, done a lot of great work. That's where you kind of came out of when Data bricks was formed. And the Berkeley basically was it invented distributed computing back in the 80's. I remember I was breaking in when Unix was proprietary, when software wasn't open you actually had the deal that under the table to get code. Now it's all open. Isn't the internet now with distributed computing and how interconnects are happening. I mean, the internet didn't break during the pandemic, which proves the benefit of the internet. And that's a positive. But as you start seeing edge, it's essentially distributed computing. So I got to ask you from a computer science standpoint. What do you see as the key learnings or connect the dots for how this distributed model will work? I see hybrids clearly, hybrid cloud is clearly the operating model but if you take it to the next level of distributed computing, what are some of the key things that you look for in the next five years as this starts to be completely interoperable, obviously software is going to drive a lot of it. What's your vision on that? >> Yeah, I mean, you know, so Berkeley, you're right for the gigs, you know, there was a now project 20, 30 years ago that basically is how we do things. There was a project on how you search in the very early on with Inktomi that became how Google and everybody else to search today. So workday was super, super early, sometimes way too early. And that was actually the mistake. Was that they were so early that people said that that stuff doesn't work. And then 20 years later you were invented. So I think 2009, Berkeley published just above the clouds saying the cloud is the future. At that time, most industry leaders said, that's just, you know, that doesn't work. Today, recently they published a research paper called, Sky Computing. So sky computing is what you get above the clouds, right? So we have the cloud as the future, the next level after that is the sky. That's one on top of them. That's what multi-cloud is. So that's a lot of the research at Berkeley, you know, into distributed systems labs is about this. And we're excited about that. Then we're one of the sky computing vendors out there. So I think you're going to see much more innovation happening at the sky level than at the compute level where you needed all those DevOps and SRE people to like, you know, build everything manually themselves. I can just see the memes now coming Ali, sky net, star track. You've got space too, by the way, space is another frontier that is seeing a lot of action going on because now the surface area of data with satellites is huge. So again, I know you guys are doing a lot of business with folks in that vertical where you starting to see real time data acquisition coming from these satellites. What's your take on the whole space as the, not the final frontier, but certainly as a new congested and contested space for, for data? >> Well, I mean, as a data vendor, we see a lot of, you know, alternative data sources coming in and people aren't using machine learning< AI to eat out signal out of the, you know, massive amounts of imagery that's coming out of these satellites. So that's actually a pretty common in FinTech, which is a vertical for us. And also sort of in the public sector, lots of, lots of, lots of satellites, imagery data that's coming. And these are massive volumes. I mean, it's like huge data sets and it's a super, super exciting what they can do. Like, you know, extracting signal from the satellite imagery is, and you know, being able to handle that amount of data, it's a challenge for all the companies that we work with. So we're excited about that too. I mean, definitely that's a trend that's going to continue. >> All right. I'm super excited for you. And thanks for coming on The Cube here for our keynote. I got to ask you a final question. As you think about the future, I see your company has achieved great success in a very short time, and again, you guys done the work, I've been following your company as you know. We've been been breaking that Data bricks story for a long time. I've been excited by it, but now what's changed. You got to start thinking about the next 20 miles stair when you look at, you know, the sky computing, you're thinking about these new architectures. As the CEO, your job is to one, not run out of money which you don't have to worry about that anymore, so hiring. And then, you got to figure out that next 20 miles stair as a company. What's that going on in your mind? Take us through your mindset of what's next. And what do you see out in that landscape? >> Yeah, so what I mentioned around Sky company optionality around multi-cloud, you're going to see a lot of capabilities around that. Like how do you get multi-cloud disaster recovery? How do you leverage the best of all the clouds while at the same time not having to just pick one? So there's a lot of innovation there that, you know, we haven't announced yet, but you're going to see a lot of it over the next many years. Things that you can do when you have the optionality across the different parts. And the second thing that's really exciting for us is bringing AI to the masses. Democratizing data and AI. So how can you actually apply machine learning to machine learning? How can you automate machine learning? Today machine learning is still quite complicated and it's pretty advanced. It's not going to be that way 10 years from now. It's going to be very simple. Everybody's going to have it at their fingertips. So how do we apply machine learning to machine learning? It's called auto ML, automatic, you know, machine learning. So that's an area, and that's not something that can be done with, right? But the goal is to eventually be able to automate a way the whole machine learning engineer and the machine learning data scientist altogether. >> You know it's really fun and talking with you is that, you know, for years we've been talking about this inside the ropes, inside the industry, around the future. Now people starting to get some visibility, the pandemics forced that. You seeing the bad projects being exposed. It's like the tide pulled out and you see all the scabs and bad projects that were justified old guard technologies. If you get it right you're on a good wave. And this is clearly what we're seeing. And you guys example of that. So as enterprises realize this, that they're going to have to look double down on the right projects and probably trash the bad projects, new criteria, how should people be thinking about buying? Because again, we talked about the RFP before. I want to kind of circle back because this is something that people are trying to figure out. You seeing, you know, organic, you come in freemium models as cloud scale becomes the advantage in the lock-in frankly seems to be the value proposition. The more value you provide, the more lock-in you get. Which sounds like that's the way it should be versus proprietary, you know, protocols. The protocol is value. How should enterprises organize their teams? Is it end to end workflows? Is it, and how should they evaluate the criteria for these technologies that they want to buy? >> Yeah, that's a great question. So I, you know, it's very simple, try to future proof your decision-making. Make sure that whatever you're doing is not blocking your in. So whatever decision you're making, what if the world changes in five years, make sure that if you making a mistake now, that's not going to bite you in about five years later. So how do you do that? Well, open source is great. If you're leveraging open-source, you can try it out already. You don't even need to talk to any vendor. Your teams can already download it and try it out and get some value out of it. If you're in the cloud, this pay as you go models, you don't have to do a big RFP and commit big. You can try it, pay the vendor, pay as you go, $10, $15. It doesn't need to be a million dollar contract and slowly grow as you're providing value. And then make sure that you're not just locking yourself in to one cloud or, you know, one particular vendor. As much as possible preserve your optionality because then that's not a one-way door. If it turns out later you want to do something else, you can, you know, pick other things as well. You're not locked in. So that's what I would say. Keep that top of mind that you're not locking yourself into a particular decision that you made today, that you might regret in five years. >> I really appreciate you coming on and sharing your with our community and The Cube. And as always great to see you. I really enjoy your clubhouse talks, and I really appreciate how you give back to the community. And I want to thank you for coming on and taking the time with us today. >> Thanks John, always appreciate talking to you. >> Okay Ali Ghodsi, CEO of Data bricks, a success story that proves the validation of cloud scale, open and create value, values the new lock-in. So Natalie, back to you for continuing coverage. >> That was a terrific interview John, but I'd love to get Dave's insights first. What were your takeaways, Dave? >> Well, if we have more time I'll tell you how Data bricks got to where they are today, but I'll say this, the most important thing to me that Allie said was he conveyed a very clear understanding of what data companies are outright and are getting ready. Talked about four things. There's not one data team, there's many data teams. And he talked about data is decentralized, and data has to have context and that context lives in the business. He said, look, think about it. The way that the data companies would get it right, they get data in teams and sales and marketing and finance and engineering. They all have their own data and data teams. And he referred to that as a data mesh. That's a term that is your mock, the Gany coined and the warehouse of the data lake it's merely a node in that global message. It meshes discoverable, he talked about federated governance, and Data bricks, they're breaking the model of shoving everything into a single repository and trying to make that the so-called single version of the truth. Rather what they're doing, which is right on is putting data in the hands of the business owners. And that's how true data companies do. And the last thing you talked about with sky computing, which I loved, it's that future layer, we talked about multi-cloud a lot that abstracts the underlying complexity of the technical details of the cloud and creates additional value on top. I always say that the cloud players like Amazon have given the gift to the world of 100 billion dollars a year they spend in CapEx. Thank you. Now we're going to innovate on top of it. Yeah. And I think the refactoring... >> Hope by John. >> That was great insight and I totally agree. The refactoring piece too was key, he brought that home. But to me, I think Data bricks that Ali shared there and why he's been open and sharing a lot of his insights and the community. But what he's not saying, cause he's humble and polite is they cracked the code on the enterprise, Dave. And to Dave's points exactly reason why they did it, they saw an opportunity to make it easier, at that time had dupe was the rage, and they just made it easier. They was smart, they made good bets, they had a good formula and they cracked the code with the enterprise. They brought it in and they brought value. And see that's the key to the cloud as Dave pointed out. You get replatform with the cloud, then you refactor. And I think he pointed out the multi-cloud and that really kind of teases out the whole future and landscape, which is essentially distributed computing. And I think, you know, companies are starting to figure that out with hybrid and this on premises and now super edge I call it, with 5G coming. So it's just pretty incredible. >> Yeah. Data bricks, IPO is coming and people should know. I mean, what everybody, they created spark as you know John and everybody thought they were going to do is mimic red hat and sell subscriptions and support. They didn't, they developed a managed service and they embedded AI tools to simplify data science. So to your point, enterprises could buy instead of build, we know this. Enterprises will spend money to make things simpler. They don't have the resources, and so this was what they got right was really embedding that, making a building a managed service, not mimicking the kind of the red hat model, but actually creating a new value layer there. And that's big part of their success. >> If I could just add one thing Natalie to that Dave saying is really right on. And as an enterprise buyer, if we go the other side of the equation, it used to be that you had to be a known company, get PR, you fill out RFPs, you had to meet all the speeds. It's like going to the airport and get a swab test, and get a COVID test and all kinds of mechanisms to like block you and filter you. Most of the biggest success stories that have created the most value for enterprises have been the companies that nobody's understood. And Andy Jazz's famous quote of, you know, being misunderstood is actually a good thing. Data bricks was very misunderstood at the beginning and no one kind of knew who they were but they did it right. And so the enterprise buyers out there, don't be afraid to test the startups because you know the next Data bricks is out there. And I think that's where I see the psychology changing from the old IT buyers, Dave. It's like, okay, let's let's test this company. And there's plenty of ways to do that. He illuminated those premium, small pilots, you don't need to go on these big things. So I think that is going to be a shift in how companies going to evaluate startups. >> Yeah. Think about it this way. Why should the large banks and insurance companies and big manufacturers and pharma companies, governments, why should they burn resources managing containers and figuring out data science tools if they can just tap into solutions like Data bricks which is an AI platform in the cloud and let the experts manage all that stuff. Think about how much money in time that saves enterprises. >> Yeah, I mean, we've got 15 companies here we're showcasing this batch and this season if you call it. That episode we are going to call it? They're awesome. Right? And the next 15 will be the same. And these companies could be the next billion dollar revenue generator because the cloud enables that day. I think that's the exciting part. >> Well thank you both so much for these insights. Really appreciate it. AWS startup showcase highlights the innovation that helps startups succeed. And no one knows that better than our very next guest, Jeff Barr. Welcome to the show and I will send this interview now to Dave and John and see you just in the bit. >> Okay, hey Jeff, great to see you. Thanks for coming on again. >> Great to be back. >> So this is a regular community segment with Jeff Barr who's a legend in the industry. Everyone knows your name. Everyone knows that. Congratulations on your recent blog posts we have reading. Tons of news, I want to get your update because 5G has been all over the news, mobile world congress is right around the corner. I know Bill Vass was a keynote out there, virtual keynote. There's a lot of Amazon discussion around the edge with wavelength. Specifically, this is the outpost piece. And I know there is news I want to get to, but the top of mind is there's massive Amazon expansion and the cloud is going to the edge, it's here. What's up with wavelength. Take us through the, I call it the power edge, the super edge. >> Well, I'm really excited about this mostly because it gives a lot more choice and flexibility and options to our customers. This idea that with wavelength we announced quite some time ago, at least quite some time ago if we think in cloud years. We announced that we would be working with 5G providers all over the world to basically put AWS in the telecom providers data centers or telecom centers, so that as their customers build apps, that those apps would take advantage of the low latency, the high bandwidth, the reliability of 5G, be able to get to some compute and storage services that are incredibly close geographically and latency wise to the compute and storage that is just going to give customers this new power and say, well, what are the cool things we can build? >> Do you see any correlation between wavelength and some of the early Amazon services? Because to me, my gut feels like there's so much headroom there. I mean, I was just riffing on the notion of low latency packets. I mean, just think about the applications, gaming and VR, and metaverse kind of cool stuff like that where having the edge be that how much power there. It just feels like a new, it feels like a new AWS. I mean, what's your take? You've seen the evolutions and the growth of a lot of the key services. Like EC2 and SA3. >> So welcome to my life. And so to me, the way I always think about this is it's like when I go to a home improvement store and I wander through the aisles and I often wonder through with no particular thing that I actually need, but I just go there and say, wow, they've got this and they've got this, they've got this other interesting thing. And I just let my creativity run wild. And instead of trying to solve a problem, I'm saying, well, if I had these different parts, well, what could I actually build with them? And I really think that this breadth of different services and locations and options and communication technologies. I suspect a lot of our customers and customers to be and are in this the same mode where they're saying, I've got all this awesomeness at my fingertips, what might I be able to do with it? >> He reminds me when Fry's was around in Palo Alto, that store is no longer here but it used to be back in the day when it was good. It was you go in and just kind of spend hours and then next thing you know, you built a compute. Like what, I didn't come in here, whether it gets some cables. Now I got a motherboard. >> I clearly remember Fry's and before that there was the weird stuff warehouse was another really cool place to hang out if you remember that. >> Yeah I do. >> I wonder if I could jump in and you guys talking about the edge and Jeff I wanted to ask you about something that is, I think people are starting to really understand and appreciate what you did with the entrepreneur acquisition, what you do with nitro and graviton, and really driving costs down, driving performance up. I mean, there's like a compute Renaissance. And I wonder if you could talk about the importance of that at the edge, because it's got to be low power, it has to be low cost. You got to be doing processing at the edge. What's your take on how that's evolving? >> Certainly so you're totally right that we started working with and then ultimately acquired Annapurna labs in Israel a couple of years ago. I've worked directly with those folks and it's really awesome to see what they've been able to do. Just really saying, let's look at all of these different aspects of building the cloud that were once effectively kind of somewhat software intensive and say, where does it make sense to actually design build fabricate, deploy custom Silicon? So from putting up the system to doing all kinds of additional kinds of security checks, to running local IO devices, running the NBME as fast as possible to support the EBS. Each of those things has been a contributing factor to not just the power of the hardware itself, but what I'm seeing and have seen for the last probably two or three years at this point is the pace of innovation on instance types just continues to get faster and faster. And it's not just cranking out new instance types because we can, it's because our awesomely diverse base of customers keeps coming to us and saying, well, we're happy with what we have so far, but here's this really interesting new use case. And we needed a different ratio of memory to CPU, or we need more cores based on the amount of memory, or we needed a lot of IO bandwidth. And having that nitro as the base lets us really, I don't want to say plug and play, cause I haven't actually built this myself, but it seems like they can actually put the different elements together, very very quickly and then come up with new instance types that just our customers say, yeah, that's exactly what I asked for and be able to just do this entire range of from like micro and nano sized all the way up to incredibly large with incredible just to me like, when we talk about terabytes of memory that are just like actually just RAM memory. It's like, that's just an inconceivably large number by the standards of where I started out in my career. So it's all putting this power in customer hands. >> You used the term plug and play, but it does give you that nitro gives you that optionality. And then other thing that to me is really exciting is the way in which ISVs are writing to whatever's underneath. So you're making that, you know, transparent to the users so I can choose as a customer, the best price performance for my workload and that that's just going to grow that ISV portfolio. >> I think it's really important to be accurate and detailed and as thorough as possible as we launch each one of these new instance types with like what kind of processor is in there and what clock speed does it run at? What kind of, you know, how much memory do we have? What are the, just the ins and outs, and is it Intel or arm or AMD based? It's such an interesting to me contrast. I can still remember back in the very very early days of back, you know, going back almost 15 years at this point and effectively everybody said, well, not everybody. A few people looked and said, yeah, we kind of get the value here. Some people said, this just sounds like a bunch of generic hardware, just kind of generic hardware in Iraq. And even back then it was something that we were very careful with to design and optimize for use cases. But this idea that is generic is so, so, so incredibly inaccurate that I think people are now getting this. And it's okay. It's fine too, not just for the cloud, but for very specific kinds of workloads and use cases. >> And you guys have announced obviously the performance improvements on a lamb** does getting faster, you got the per billing, second billings on windows and SQL server on ECE too**. So I mean, obviously everyone kind of gets that, that's been your DNA, keep making it faster, cheaper, better, easier to use. But the other area I want to get your thoughts on because this is also more on the footprint side, is that the regions and local regions. So you've got more region news, take us through the update on the expansion on the footprint of AWS because you know, a startup can come in and these 15 companies that are here, they're global with AWS, right? So this is a major benefit for customers around the world. And you know, Ali from Data bricks mentioned privacy. Everyone's a privacy company now. So the huge issue, take us through the news on the region. >> Sure, so the two most recent regions that we announced are in the UAE and in Israel. And we generally like to pre-announce these anywhere from six months to two years at a time because we do know that the customers want to start making longer term plans to where they can start thinking about where they can do their computing, where they can store their data. I think at this point we now have seven regions under construction. And, again it's all about customer trice. Sometimes it's because they have very specific reasons where for based on local laws, based on national laws, that they must compute and restore within a particular geographic area. Other times I say, well, a lot of our customers are in this part of the world. Why don't we pick a region that is as close to that part of the world as possible. And one really important thing that I always like to remind our customers of in my audience is, anything that you choose to put in a region, stays in that region unless you very explicitly take an action that says I'd like to replicate it somewhere else. So if someone says, I want to store data in the US, or I want to store it in Frankfurt, or I want to store it in Sao Paulo, or I want to store it in Tokyo or Osaka. They get to make that very specific choice. We give them a lot of tools to help copy and replicate and do cross region operations of various sorts. But at the heart, the customer gets to choose those locations. And that in the early days I think there was this weird sense that you would, you'd put things in the cloud that would just mysteriously just kind of propagate all over the world. That's never been true, and we're very very clear on that. And I just always like to reinforce that point. >> That's great stuff, Jeff. Great to have you on again as a regular update here, just for the folks watching and don't know Jeff he'd been blogging and sharing. He'd been the one man media band for Amazon it's early days. Now he's got departments, he's got peoples on doing videos. It's an immediate franchise in and of itself, but without your rough days we wouldn't have gotten all the great news we subscribe to. We watch all the blog posts. It's essentially the flow coming out of AWS which is just a tsunami of a new announcements. Always great to read, must read. Jeff, thanks for coming on, really appreciate it. That's great. >> Thank you John, great to catch up as always. >> Jeff Barr with AWS again, and follow his stuff. He's got a great audience and community. They talk back, they collaborate and they're highly engaged. So check out Jeff's blog and his social presence. All right, Natalie, back to you for more coverage. >> Terrific. Well, did you guys know that Jeff took a three week AWS road trip across 15 cities in America to meet with cloud computing enthusiasts? 5,500 miles he drove, really incredible I didn't realize that. Let's unpack that interview though. What stood out to you John? >> I think Jeff, Barr's an example of what I call direct to audience a business model. He's been doing it from the beginning and I've been following his career. I remember back in the day when Amazon was started, he was always building stuff. He's a builder, he's classic. And he's been there from the beginning. At the beginning he was just the blog and it became a huge audience. It's now morphed into, he was power blogging so hard. He has now support and he still does it now. It's basically the conduit for information coming out of Amazon. I think Jeff has single-handedly made Amazon so successful at the community developer level, and that's the startup action happened and that got them going. And I think he deserves a lot of the success for AWS. >> And Dave, how about you? What is your reaction? >> Well I think you know, and everybody knows about the cloud and back stop X** and agility, and you know, eliminating the undifferentiated, heavy lifting and all that stuff. And one of the things that's often overlooked which is why I'm excited to be part of this program is the innovation. And the innovation comes from startups, and startups start in the cloud. And so I think that that's part of the flywheel effect. You just don't see a lot of startups these days saying, okay, I'm going to do something that's outside of the cloud. There are some, but for the most part, you know, if you saw in software, you're starting in the cloud, it's so capital efficient. I think that's one thing, I've throughout my career. I've been obsessed with every part of the stack from whether it's, you know, close to the business process with the applications. And right now I'm really obsessed with the plumbing, which is why I was excited to talk about, you know, the Annapurna acquisition. Amazon bought and a part of the $350 million, it's reported, you know, maybe a little bit more, but that isn't an amazing acquisition. And the reason why that's so important is because Amazon is continuing to drive costs down, drive performance up. And in my opinion, leaving a lot of the traditional players in their dust, especially when it comes to the power and cooling. You have often overlooked things. And the other piece of the interview was that Amazon is actually getting ISVs to write to these new platforms so that you don't have to worry about there's the software run on this chip or that chip, or x86 or arm or whatever it is. It runs. And so I can choose the best price performance. And that's where people don't, they misunderstand, you always say it John, just said that people are misunderstood. I think they misunderstand, they confused, you know, the price of the cloud with the cost of the cloud. They ignore all the labor costs that are associated with that. And so, you know, there's a lot of discussion now about the cloud tax. I just think the pace is accelerating. The gap is not closing, it's widening. >> If you look at the one question I asked them about wavelength and I had a follow up there when I said, you know, we riff on it and you see, he lit up like he beam was beaming because he said something interesting. It's not that there's a problem to solve at this opportunity. And he conveyed it to like I said, walking through Fry's. But like, you go into a store and he's a builder. So he sees opportunity. And this comes back down to the Martine Casada paradox posts he wrote about do you optimize for CapEx or future revenue? And I think the tell sign is at the wavelength edge piece is going to be so creative and that's going to open up massive opportunities. I think that's the place to watch. That's the place I'm watching. And I think startups going to come out of the woodwork because that's where the action will be. And that's just Amazon at the edge, I mean, that's just cloud at the edge. I think that is going to be very effective. And his that's a little TeleSign, he kind of revealed a little bit there, a lot there with that comment. >> Well that's a to be continued conversation. >> Indeed, I would love to introduce our next guest. We actually have Soma on the line. He's the managing director at Madrona venture group. Thank you Soma very much for coming for our keynote program. >> Thank you Natalie and I'm great to be here and will have the opportunity to spend some time with you all. >> Well, you have a long to nerd history in the enterprise. How would you define the modern enterprise also known as cloud scale? >> Yeah, so I would say I have, first of all, like, you know, we've all heard this now for the last, you know, say 10 years or so. Like, software is eating the world. Okay. Put it another way, we think about like, hey, every enterprise is a software company first and foremost. Okay. And companies that truly internalize that, that truly think about that, and truly act that way are going to start up, continue running well and things that don't internalize that, and don't do that are going to be left behind sooner than later. Right. And the last few years you start off thing and not take it to the next level and talk about like, not every enterprise is not going through a digital transformation. Okay. So when you sort of think about the world from that lens. Okay. Modern enterprise has to think about like, and I am first and foremost, a technology company. I may be in the business of making a car art, you know, manufacturing paper, or like you know, manufacturing some healthcare products or what have you got out there. But technology and software is what is going to give me a unique, differentiated advantage that's going to let me do what I need to do for my customers in the best possible way [Indistinct]. So that sort of level of focus, level of execution, has to be there in a modern enterprise. The other thing is like not every modern enterprise needs to think about regular. I'm competing for talent, not anymore with my peers in my industry. I'm competing for technology talent and software talent with the top five technology companies in the world. Whether it is Amazon or Facebook or Microsoft or Google, or what have you cannot think, right? So you really have to have that mindset, and then everything flows from that. >> So I got to ask you on the enterprise side again, you've seen many ways of innovation. You've got, you know, been in the industry for many, many years. The old way was enterprises want the best proven product and the startups want that lucrative contract. Right? Yeah. And get that beach in. And it used to be, and we addressed this in our earlier keynote with Ali and how it's changing, the buyers are changing because the cloud has enabled this new kind of execution. I call it agile, call it what you want. Developers are driving modern applications, so enterprises are still, there's no, the playbooks evolving. Right? So we see that with the pandemic, people had needs, urgent needs, and they tried new stuff and it worked. The parachute opened as they say. So how do you look at this as you look at stars, you're investing in and you're coaching them. What's the playbook? What's the secret sauce of how to crack the enterprise code today. And if you're an enterprise buyer, what do I need to do? I want to be more agile. Is there a clear path? Is there's a TSA to let stuff go through faster? I mean, what is the modern playbook for buying and being a supplier? >> That's a fantastic question, John, because I think that sort of playbook is changing, even as we speak here currently. A couple of key things to understand first of all is like, you know, decision-making inside an enterprise is getting more and more de-centralized. Particularly decisions around what technology to use and what solutions to use to be able to do what people need to do. That decision making is no longer sort of, you know, all done like the CEO's office or the CTO's office kind of thing. Developers are more and more like you rightly said, like sort of the central of the workflow and the decision making process. So it'll be who both the enterprises, as well as the startups to really understand that. So what does it mean now from a startup perspective, from a startup perspective, it means like, right. In addition to thinking about like hey, not do I go create an enterprise sales post, do I sell to the enterprise like what I might have done in the past? Is that the best way of moving forward, or should I be thinking about a product led growth go to market initiative? You know, build a product that is easy to use, that made self serve really works, you know, get the developers to start using to see the value to fall in love with the product and then you think about like hey, how do I go translate that into a contract with enterprise. Right? And more and more what I call particularly, you know, startups and technology companies that are focused on the developer audience are thinking about like, you know, how do I have a bottom up go to market motion? And sometime I may sort of, you know, overlap that with the top down enterprise sales motion that we know that has been going on for many, many years or decades kind of thing. But really this product led growth bottom up a go to market motion is something that we are seeing on the rise. I would say they're going to have more than half the startup that we come across today, have that in some way shape or form. And so the enterprise also needs to understand this, the CIO or the CTO needs to know that like hey, I'm not decision-making is getting de-centralized. I need to empower my engineers and my engineering managers and my engineering leaders to be able to make the right decision and trust them. I'm going to give them some guard rails so that I don't find myself in a soup, you know, sometime down the road. But once I give them the guard rails, I'm going to enable people to make the decisions. People who are closer to the problem, to make the right decision. >> Well Soma, what are some of the ways that startups can accelerate their enterprise penetration? >> I think that's another good question. First of all, you need to think about like, Hey, what are enterprises wanting to rec? Okay. If you start off take like two steps back and think about what the enterprise is really think about it going. I'm a software company, but I'm really manufacturing paper. What do I do? Right? The core thing that most enterprises care about is like, hey, how do I better engage with my customers? How do I better serve my customers? And how do I do it in the most optimal way? At the end of the day that's what like most enterprises really care about. So startups need to understand, what are the problems that the enterprise is trying to solve? What kind of tools and platform technologies and infrastructure support, and, you know, everything else that they need to be able to do what they need to do and what only they can do in the most optimal way. Right? So to the extent you are providing either a tool or platform or some technology that is going to enable your enterprise to make progress on what they want to do, you're going to get more traction within the enterprise. In other words, stop thinking about technology, and start thinking about the customer problem that they want to solve. And the more you anchor your company, and more you anchor your conversation with the customer around that, the more the enterprise is going to get excited about wanting to work with you. >> So I got to ask you on the enterprise and developer equation because CSOs and CXOs, depending who you talk to have that same answer. Oh yeah. In the 90's and 2000's, we kind of didn't, we throttled down, we were using the legacy developer tools and cloud came and then we had to rebuild and we didn't really know what to do. So you seeing a shift, and this is kind of been going on for at least the past five to eight years, a lot more developers being hired yet. I mean, at FinTech is clearly a vertical, they always had developers and everyone had developers, but there's a fast ramp up of developers now and the role of open source has changed. Just looking at the participation. They're not just consuming open source, open source is part of the business model for mainstream enterprises. How is this, first of all, do you agree? And if so, how has this changed the course of an enterprise human resource selection? How they're organized? What's your vision on that? >> Yeah. So as I mentioned earlier, John, in my mind the first thing is, and this sort of, you know, like you said financial services has always been sort of hiring people [Indistinct]. And this is like five-year old story. So bear with me I'll tell you the firewall story and then come to I was trying to, the cloud CIO or the Goldman Sachs. Okay. And this is five years ago when people were still like, hey, is this cloud thing real and now is cloud going to take over the world? You know, am I really ready to put my data in the cloud? So there are a lot of questions and conversations can affect. The CIO of Goldman Sachs told me two things that I remember to this day. One is, hey, we've got a internal edict. That we made a decision that in the next five years, everything in Goldman Sachs is going to be on the public law. And I literally jumped out of the chair and I said like now are you going to get there? And then he laughed and said like now it really doesn't matter whether we get there or not. We want to set the tone, set the direction for the organization that hey, public cloud is here. Public cloud is there. And we need to like, you know, move as fast as we realistically can and think about all the financial regulations and security and privacy. And all these things that we care about deeply. But given all of that, the world is going towards public load and we better be on the leading edge as opposed to the lagging edge. And the second thing he said, like we're talking about like hey, how are you hiring, you know, engineers at Goldman Sachs Canada? And he said like in hey, I sort of, my team goes out to the top 20 schools in the US. And the people we really compete with are, and he was saying this, Hey, we don't compete with JP Morgan or Morgan Stanley, or pick any of your favorite financial institutions. We really think about like, hey, we want to get the best talent into Goldman Sachs out of these schools. And we really compete head to head with Google. We compete head to head with Microsoft. We compete head to head with Facebook. And we know that the caliber of people that we want to get is no different than what these companies want. If you want to continue being a successful, leading it, you know, financial services player. That sort of tells you what's going on. You also talked a little bit about like hey, open source is here to stay. What does that really mean kind of thing. In my mind like now, you can tell me that I can have from given my pedigree at Microsoft, I can tell you that we were the first embraces of open source in this world. So I'll say that right off the bat. But having said that we did in our turn around and said like, hey, this open source is real, this open source is going to be great. How can we embrace and how can we participate? And you fast forward to today, like in a Microsoft is probably as good as open source as probably any other large company I would say. Right? Including like the work that the company has done in terms of acquiring GitHub and letting it stay true to its original promise of open source and community can I think, right? I think Microsoft has come a long way kind of thing. But the thing that like in all these enterprises need to think about is you want your developers to have access to the latest and greatest tools. To the latest and greatest that the software can provide. And you really don't want your engineers to be reinventing the wheel all the time. So there is something available in the open source world. Go ahead, please set up, think about whether that makes sense for you to use it. And likewise, if you think that is something you can contribute to the open source work, go ahead and do that. So it's really a two way somebody Arctic relationship that enterprises need to have, and they need to enable their developers to want to have that symbiotic relationship. >> Soma, fantastic insights. Thank you so much for joining our keynote program. >> Thank you Natalie and thank you John. It was always fun to chat with you guys. Thank you. >> Thank you. >> John we would love to get your quick insight on that. >> Well I think first of all, he's a prolific investor the great from Madrona venture partners, which is well known in the tech circles. They're in Seattle, which is in the hub of I call cloud city. You've got Amazon and Microsoft there. He'd been at Microsoft and he knows the developer ecosystem. And reason why I like his perspective is that he understands the value of having developers as a core competency in Microsoft. That's their DNA. You look at Microsoft, their number one thing from day one besides software was developers. That was their army, the thousand centurions that one won everything for them. That has shifted. And he brought up open source, and .net and how they've embraced Linux, but something that tele before he became CEO, we interviewed him in the cube at an Xcel partners event at Stanford. He was open before he was CEO. He was talking about opening up. They opened up a lot of their open source infrastructure projects to the open compute foundation early. So they had already had that going and at that price, since that time, the stock price of Microsoft has skyrocketed because as Ali said, open always wins. And I think that is what you see here, and as an investor now he's picking in startups and investing in them. He's got to read the tea leaves. He's got to be in the right side of history. So he brings a great perspective because he sees the old way and he understands the new way. That is the key for success we've seen in the enterprise and with the startups. The people who get the future, and can create the value are going to win. >> Yeah, really excellent point. And just really quickly. What do you think were some of our greatest hits on this hour of programming? >> Well first of all I'm really impressed that Ali took the time to come join us because I know he's super busy. I think they're at a $28 billion valuation now they're pushing a billion dollars in revenue, gap revenue. And again, just a few short years ago, they had zero software revenue. So of these 15 companies we're showcasing today, you know, there's a next Data bricks in there. They're all going to be successful. They already are successful. And they're all on this rocket ship trajectory. Ali is smart, he's also got the advantage of being part of that Berkeley community which they're early on a lot of things now. Being early means you're wrong a lot, but you're also right, and you're right big. So Berkeley and Stanford obviously big areas here in the bay area as research. He is smart, He's got a great team and he's really open. So having him share his best practices, I thought that was a great highlight. Of course, Jeff Barr highlighting some of the insights that he brings and honestly having a perspective of a VC. And we're going to have Peter Wagner from wing VC who's a classic enterprise investors, super smart. So he'll add some insight. Of course, one of the community session, whenever our influencers coming on, it's our beat coming on at the end, as well as Katie Drucker. Another Madrona person is going to talk about growth hacking, growth strategies, but yeah, sights Raleigh coming on. >> Terrific, well thank you so much for those insights and thank you to everyone who is watching the first hour of our live coverage of the AWS startup showcase for myself, Natalie Ehrlich, John, for your and Dave Vellante we want to thank you very much for watching and do stay tuned for more amazing content, as well as a special live segment that John Furrier is going to be hosting. It takes place at 12:30 PM Pacific time, and it's called cracking the code, lessons learned on how enterprise buyers evaluate new startups. Don't go anywhere.

>>welcome back. And thanks for joining us for our second session. External data, your new leading indicators. We'll be hearing from industry leaders as they share best practices and challenges in leveraging external data. This panel will be a true conversation on the part of the possible. All right, let's get to >>it >>today. We're excited to be joined by thought spots. Chief Data Strategy Officer Cindy Housing Deloitte's chief data officer Manteo, the founder and CEO of Eagle Alfa. And it Kilduff and Snowflakes, VP of data marketplace and customer product strategy. Matt Glickman. Cindy. Without further ado, the floor is yours. >>Thank you, Mallory. And I am thrilled to have this brilliant team joining us from around the world. And they really bring each a very unique perspective. So I'm going to start from further away. Emmett, Welcome. Where you joining us from? >>Thanks for having us, Cindy. I'm joining from Dublin, Ireland, >>great. And and tell us a little bit about Eagle Alfa. What do you dio >>from a company's perspective? Think of Eagle Alfa as an aggregator off all the external data sets on a word I'll use a few times. Today is a big advantage we could bring companies is we have a data concierge service. There's so much data we can help identify the right data sets depending on the specific needs of the company. >>Yeah. And so, Emma, you know, people think I was a little I kind of shocked the industry. Going from gardener to a tech startup. Um, you have had a brave journey as well, Going from financial services to starting this company, really pioneering it with I think the most data sets of any of thes is that right? >>Yes, it was. It was a big jump to go from Morgan Stanley. Uh, leave the comforts of that environment Thio, PowerPoint deck and myself raising funding eight years ago s So it was a big jump on. We were very early in our market. It's in the last few years where there's been real momentum and adoption by various types of verticals. The hedge funds were first, maybe then private equity, but corporate sar are following quite quickly from behind. That will be the biggest users, in our view, by by a significant distance. >>Yeah, great. Thank um, it So we're going to go a little farther a field now, but back to the U. S. So, Juan, where you joining us from? >>Hey, Cindy. Thanks for having me. I'm joining you from Houston, Texas. >>Great. Used to be my home. Yeah, probably see Rice University back there. And you have a distinct perspective serving both Deloitte customers externally, but also internally. Can you tell us about that? >>Yeah, absolutely. So I serve as the Lord consultants, chief data officer, and as a professional service firm, I have the responsibility for overseeing our overall data agenda, which includes both the way we use data and insights to run and operate our own business, but also in how we develop data and insights services that we then take to market and how we serve our dealers and clients. >>Great. Thank you, Juan. And last but not least, Matt Glickman. Kind of in my own backyard in New York. Right, Matt? >>Correct. Joining I haven't been into the city and many months, but yes, um, based in New York. >>Okay. Great. And so, Matt, you and Emmett also, you know, brave pioneers in this space, and I'm remembering a conversation you and I shared when you were still a J. P. Morgan, I believe. And you're Goldman Sachs. Sorry. Sorry. Goldman. Can you Can you share that with us? >>Sure. I made the move back in 2015. Um, when everyone thought, you know, my wife, my wife included that I was crazy. I don't know if I would call it Comfortable was emitted, but particularly had been there for a long time on git suffered in some ways. A lot of the pains we're talking about today, given the number of data, says that the amount of of new data sets that are always demand for having run analytics teams at Goldman, seeing the pain and realizing that this pain was not unique to Goldman Sachs, it was being replicated everywhere across the industry, um, in a mind boggling way and and the fortuitous, um, luck to have one of snowflakes. Founders come to pitch snowflake to Goldman a little bit early. Um, they became a customer later, but a little bit early in 2014. And, you know, I realized that this was clearly, you know, the answer from first principles on bond. If I ever was going to leave, this was a problem. I was acutely aware of. And I also was aware of how much the man that was in financial services for a better solution and how the cloud could really solve this problem in particular the ability to not have to move data in and out of these organizations. And this was something that I saw the future of. Thank you, Andi, that this was, you know, sort of the pain that people just expected to pay. Um, this price if you need a data, there was method you had thio. You had to use you either ftp data in and out. You had data that was being, you know, dropped off and, you know, maybe in in in a new ways and cloud buckets or a P i s You have to suck all this data down and reconstruct it. And God forbid the formats change. It was, you know, a nightmare. And then having issues with data, you had a what you were seeing internally. You look nothing like what the data vendors were seeing because they want a completely different system, maybe model completely differently. Um, but this was just the way things were. Everyone had firewalls. Everyone had their own data centers. There was no other way on git was super costly. And you know this. I won't even share the the details of you know, the errors that would occur in the pain that would come from that, Um what I realized it was confirmed. What I saw it snowflake at the time was once everyone moves to run their actual workloads in this in the cloud right where you're now beyond your firewall, you'll have all this scale. But on top of that, you'll be able to point at data from these vendors were not there the traditional data vendors. Or, you know, this new wave of alternative data vendors, for example, like the ones that eagle out for brings together And bring these all these data sets together with your own internal data without moving it. Yeah, this was a fundamental shift of what you know, it's in some ways, it was a side effect of everyone moving to the cloud for costs and scale and elasticity. But as a side effect of that is what we talked about, You know it snowflake summit, you know, yesterday was this notion of a data cloud that would connect data between regions between cloud vendors between customers in a way where you could now reference data. Just like your reference websites today, I don't download CNN dot com. I point at it, and it points me to something else. I'm always seeing the latest version, obviously, and we can, you know, all collaborate on what I'm seeing on that website. That's the same thing that now can happen with data. So And I saw this as what was possible, and I distinctly asked the question, you know, the CEO of the time Is this possible? And not only was it possible it was a fundamental construct that was built into the way that snowflake was delivered. And then, lastly, this is what we learned. And I think this is what you know. M It also has been touting is that it's all great if data is out there and even if you lower that bar of access where data doesn't have to move, how do I know? Right? If I'm back to sitting at Goldman Sachs, how do I know what data is available to me now in this this you know, connected data network eso we released our data marketplace, which was a very different kind of marketplace than these of the past. Where for us, it was really like a global catalog that would elect a consumer data consumer. Noah data was available, but also level the playing field. Now we're now, you know, Eagle, Alfa, or even, you know, a new alternative data vendor build something in their in their basement can now publish that data set so that the world could see and consume and be aligned to, you know, snowflakes, core business, and not where we wouldn't have to be competing or having to take, um, any kind of custody of that data. So adding that catalog to this now ubiquitous access, um really changed the game and, you know, and then now I seem like a genius for making this move. But back then, like I said, we've seen I seem like instant. I was insane. >>Well, given, given that snowflake was the hottest aipo like ever, you were a genius. Uh, doing this, you know, six years in advance. E think we all agree on that, But, you know, a lot of this is still visionary. Um, you know, some of the most leading companies are already doing this. But one What? What is your take our Are you best in class customers still moving the data? Or is this like they're at least thinking about data monetization? What are you seeing from your perspective? >>Yeah, I mean, I did you know, the overall appreciation and understanding of you know, one. I got to get my house in order around my data, um, has something that has been, you know, understood and acted upon. Andi, I do agree that there is a shift now that says, you know, data silos alone aren't necessarily gonna bring me, you know, new and unique insights on dso enriching that with external third party data is absolutely, you know, sort of the the ship that we're seeing our customers undergo. Um, what I find extremely interesting in this space and what some of the most mature clients are doing is, you know, really taking advantage of these data marketplaces. But building data partnerships right there from what mutually exclusive, where there is a win win scenario for for you know, that organization and that could be, you know, retail customers or life science customers like with pandemic, right the way we saw companies that weren't naturally sharing information are now building these data partnership right that are going are going into mutually benefit, you know, all organizations that are sort of part of that value to Andi. I think that's the sort of really important criteria. And how we're seeing our clients that are extremely successful at this is that partnership has benefits on both sides of that equation, right? Both the data provider and then the consumer of that. And there has to be, you know, some way to ensure that both parties are are are learning right, gaining you insights to support, you know, whatever their business organization going on. >>Yeah, great one. So those data partnerships getting across the full value chain of sharing data and analytics Emmett, you work on both sides of the equation here, helping companies. Let's say let's say data providers maybe, like, you know, cast with human mobility monetize that. But then also people that are new to it. Where you seeing the top use cases? Well, >>interestingly, I agree with one of the supply side. One of the interesting trends is we're seeing a lot more data coming from large Corporates. Whether they're listed are private equity backed, as opposed to maybe data startups that are earning money just through data monetization. I think that's a great trend. I think that means a lot of the best. Data said it data is yet to come, um, in terms off the tough economy and how that's changed. I think the category that's had the most momentum and your references is Geo location data. It's that was the category at our conference in December 2000 and 12 that was pipped as the category to watch in 2019. On it didn't become that at all. Um, there were some regulatory concerns for certain types of geo data, but with with covert 19, it's Bean absolutely critical for governments, ministries of finance, central banks, municipalities, Thio crunch that data to understand what's happening in a real time basis. But from a company perspective, it's obviously critical as well. In terms of planning when customers might be back in the High Street on DSO, fourth traditionally consumer transaction data of all the 26 categories in our taxonomy has been the most popular. But Geo is definitely catching up your slide. Talked about being a tough economy. Just one point to contradict that for certain pockets of our clients, e commerce companies are having a field day, obviously, on they are very data driven and tech literate on day are they are really good client base for us because they're incredibly hungry, firm or data to help drive various, uh, decision making. >>Yeah, So fair enough. Some sectors of the economy e commerce, electron, ICS, healthcare are doing great. Others travel, hospitality, Um, super challenging. So I like your quote. The best is yet to come, >>but >>that's data sets is yet to come. And I do think the cloud is enabling that because we could get rid of some of the messy manual data flows that Matt you talked about, but nonetheless, Still, one of the hardest things is the data map. Things combining internal and external >>when >>you might not even have good master data. Common keys on your internal data. So any advice for this? Anyone who wants to take that? >>Sure I can. I can I can start. That's okay. I do think you know, one of the first problems is just a cataloging of the information that's out there. Um, you know, at least within our organization. When I took on this role, we were, you know, a large buyer of third party data. But our organization as a whole didn't necessarily have full visibility into what was being bought and for what purpose. And so having a catalog that helps us internally navigate what data we have and how we're gonna use it was sort of step number one. Um, so I think that's absolutely important. Um, I would say if we could go from having that catalog, you know, created manually to more automated to me, that's sort of the next step in our evolution, because everyone is saying right, the ongoing, uh, you know, creation of new external data sets. It's only going to get richer on DSO. We wanna be able to take advantage of that, you know, at the at the pacing speed, that data is being created. So going from Emanuel catalog to anonymous >>data >>catalog, I think, is a key capability for us. But then you know, to your second point, Cindy is how doe I then connect that to our own internal data to drive greater greater insights and how we run our business or how we serve our customers. Andi, that one you know really is a It's a tricky is a tricky, uh, question because I think it just depends on what data we're looking toe leverage. You know, we have this concept just around. Not not all data is created equal. And when you think about governance and you think about the management of your master data, your internal nomenclature on how you define and run your business, you know that that entire ecosystem begins to get extremely massive and it gets very broad and very deep on DSO for us. You know, government and master data management is absolutely important. But we took a very sort of prioritized approach on which domains do we really need to get right that drive the greatest results for our organization on dso mapping those domains like client data or employee data to these external third party data sources across this catalog was really the the unlocked for us versus trying to create this, you know, massive connection between all the external data that we're, uh, leveraging as well as all of our own internal data eso for us. I think it was very. It was a very tailored, prioritized approach to connecting internal data to external data based on the domains that matter most to our business. >>So if the domains so customer important domain and maybe that's looking at things, um, you know, whether it's social media data or customer transactions, you prioritized first by that, Is that right? >>That's correct. That's correct. >>And so, then, Matt, I'm going to throw it back to you because snowflake is in a unique position. You actually get to see what are the most popular data sets is is that playing out what one described are you seeing that play out? >>I I'd say Watch this space. Like like you said. I mean this. We've you know, I think we start with the data club. We solve that that movement problem, which I think was really the barrier that you tended to not even have a chance to focus on this mapping problem. Um, this notion of concordance, I think this is where I see the big next momentum in this space is going to be a flurry of traditional and new startups who deliver this concordance or knowledge graph as a service where this is no longer a problem that I have to solve internal to my organization. The notion of mastering which is again when everyone has to do in every organization like they used to have to do with moving data into the organization goes away. And this becomes like, I find the best of breed for the different scopes of data that I have. And it's delivered to me as a, you know, as a cloud service that just takes my data. My internal data maps it to these 2nd and 3rd party data sets. Um, all delivered to me, you know, a service. >>Yeah, well, that would be brilliant concordance as a service or or clean clean master data as a service. Um, using augmented data prep would be brilliant. So let's hope we get there. Um, you know, so 2020 has been a wild ride for everyone. If I could ask each of you imagine what is the art of the possible or looking ahead to the next to your and that you are you already mentioned the best is yet to come. Can you want to drill down on that. What what part of the best is yet to come or what is your already two possible? >>Just just a brief comment on mapping. Just this week we published a white paper on mapping, which is available for for anyone on eagle alfa dot com. It's It's a massive challenge. It's very difficult to solve. Just with technology Onda people have tried to solve it and get a certain level of accuracy, but can't get to 100% which which, which, which makes it difficult to solve it. If if if there is a new service coming out against 100% I'm all ears and that there will be a massive step forward for the entire data industry, even if it comes in a few years time, let alone next year, I think going back to the comment on data Cindy. Yes, I think boards of companies are Mawr and Mawr. Viewing data as an asset as opposed to an expense are a cost center on bond. They are looking therefore to get their internal house in order, as one was saying, but also monetize the data they are sitting on lots of companies. They're sitting on potentially valuable data. It's not all valuable on a lot of cases. They think it's worth a lot more than it is being frank. But in some cases there is valuable data on bond. If monetized, it can drop to the bottom line on. So I think that bodes well right across the world. A lot of the best date is yet to come on. I think a lot of firms like Deloitte are very well positioned to help drive that adoption because they are the trusted advisor to a lot of these Corporates. Um, so that's one thing. I think, from a company perspective. It's still we're still at the first base. It's quite frustrating how slow a lot of companies are to move and adopt, and some of them are haven't hired CDO. Some of them don't have their internal house in order. I think that has to change next year. I think if we have this conference at this time next year, I would expect that would hopefully be close to the tipping point for Corporates to use external data. And the Malcolm Gladwell tipping point on the final point I make is I think, that will hopefully start to see multi department use as opposed to silos again. Parliaments and silos, hopefully will be more coordinated on the company's side. Data could be used by marketing by sales by r and D by strategy by finance holds external data. So it really, hopefully will be coordinated by this time next year. >>Yeah, Thank you. So, to your point, there recently was an article to about one of the airlines that their data actually has more value than the company itself now. So I know, I know. We're counting on, you know, integrators trusted advisers like Deloitte to help us get there. Uh, one what? What do you think? And if I can also drill down, you know, financial services was early toe all of this because they needed the early signals. And and we talk about, you know, is is external data now more valuable than internal? Because we need those early signals in just such a different economy. >>Yeah, I think you know, for me, it's it's the seamless integration of all these external data sources and and the signals that organizations need and how to bring those into, you know, the day to day operations of your organization, right? So how do you bring those into, You know, you're planning process. How do you bring that into your sales process on DSO? I think for me success or or where I see the that the use and adoption of this is it's got to get down to that level off of operations for organizations. For this to continue to move at the pace and deliver the value that you know, we're all describing. I think we're going to get there. But I think until organizations truly get down to that level of operations and how they're using this data, it'll sort of seem like a Bolton, right? So for me, I think it's all about Mawr, the seamless integration. And I think to what Matt mentioned just around services that could help connect external data with internal data. I'll take that one step beyond and say, How can we have the data connect itself? Eso I had references Thio, you know, automation and machine learning. Um, there's significant advances in terms of how we're seeing, you know, mapping to occur in a auto generated fashion. I think this specific space and again the connection between external and internal data is a prime example of where we need to disrupt that, you know, sort of traditional data pipeline on. Try to automate that as much as possible. And let's have the data, you know, connect itself because it then sort of supports. You know, the first concept which waas How do we make it more seamless and integrated into, you know, the business processes of the organization's >>Yeah, great ones. So you two are thinking those automated, more intelligent data pipelines will get us there faster. Matt, you already gave us one. Great, Uh, look ahead, Any more to add to >>it, I'll give you I'll give you two more. One is a bit controversial, but I'll throw that you anyway, um, going back to the point that one made about data partnerships What you were saying Cindy about, you know, the value. These companies, you know, tends to be somehow sometimes more about the data they have than the actual service they provide. I predict you're going to see a wave of mergers and acquisitions. Um, that it's solely about locking down access to data as opposed to having data open up. Um to the broader, you know, economy, if I can, whether that be a retailer or, you know, insurance company was thes prime data assets. Um, you know, they could try to monetize that themselves, But if someone could acquire them and get exclusive access that data, I think that's going to be a wave of, um, in a that is gonna be like, Well, we bought this for this amount of money because of their data assets s. So I think that's gonna be a big wave. And it'll be maybe under the guise of data partnerships. But it really be about, you know, get locking down exclusive access to valuable data as opposed to trying toe monetize it itself number one. And then lastly, you know. Now, did you have this kind of ubiquity of data in this interconnected data network? Well, we're starting to see, and I think going to see a big wave of is hyper personalization of applications where instead of having the application have the data itself Have me Matt at Snowflake. Bring my data graph to applications. Right? This decoupling of we always talk about how you get data out of these applications. It's sort of the reverse was saying Now I want to bring all of my data access that I have 1st, 2nd and 3rd party into my application. Instead of having to think about getting all the data out of these applications, I think about it how when you you know, using a workout app in the consumer space, right? I can connect my Spotify or connect my apple music into that app to personalize the experience and bring my music list to that. Imagine if I could do that, you know, in a in a CRM. Imagine I could do that in a risk management. Imagine I could do that in a marketing app where I can bring my entire data graph with me and personalize that experience for, you know, for given what I have. And I think again, you know, partners like thoughts. But I think in a unique position to help enable that capability, you know, for this next wave of of applications that really take advantage of this decoupling of data. But having data flow into the app tied to me as opposed to having the APP have to know about my data ahead of time, >>Yeah, yeah, So that is very forward thinking. So I'll end with a prediction and a best practice. I am predicting that the organizations that really leverage external data, new data sources, not just whether or what have you and modernize those data flows will outperform the organizations that don't. And as a best practice to getting there, I the CDOs that own this have at least visibility into everything they're purchasing can save millions of dollars in duplicate spend. So, Thio, get their three key takeaways. Identify the leading indicators and market signals The data you need Thio. Better identify that. Consolidate those purchases and please explore the data sets the range of data sets data providers that we have on the thought spot. Atlas Marketplace Mallory over to you. >>Wow. Thank you. That was incredible. Thank you. To all of our Panelists for being here and sharing that wisdom. We really appreciate it. For those of you at home, stay close by. Our third session is coming right up and we'll be joined by our partner AWS and get to see how you can leverage the full power of your data cloud complete with the demo. Make sure to tune in to see you >>then

>>from around the globe. It's the Cube covering HP Discover Virtual Experience Brought to You by HP >>I Welcome to the Cube's coverage of HP Discover. 2020. The virtual experience I'm Stew Minimum course This year we're getting to talk to HP, their customers and their partners where they are around the globe. We said many times these were, you know, together, even while we're art happy to dig into a really important partnership with HP and VM Ware. Welcome to the program. First time guest on the program Christmas Satterthwaite. She is the vice president of product management for Compute with Packard Enterprise and welcome back to the program Lee Caswell. He is the vice president, product marketing for hyper converged infrastructure, her at VM Ware talking about V sphere and how that gets bundled into everything else. Chris Stanley, thanks so much for joining us. >>Thanks for having us. >>Alright, So, Chris, let's start with you. So you know, like a little bit about your background? The HP and HP relationship with VM Ware, you know, goes back to you know, the earliest days, but, you know, give us a little bit about you know where in the portfolio you focus on and and how VM Ware fit, then >>Oh, sure, sure. So I've been with H P E for 23 years now, and I'm leading the business for Alliance and Synergy and talking a little bit about the relationship with VM Ware. So we've been partnering for 19 years and we have over 200,000 joint customers together. And I'm actually often asked about the partnership and how we partner and we really partner across all fronts. So it's from the innovation for the co engineering, the working with specific customers on what solutions are good for them to servicing our customers. So we're really working across the board, and a lot of customers we work with closely are really impressed with how closely we're working together, because that's what they look for. >>Yeah, and we it's it's It's an interesting relationship to watch. Obviously, you know, long history Chris talked about on the it side, but the VM partnership is more than just the compute. Maybe gives a little bit of a view inside. You know, the joint engineering go to market efforts that you do. >>Yeah. I mean, customers always sit up straight when we talk together, because both hard companies or just raw engines of innovation and they look forward to not just the capabilities or bringing, but also the seamless way that we integrate that and make that seamless and easy for customers to digest. So certainly on the server front through V sphere, that's been a longstanding, uh, participation the VM Ware Cloud Foundation. Then this fully software defined stack became a really interesting way for us to go in partner and show joint value to customers who are trying to basically get more speed the speed. We're gonna talk about a lot that today and then finally, the confirmation that we've opened up into storage systems, right? So there's certainly a hyper converged element of it. But now what we do with Nimble three Par and now I'm Era is a really interesting way for us to take the vehicle technology that we have and extend the common operating model. So really just interesting innovation for customers that take advantage of as they look to innovate themselves. >>Krista, from from a research standpoint, you know, we were really early in watching, you know, new models of building out storage. And we said, You know, the pendulum has swung back to pull it much closer to the compute you talked about. You've got a broad portfolio and compute. You know, synergy has some really interesting, you know, ways to be able to compose things and leverage software capabilities. So maybe give >>us a >>little bit of how HP differentiates in the market cause, you know, VM Ware does partner with lots of people. But you know what separates the's point solution? Everything else out in the market? >>Sure, and synergy is a great example, because what we're seeing is really, really high interest on on synergy with VCF. And the reason for that is because customers want a software to find infrastructure that they can compose, compute storage and networking as they need to to address any workload they have. And they want to do that with a partner like VM Ware and VCF. So what we see is customers choosing those two things together and building their hybrid cloud environments on those two. When I think of some of the customers that we have, I'll give you a specific example. So Banco Santander's one of the largest banking groups in the world. And they are really trying to drive innovation across all of their, um, locations there in North America, South America, Europe, Asia. They're trying to drive innovation across. They have a big project, and they selected Synergy and VCF and as a service green lake bottle to help them transform their business. And they're really excited because what they think this is providing to them is a reduced a data center space, reduced power consumption and reduce costs. And all of that with automation, more automation than they've had in the past. More flexibility than they've had in the past. >>Yeah, I'm so glad you brought up the Green Lake because you know, those as a service models. You know, Cloud obviously has been a big discussion for the last two years, Lee, Um, you know, VM Ware is no stranger to, you know, working in multi and hybrid environments. It gives a little bit about you know what you're hearing from your customers. You know, if you meant Green Lake, how does that fit in the overall? You know, VM Ware multi cloud offering. >>Well, you know, we all know these air uncertain times, right? and customers and uncertain times. We're looking for flexibility. How do they go? And basically, you know, invest smartly, right? Look to come out of uncertain times stronger. And what we're finding is that the flexibility, you know, starting it. You know, we're really impressed with this energy platform, by the way, the idea of being able to flexibly, configure, compute and storage to tie into external arrays from that end, to have the VM Ware Cloud Foundation is a unifying, software defined data center concept that's available on Prem and then extends into the hybrid cloud. This basic gives investment protection to customers who are looking for how to invest in. You know, you mentioned Green Lake as well, and I just mentioned that innovation on Green Lake is about true consumption based purchasing miles, if you will. And that's different than just a financial engineering aspect. I mean, that's real innovation and real technical innovation in terms of how customers can go in a why infrastructure at the time that they needed relative to that compelling business models, >>and I'll chime in their Teoh, I'll tell you a little story about when I first presented the green like model. At that time, it wasn't called Green Lake, but I presented it to a bunch of customers, about 100 customers in an advisory council. And I have never had so many people come up to me afterwards trying to figure out how they can get that for themselves as I did when I had that presentation. What really resonated with people is that they wanted to take advantage of the latest and greatest technologies, but they didn't have big budgets. And when they did take advantage of those technologies, one of the challenges has been growth. So when they need to expand, that's another procurement cycle. You have a way to have the standard all love with Green Lake. You actually have that added capacity on sites and then also painful what you use s so they were attracted to all of those things. And I feel like right now and the environment were in many people had big, big projects, things they want to do, and they may have planned those ah, a capital expenditure for that. But that money may not be there, So Green Lake is one of those things that can help overcome that challenge. And what we found is when people use green like we don't see many people. Um, go back. So what? I was talking to the green like team, and I said, You know what happens if they decide not to do Green Lake and they're kind of pause, and they're like, Well, we really haven't run into that very often. So it's very, very popular, and customers were really happy with it. >>Yeah. Talking about innovation and helping customers take advantage of new technologies. You know, maybe we'll start with you and Krista. Definitely want your but been a lot of feedback about V. Sphere seven. Of course, One of the big pieces of that is how, you know, cloud native container ization kubernetes It can be pulled into the, you know, the virtualization platform. So we're talking a lot about vcf Lee. That's the you know. Wait. Get it. The community's piece today. Tell us a little bit about that and what you hear from customers. And then Chris, I'd like to understand how that fits into the HP offering. >>Yeah, you know, the data we have shows that 95% of new applications are being developed on containers. Why? Because it's the speed of ill. And so at VM Ware, we've re architected V sphere for the first time that, you know in the last five years. And you look carefully at what the EMR integrates into the hyper visor because that's what we believe is going to be really benefiting from performance efficiency and management. And so we've integrated kubernetes directly into the hyper visor itself and then to our Tom's, a portfolio. Introduce an upstream compatible kubernetes development environment so that we have developer ready infrastructure. And that's really important because at the speed of new applications, basically you need to be able to respond quickly to those and what VM Ware has always offered right, which is a resilient underlying infrastructure with an intrinsic security model built in conceptually important when containers are being spun up more quickly. All right, mark quickly. They're being portable and portable across the hybrid cloud. Those models right mean that you need and convince you get value right from this integrated model that leverages all of the experience and knowledge that people have around how to run V Center and V Sphere so really exciting, and it's available in VCF for with >>I actually see the interest. I see customers asking about an enquiring about it. Vikan, you know, definitely second everything that we just said. I think you're really you're going to see a really fast transition over because there's so much value. Add it in. >>Excellent. Okay, Crystal, while I've got you on the compute piece, you know, legally said that 95% of application new applications are being built on container ization. How has that impacted architecture, er and how you're working with? >>Yeah. So what I find is that customers are very interested in containers. What we're doing is we're helping them from a services standpoint. A consulting standpoint of many of these customers are adopting for the first time trying to figure out how they could they could leverage containers in their environment. From our standpoint, it's making sure that we have the right platforms and we're advising and consulting and helping customers get there. >>Excellent, Lee. You know, Kristen talked about a sense and under one wondering if you've got any customer examples you like to share? >>Yeah. Great. One is ah, portion. I love the portion example. Just because portion, just the epitome of speed. And so the idea of this flexibility well, you're finding rate is the flexibility, right? Starting from, let's say, from a synergy, I'm flexible on the part of their allocation, right? And then, with VCF right now being able to be flexible across the hybrid cloud and now with VCF or with ponzu, the flexibility of introducing new modern applications support on Finally Layer and Green Lake On top of that which which is also using it, gives you this idea that you know, especially in uncertain times. But, you know, regardless, the changing business environment where everyone's responding, toe app, development rushers, timelines and innovation. We've got a really interesting model now for customers to invest responsibly and be able to respond quickly. >>Hm. Excellent. Crystal, I guess. Said the other pieces were at discover any updates on the portfolio expanding the VM solution. That >>Yeah. Yeah. So I'd like to talk a little bit about our pre validated synergy vcf solution stack with built in automation. So we literally got rid of hundreds of that's pre and post employment so we could speed deployment by five times. We were talking to point in hours instead of weeks. So we're really, really excited about that. We're working together to make sure we're making things easier for customers making that journey to a hybrid cloud very, very simple. So we're really happy to have, you know, offer that to customers. >>Great Lee, Any any final words you can share on the partnership? You >>know what I might say? It's right that the pace of innovation from our companies right is so great, Right? That really v vm Ware Cloud Foundation is a way, you know, in our joint effort and joint delivery rate is a way for customers to assimilate all of this innovation. So that day zero, it's guaranteed the work. And that day two, you can lifecycle manage all the individual components from a common sec manager interface. That's the value that we're bringing together today. Is that Listen, you know, putting all this in place conceived, daunting until the VM Ware Cloud Foundation, with synergy with all of the joint value we have basically makes it manageable so that you can go and basically stop looking down it infrastructure. Look up the ass. >>All right. Christine will let you have the final word and final takeaways from HP Discover. >>Okay, sure. Thanks. Together. What we're trying to do is simplify that journey to hybrid cloud. Make sure that customers can innovate faster, provide stable operations and reduce their costs. >>Well, Chris Stanley, thank you so much for joining us. Congratulations on the progress. Looking forward. Toa watching down the road. >>All right, thanks. >>Alright, Stay tuned for lots more coverage from the Cube, HP Discover 2020. Virtual experience on stew Minimum. Thanks for watching. Yeah, >>yeah, yeah, yeah.

from our studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi and welcome to the cube Studios for another cube conversation where we go in-depth with thought leaders driving innovation across the tech industry I'm your host Peter Burris if there's one thing we know about cloud it's that it's going to drive new data and a lot of it and that places a lot of load on storage technologies who have to be able to capture persist and ultimately deliver that data to new classes of applications in support of whatever the digital business is trying to do so how is the whole storage industry and the relationship between data and storage going to evolve I can't think of a better person to have that conversation with in stanley's a phos senior vice president product marketing infinite dad Stan welcome to the cube thank you for it's my pleasure to be here and I'm flattered with that introduction well hold on look you and I have known each other for a long time we have been walking into user presentations and you've been walking out until recently though you were generally regarded as the thought leader when it came to user side concerns about storage what is that problem that users are fundamentally focused on as they think about their data data management and storage requirements fundamental problems and this afflicts all classes of users whether in a financial institution at university government small business medium-sized businesses is that they're coping with the number of primal forces that don't change and the first is that the environment is becoming ever more competitive and with the environment being ever more competitive that means that they're always under budget constraints they're usually suffering from skill shortages especially now when we see so many new technologies and the realization that we can coax value out of the information that we capture and store creating new demands elsewhere within the IT organization so what we see historically is that uses understand that there you have an insatiable demand for capacity they have finite budgets they have limited skills and they realize that recovering from a loss of data integrity is a far more painful process than recovering from an application blowing up or a networking issue and they got to do it faster and they have to do it faster so what we see in some ways is in effect the perfect storm and this is part of the reason that we've seen a number of the technical evolutions that we've witnessed over the past decade or two decades or however long we'd like to admit we've been tracking this industry occurring and growing in importance what we've also seen is that many of the technologies that are useful in helping to deliver usable availability to the application are in some ways becoming more commoditized so when we look across these industries some of the things that we're looking for is cost efficiency we're looking at increasing levels of automation we're looking of increases in data mobility with the ultimate objective being of course to allow data to reside where it naturally belongs and we're trying to deliver these new capabilities at scale in infrastructures that were built with storage arrays that would design for a terabyte world instead of a petabyte world and it won't be too long before we start talking about exabytes as we're already seeing so to be able to satisfy new scale problems with traditional and well understood issues is there are three basic types of storage companies that are targeting this problem the first of the established storage companies the incumbents the incumbents and the incumbents I really don't envy them because they have to maintain backwards compatibility which limits their ability to innovate at the same time they're competing against privately held newer companies that aren't constrained by the need for backwards compatibility and therefore able to take better advantage of the technology improvements that we're seeing to live it and when I say technology improvements not just in hardware but also in terms of software also in terms of management and government and governing philosophies so beginning with the point that all companies large small have some basic problems that are similar what we then see is there are three types of storage companies addressing them one of the in established and common vendors the other and they've gotten a lot of press or the companies that realize that flash media very media that delivers one to two orders of magnitude improvements in terms of performance in terms of bandwidth in terms of environmental x' that they could create storage solutions that address real pain points within a data center within an organization but at a very high price point and then it was the third approach and this is the approach that infinite I chose to take and that is to define the customer problem to find the customer market and then create an architecture which is underpinned by brilliant software to solve these problems in a way that is both cost-effective and extensible and of course meeting all of the critical capabilities that users are looking for so we've got the situation where we've got the incumbents who have install bases and are trying to bring their customers forward but right I have to do so within the constraints of past technology choices we've got the new folks who are basically technology first and saying jump to a new innovation curve and we've got other companies that are trying to bring the best of the technology to the best of the customer reality and marry it and you're asserting that's what infinite at ease and then it's precisely what we've done so let's talk about why did you then come to infinite at what is it about infinite act that gets you excited well one of the things that got well your number of things that got me excited about it so the first is that when I look at this and I approach these things as an engineer who's steeped in aerospace and weapon systems design so you look at the problem you superimpose capabilities there and then you blow it up and then if well we do blow it up but we blow it up using economics we blow it up using superior post-sale support effectiveness we blow it up with a fundamentally different approach to how we give our install base access to new capabilities so we're established storage companies and to some extent media based storage companies of forcing upgrades to avoid architectural obsolescence that is to gain access to new features and functions that can improve their staff productivity or deliver new capabilities to support new applications and workloads we're not forcing a cadence of infrastructure refreshes to gain access to that so if you take a look at our history our past behavior we allow today we're allowing current software to run on n minus 2 generation hardware so that now when you're doing a refresh on your hardware you're doing a refresh on the hardware because you've outgrown it because it's so old that it's moved past its useful service life which hasn't happened to us yet because that's usually on the order of about eight years and sometimes longer if it's kept in a clean data center and we have a steady cadence of product announcements and we understood some underlying economics so whether I talk to banking institutions colleges manufacturing companies telcos service providers everybody's in general agreement that roughly two-thirds of the data that they have online and accessible is stale data meaning that it hasn't been accessed in 60 to 90 days and then when I take a look at industry forecasts in terms of dollar per terabyte pricing for HD DS for disk drives and I look at dollar per terabyte forecast for flash technologies there's an order of magnitude difference in meaning 10x and even if you want to be a pessimist call it only 5x what you see is that we have a built-in advantage for storing 60% of the data that's already up and spinning and there are those questions of whether or not the availability of flash is going to come under pressure over the next few years as because we're not expanding another fabs out there they're generating flash so let me come back right it's kind of core points out there so we have quality yeah the right now you guys are trying to bring the economics of HDD to the challenges are faster more reliable more scalable data delivery right so that you can think about not only persisting your data from transactional applications but also delivering that data to the new uses new requirements new applications new business needs so you've made you know infinite out has made some choices about how to bring technology together that are some somewhat that are unique first thing is the team that did this tell us a little bit about the team and then let's talk about some of those torches so one of the draws for me personally is that we have a development team that has had the unique possibly the unique experience of having done three not one not two but three clean sheet designs of storage arrays now if you believe that practice makes perfect and you're starting off with very bright people that experience before they designed a storage array when we look at the InfiniBand when we look at in Finnegan what we see is the benefit of three clean sheet designs and what does that design look like what is it how did you guys bring these different senses of technology together to improve the notion of it all right so what we looked at we looked at trends instead of being married to a technology or married to an architecture we were we define the users problem we understood that they have an insatiable need for data we can argue whether they're growing at fifteen percent 30 percent or 100 percent per year but data growth is insatiable stale data being a constant megive n' and of course now with digital business initiatives and moving the infrastructure to the edge where we could capture ever more data if anything the amount of stale data that was storing is likely to increase so we've all seen survey after survey that 80% of all the data created is unstructured data meaning we're collecting it we know that may be a value at some point but we're not quite sure when so this is not data that you want to store in the most expensive media that we know how to manufacture or sell right not happening so we have a built-in economic advantage for this at least 60% of the data that users want to keep online we understand that if you implement an archiving solution that archive data still has to be stored somewhere and for practical purposes that's either disk or tape and we're not here to talk about the fact that I can take tape and store in a bunker for years but if I want to recover something if I have to answer a problem I want it on disk so the economic gap the price Delta between an archive storage solution per se and our approach is much narrower because we're using a common technology and when Seagate or West and digital a Toshiba cell and HDD they're not asking you where you're putting it they're saying you want this capacity this rpm this mean time between face its this is how much it's going to cost so when we take a look at a lot of the innovation and go to market models what they really are or revenue protection schemes for the existing established vendors and for the emerging companies the difference is there are in the problems that they're solving am i creating a backup restore solution the backup and restore is always a high impact pain point am i creating a backup restore solution am i building a system for primary storage a my targeting virtualized environments my targeting VDI now our install base the bulk of our install base I'm not sure we actually we should share percentages but it's well north of 50 and if you take a look at some virtualized estimates probably 80% of workloads today are virtualized we understood that to satisfy this environment and to have a built-in advantage that's memorable after the marketing presentations are done in other words treating these things as black boxes so if we take a look at my high-level description of an infinite box array installed at a customer site consistent sub-millisecond response times and we're able to do that because we service over 80% of all iOS out of DRAM which is probably about four orders of magnitude faster than NAND flash and then we have a large read cache to increase our cache hit ratio even further and when I say large we're not talking about single digits of terabytes we're talking about 20 plus terabytes and that can grow as necessary so that when we're done we're achieving cache hit ratios that are typically in excess of 90% now if I'm servicing iOS out of cache do I really care what's on the back end the answer is no but what I do care about for certain analytics applications is I want lots of bandwidth and I want and if I have workloads with high right content I don't want to be spending a lot of time paying my raid right penalty so what we've done is to take the obvious solution and coalesce rights so that instead of doing partial stripe rights we're always doing full stripe rights so we have double bit protection on data stored on HD DS which means that the world is likely to come to an end before we lose this slight exaggeration I think we're expecting the world to come to an end in 14 billion years yeah yeah let's do so so if I'm wrong get back to me in a Bay and it's a little bit less than that but it doesn't matter yeah okay high on that all so we've got a so we've got a built in economic advantage we've got a built in performance advantage because when I'm servicing most iOS out of DRAM which is for does magnitude faster than NAND flash I've got a lot of room to do a lot of very clever things in terms of metadata and still be faster so and you got a team that's done it before and we've got a team that's done it before and experimented because remember this is a team that has experience with scale-up architectures as in symmetric s-- they have experience with scale-out architectures which is XIV which was very disruptive to the market well so was it symmetric spec and now of course we've got this third bite at the Apple with infinite at where they also understood that the rate of microprocessor performance improvement was going up a lot faster than than our ability to transfer data on and off of HD DS or SSDs so what they realized is that they could change the ratio they can have a much lower microprocessor or controller to back-end storage ratio and still be able to deliver this tremendous performance and now if you have fewer parts and you're not affecting the ID MTBF by driving more iOS through I've lowered my overall cost of goods so now I've got an advantage in back-end media I have a bag I have an advantage in terms of the number of controllers I need to deliver sub sillas eken response time I have an advantage in terms of delivering usable availability so I'm now in a position to be able to unashamedly compete on price unashamedly compete on performance unashamedly compete on a better post sale support experience because remember if there's less stuff they had a break we're taking less calls and because of the way we're organized our support generally goes to what other vendors might think of it's third level support because of a guided answer answers the phone from us doesn't solve the problem he's calling development so if you take a look at gotten apear insights we're off the scale in terms of having great reviews and when you have I think it's 99% I may be off by a percent ninety eight to a hundred percent of our customers saying they'd recommend our kit to their to their peers that's a pretty positive endorsement yeah so let me let me break in and and kind of wrap up a little bit let me make this quick observation because the other thing that you guys have done is you've demonstrated that you're not bound to a single technology so smart people with a great architecture that's capable of utilizing any technology to serve a customer problem at a price point that reflects the value of the problem that's being solved right and in fact we it's very insightful observation because when you recognize that we've built a multimedia integrated architecture that makes our that makes very easy for us to include storage class memory and because of the way we've done our drivers we're also going to be nvme over if ready when that starts to gain traction as well excellent Stanley Zappos senior vice president product management Infini debt thanks very much for being in the cube we'll have you back oh it's my pleasure there's been a blast and once again I want to thank you for joining us for another cube conversation on Peterborough's see you next time [Music]

>>Live from Miami, Florida. It's the cube covering IBM's data and AI forum brought to you by IBM. >>Welcome everybody to the port of Miami. My name is Dave Vellante and you're watching the cube, the leader in live tech coverage. We go out to the events, we extract the signal from the noise and we're here at the IBM data and AI form. The hashtag is data AI forum. This is IBM's. It's formerly known as the, uh, IBM analytics university. It's a combination of learning peer network and really the focus is on AI and data. And there are about 1700 people here up from, Oh, about half of that last year, uh, when it was the IBM, uh, analytics university, about 600 customers, a few hundred partners. There's press here, there's, there's analysts, and of course the cube is covering this event. We'll be here for one day, 128 hands-on sessions or ER or sessions, 35 hands on labs. As I say, a lot of learning, a lot of technical discussions, a lot of best practices. >>What's happening here. For decades, our industry has marched to the cadence of Moore's law. The idea that you could double the processor performance every 18 months, doubling the number of transistors, you know, within, uh, the footprint that's no longer what's driving innovation in the it and technology industry today. It's a combination of data with machine intelligence applied to that data and cloud. So data we've been collecting data, we've always talked about all this data that we've collected and over the past 10 years with the advent of lower costs, warehousing technologies in file stores like Hadoop, um, with activity going on at the edge with new databases and lower cost data stores that can handle unstructured data as well as structured data. We've amassed this huge amount of, of data that's growing at a, at a nonlinear rate. It's, you know, this, the curve is steepening is exponential. >>So there's all this data and then applying machine intelligence or artificial intelligence with machine learning to that data is the sort of blending of a new cocktail. And then the third piece of that third leg of that stool is the cloud. Why is the cloud important? Well, it's important for several reasons. One is that's where a lot of the data lives too. It's where agility lives. So cloud, cloud, native of dev ops, and being able to spin up infrastructure as code really started in the cloud and it's sort of seeping to to on prem, slowly and hybrid and multi-cloud, ACC architectures. But cloud gives you not only that data access, not only the agility, but also scale, global scale. So you can test things out very cheaply. You can experiment very cheaply with cloud and data and AI. And then once your POC is set and you know it's going to give you business value and the business outcomes you want, you can then scale it globally. >>And that's really what what cloud brings. So this forum here today where the big keynotes, uh, Rob Thomas kicked it off. He uh, uh, actually take that back. A gentleman named Ray Zahab, he's an adventure and ultra marathon or kicked it off. This Jude one time ran 4,500 miles in 111 days with two ultra marathon or colleagues. Um, they had no days off. They traveled through six countries, they traversed Africa, the continent, and he took two showers in a 111 days. And his whole mission is really talking about the power of human beings, uh, and, and the will of humans to really rise above any challenge would with no limits. So that was the sort of theme that, that was set for. This, the, the tone that was set for this conference that Rob Thomas came in and invoked the metaphor of superheroes and superpowers of course, AI and data being two of those three superpowers that I talked about in addition to cloud. >>So Rob talked about, uh, eliminating the good to find the great, he talked about some of the experiences with Disney's ward. Uh, ward Kimball and Stanley, uh, ward Kimball went to, uh, uh, Walt Disney with this amazing animation. And Walter said, I love it. It was so funny. It was so beautiful, was so amazing. Your work 283 days on this. I'm cutting it out. So Rob talked about cutting out the good to find, uh, the great, um, also talking about AI is penetrated only about four to 10% within organizations. Why is that? Why is it so low? He said there are three things that are blockers. They're there. One is data and he specifically is referring to data quality. The second is trust and the third is skillsets. So he then talked about, you know, of course dovetailed a bunch of IBM products and capabilities, uh, into, you know, those, those blockers, those challenges. >>He talked about two in particular, IBM cloud pack for data, which is this way to sort of virtualize data across different clouds and on prem and hybrid and and basically being able to pull different data stores in, virtualize it, combine join data and be able to act on it and apply a machine learning and AI to it. And then auto AI a way to basically machine intelligence for artificial intelligence. In other words, AI for AI. What's an example? How do I choose the right algorithm and that's the best fit for the use case that I'm using. Let machines do that. They've got experience and they can have models that are trained to actually get the best fit. So we talked about that, talked about a customer, a panel, a Miami Dade County, a Wunderman Thompson, and the standard bank of South Africa. These are incumbents that are using a machine intelligence and AI to actually try to super supercharge their business. We heard a use case with the Royal bank of Scotland, uh, basically applying AI and driving their net promoter score. So we'll talk some more about that. Um, and we're going to be here all day today, uh, interviewing executives, uh, from, uh, from IBM, talking about, you know, what customers are doing with a, uh, getting the feedback from the analysts. So this is what we do. Keep it right there, buddy. We're in Miami all day long. This is Dave Olanta. You're watching the cube. We'll be right back right after this short break..

>>Live from Las Vegas. It's the cube covering UI path forward Americas 2019 brought to you by UI path. >>We're back in Las Vegas. UI path forward three. You're watching the cube, the leader in live tech coverage. Bobby Patrick is here. He's the COO of UI path. Welcome. Hi Dave. Good to see it to be here. Wow. Great to have the cube here again. Right? Q loves these hot shows like this. I mean this is, you've said Gardner hasn't done the fastest growing software segment you've seen in the data that we share from ETR. You guys are off the chart in terms of net score. It's happening. I hanging onto the rocket ship. How's it feel? Well it's crazy. I mean it's great. You all have seen some of the growth along the way too, right? I mean we had our first forward event less than two years ago and you know about 500 plus plus non UI path and people then go year later. It was Miami USY. >>There's probably a lot. Cube I think was Miami right yet and a, and that was a great event, but that was more in the 13 1400 range. This one's almost 3000 and the most amazing part about it was we had 8% attrition from the registrations. Yeah. That's never seen that we're averaging 18% of 20% for all of our, most of our events worldwide. But 8% the commitment is unbelievable. Even 18 to to 20% is very good. I mean normally you'll see 25 to sometimes as high as 50% yeah. It just underscores the heat. >> Well I think what's also great, other stats that you might find interesting. So over 50% of the attendees here are exec. Our senior executives, like for the first time we actually had S you know, C level executive CHRs and CEOs on stage. Right. You could feel the interest level. Now of course we want RPA developers at events too, right? >>But this show really does speak, I think to the bigger value propositions and the bigger business transformation opportunity from RPA. And I mean, you've come so far where no one knew RPA two years ago to the CIO of Morgan Stanley on stage, just warning raving about it. That's, we've come a long way in two years. >> Well, and I saw a lot of the banks here hovering around, you know, knocking on your door so they, they know they are like heat seeking missiles, you know, so, but the growth has been amazing. I mean I think ARR in 2017 was what, 25 million at this time. Uh, at the end of 17 it was 43 and 43 and 25 and now you're at 12 times higher now 1212 X solve X growth, which is the fastest growing software company. I think in that we know from one to 100 we were, we did that in 21 months and all that. >>And we had banks who now we're not really counting anymore and we're kind of, you know, now focus more on customer expansion. Even though we hit 5,000 customers, which we started the year at 2050 ish. We just crossed 5,000. I mean, so the number of customers is great, but there's no question. This conference is focused on scaling, helping them grow at enterprise wide with, with, with RPA. So I think our focus will be in to shift a bit, you know, to really customer expansion. Uh, and that's a lot of what this announcements, the product announcements were about a lot of what the theme here is about. We had four dozen customers on, on stage, you know, the Uber's of the world, the Amazons of the world. It's all about how they've been scaling. So that's the story now. Well, you know, we do a lot of these events and I go back to some of the, uh, when the cube first started, companies like Tablo, Dallas Blunck great service. >>Now, I mean, these you can, and when you talk to customers, first of all, it's easy to get customers to come talk about RPA. Yeah. And they're, they're all saying the same thing. I mean, Jeanne younger said she's never been more excited in her career from security benefit. But the thing is, Bobby, it's, I feel like they're, they're really just getting started. Yeah. I mean most of the use cases that you see are again, automating mundane task. We had one which was the American fidelity, which is a really bringing in AI. Right. But they're really just getting started. It's like one to 3% penetration. So what are your thoughts on that to kind of land and expand, if you will? I think, you know, look, last year we announced our vision of a robot for every person. At that point we had SNBC on stage and they were the one behind it. >>And they are an amazing story. Now we have a dozen or so that are onstage talking about a robot for every person like st and others. And so, but that, that, that's a pretty, pretty, pretty bold vision I think. Look, I think it's important to look at it both ways. Um, there's huge gold and applying RPA to solve real problems. There's a big opportunity, enterprise wide, no question. We've got that. But I look New York Foundling was on stage yesterday. We have New York Foundling is a 150 year old associate. Our charity in New York focused on child welfare, started by three fishers of charity. They focused on infants. And anyway, it's an amazing firm. Just the passion that New York family had on stage with Daniel yesterday was amazing. But what they flew here because for once they found a technology that actually makes a huge difference for them and what in their mission. >>So their first RPA operation was they have 850 clinicians every week. They spend four hours a week moving their contact, uh, a new contact data associate with child child issues from system to system to spreadsheet and paper to system, right? They use RPA and they now say for a 200,000 hours a year. But more importantly, those clinicians spend those four hours every week with children not moving. So I'm still taking, I think Daniel had a bit of a tear in his eye, hearing them talk about it on stage, but I'm still taken by, by the, by the sheer massive opportunity for RPA in, in a particular to solve some really amazing things. Now on a mass scale, a company can drive, you know, 10, 15, 20% productivity by every employee having a robot. Yes, that's true on a mass scale. They can completely transform their business, your transform customer experience, transform the workplace on a mass scale. >>And that, that is, that's a sea level GFC level goal and that's a big deal. But I love the stories that are very real. Um, and, and I think those are important to still do plug some great tech for good story. Look, tech gives, you know, the whole Facebook stuff and the fake news got beat up and it had Benny come out recently say, Hey, it's, it's not just about increasing the value to shareholders, you know, it's about tech for good and doing other things affecting lifestyle's life changing. And Michael Dell is another one. Now I've, I've, I've kind of said tongue in cheek, you know, show me the CEO misses is four quarters in a row and see if that holds up. But nonetheless, you love to see successful companies giving back. It seems to be, it's part of your, well look I've been part of hardware companies and I met you all through a few of them and others they have good noble causes but it was hard to really connect the dots. >>Yes there CPS underneath a number of these things. But I think judging by the emotional connection that these customers have on stage, right and these are the Walmarts and Uber's and others in the world judging by the employee and job satisfaction that they talk about the benefits there. I just, I my career, I have not seen that kind of real direct impact from you know, from B2B software for example on the lives of people both everyday at work but also just solving the solving, you know, help accelerate human achievement. Right. And so many amazing ways. We had the CEO of the U N I T shared services group on stage yesterday and they have a real challenge with, you know, with the growth of refugees worldwide and he would express them and they can't hit keep up. They don't have the funding, which is, you know, with everybody and, and Trump and others trying to hold back money. >>But they had this massive charter for of good, the only way they get there is through digital. The new CEO, the new head of the U N is a technology engineer. He came in and said, the way we solve this is with templates, with technology. And they decided, they said on stage yesterday that RPA and RPA has the path to AI and the greater, the greater new technologies and that's how they're going to do it. And it's just a, it's a really, it's, I think it's, it feels really great. You know, it's funny too, one of the things we've been talking about this week is people might be somewhat surprised that there's so much head room left for automation because the boy, 50 years of tech, Kevin, we automated everything. That's the other, but, and Daniel put forth the premise last night, it actually, technology is created more process problems or inefficiencies. >>So it's almost like tech has created this new problem. Can tech get us out of the problem? Well, essentially you think about all the applications we use in our lives, right? Um, you know, although people do have, you know, a Salesforce stack and sometimes in this SAP, the reality is they have a mix of a bunch of systems and then we add Slack to it and we add other tools and we add all the tools alone, have some great value. But from a process perspective of how we work everyday, right? How a business user might work at a call center, they have to interact then. And the reality is they're often interacting with old systems too because moving them is not easy, right? So now you've got old systems, new systems and, and really the only way to do that is to put a layer on top of the systems of engagement and the systems of record, right? >>A layer on top that's easy to actually build an application that goes between all of these different, these different applications, outlook, Excel, legacy systems and salesforce.com and so on and so on and, and build an app that solves a real problem, have it have outcomes quickly. And this is why, Dave, we unveiled the vision here that we believe that automation is the application. And when you begin to think about I could solve a problem now without requiring a bunch of it engineers who already are maxed out, right? Uh, I can solve a problem that can directly impact the businesses or directly impact customers. And I can do that on top of these old technologies by just dragging and dropping and using a designer tool like studio or studio X in a business user can do that. That's, that's a game changer. I think what's amazing is when you go to talk to a CIO who says, I've been automating for 20 years, you know, take up the ROI. >>Once they realize this is different, the light bulb goes off. We call it the automation first mindset. A light bulb goes off and you realize, okay, this is a very different whole different way of creating value for, for an organization. I think about how people weigh the way that people work today. You're constantly context switching. You're in different systems. Like you said, Slack, you're getting texts and you want to be responsive. You want to be real time. I know Jeff Frick who was the GM of the cube has got two giant screens right on his desk. I myself, I always have 1520 tabs open if I go, Oh you got so many tabs on my, yeah. Cause I'm constantly context switching, pulling things out of email, going back and forth and so and so. I'm starting to grok this notion of the automation is the app. >>At first I thought, okay, it's the killer app, but it's not about stitching things together with through API APIs. It's really about bringing an automation perspective across the organization. We heard it from Pepsi yesterday. Yeah, right. Sort of the fabric, the automation fabric throughout the organization. Now that's aspirational for most companies today, but that really is the vision. Well, I think you had Layla from Coca-Cola also on, right. And her V their vision there and they actually took the CDO role of the CIO and put them together. And they're realizing now that that transformation is driven by this new way of thinking. Yeah, I think, you know, look, we introduced a whole set of new brand new products and capabilities around scaling around helping build these applications quicker. I, I think, you know, fast forward one year from now, the, you know, the vision we outlined will be very obvious the way people interact with, you know, via UI path to build applications, assault come, the speed to the operate will be transformational and, and so, you know, and you see this conference hear me walk around. >>I mean you saw last year in the year before you see the year before, but it's, it's a whole, the speed at which we're evolving here, I think it's unprecedented. And so I'll talk a little bit about the market for has Crigler killer was awesome this morning. He really knows his stuff now. Last year I saw some data from him and said the market by 2020 4 billion, and I said, no way. It's going to be much larger than that. Gonna be 10 billion by 2020 I did Dave Volante fork, Becca napkin by old IDC day forecast. Now what he, what he showed today is data. It actually was 10 billion by 2020 because he was including services, the services, which is what I was including in my number as well, but the of it, which was so good for him now, but the only thing is he had this kind of linear growth and that's not how these rocket ship Marcus grow. >>They're more like an old guy for an S curve. You're going to get some steep part now, so I'd love to see like a longer term forecast because that it feels like that's how this is going to evolve. Right now it's like you've seated the base and you can just feel the momentum building and then I would expect you're going to see massive steep sort of exponential growth. Steeper. There may be, you know, nonlinear because that's how these markets go >> to come from the expansion potential, right? And none of our customers are more than 1% audit automated from an RPA perspective. So that shows you the massive opportunity. But back to the market site, data size, Craig and I and the other analysts, we talk often about this. I think the Tam views are very low and you'll look at our market share, let's just get some real data out there, right? >>Our market share in 2017 was 5% let's use Craig's linear data for now. You know, our market share this year is over 20% our market share applying, and I don't want to give the exact numbers as you don't provide guidance anymore, is substantially we're substantially gaining share now. I believe that's the reality of the market. I think because we know blue prisms numbers, we go four times faster than the every quarter automation. The world won't share their numbers. But you know, I can make some guesses, but either way I think, you know, I think we're gaining share on them significantly. I think, you know, Craig's not gonna want us to be 50% of the market two years, he's just not. And so he's going to have to figure out how to identify how to think. That brought more broadly about, about that market trend. He talked about it on stage today about how does he calculate the AI impact and the other pieces now the process mining now that now that we are integrating process mining into RPA, right? >>It's strategic component of that. How does that also involve the market? So I think you have both the expansion and the plot product portfolio, which drives it. And then you have the fact that customers are going to add more automations at faster pace and more robots and that's where the expansion really kicks in. And we often say, you know, look as a, as a, as a, as a company that, you know, one day we'll be public company, our ARR numbers. Very important. We do openly transparently share that. But you know, the other big metric will be, you know, dollar based net expansion rate that shows really how customers are expanding. I think that, I know it, our numbers, we haven't shared it yet. I know all the SAS companies, the top 10 I can tell you, you know we're higher than all of them. >>The market projections are low. And I think he knows it well. >> Speaking of Tam, and when we, I saw this with, with service now, now service now the core was it right? So the, the ROI was not as obvious with, with, with you guys, you're touching business process. And so, so in David Flory are way, way back, did an analysis of service and now he said, wow, the Tam is way being way under counted by everybody. That wall street analyst Gardner, it feels like the same here because there are so many adjacencies and just talk to the customers and you're seeing that the Tam could be enormous, much bigger than the whatever 16 billion a Daniel show, the other Danielson tangles, the guy's balls. He said, Oh that's 16 billion. That's you. I pass this data. And you know, we laugh, but I'm, I'm like listening. Say I wonder if he's serious cause this guy thinks big. >>I mean, who would've thought that he'd be at this point by now? And you're just getting started? Well, I think, you know, one thing I think is, you know, we're, we're, you know, we were a little bit kind of over a little less humble when we talked about things like valuation over the last few years. We were trying to show this market's real, you know, we want to now focus more on outcomes and things get a little less from around those numbers. And I think that shows the evolution of a company's maturity, um, that we, I think we're going through right now. Uh, you know, the outcomes of, you know, Walmart on stage saying, you know, their first robot that was, this was, this was two years ago, delivered 360,000 hours of capacity for them in, in, in, in, in HR, right? That, you know, I think those, that's where we're gonna be focused because the reality is if we can deliver these big outcomes and continue them and we can go company-wide deliver on the robot for every, every, every, every person, then you know, the numbers follow along with it. >>Well we saw some M and a this week as well, which again leads me to the larger Tam cause we had PD on, um, with Rudy and you can start to see how, okay now we're going to actually move into that vision that the guy from PepsiCo laid out this, this fabric of this automation fabric across the organization. So M and a is, is a part of that as well. That starts to open up new Tam. Opportunity does. And I think, you know, a process mind is a great example of a market that is pretty well known in Europe, not so much in the U S um, and there are really only a few players in that, in that market today. Look, we're going to do what we did in RPA. We're going to do the same thing. You're process mining. We're going to just say anything we're doing in it, not as democratization, you'll our strategy will be to go mass market with these technologies, make it very easy for accessibility for every single person in the case of process mining, every business analyst to be able to mind their processes for them and, and ultimately that flows through to drive faster implementations and then faster, faster outcomes. >>I think our approach, again, our approach to the business users, our approach to democratization, um, you know it's very different than our competitors. A lot of these low code companies, I won't name a number cause I don't remember our partners here at our conference. They're IT-focused their services heavy and, and you know, their growth rates I'll be at okay are 30% year over year in this market. That shouldn't be the case at all. I mean we're a 200 plus a year. We are still and we've got big numbers and we have a whole different approach to the market. I don't think people have figured it out yet, Dave. Exactly, exactly. The strategy behind which is, which is when you have business users, subject matter experts and citizen developers that can access our technology and build automations quickly and deliver value proof for their company. And you do that in mass scale. >>Right. And then you will now allow with our apps for your end users, I get a call center to engage with a robot as part of their daily operation that none of the other it vendors who are all kind of conventional thinking and that's not, our models are very different, which I think shows in our numbers and and, and the growth rates. Yeah. Well you bet on simplicity early on. In fact, when you join you iPad, you challenged me so you have some of your Wiki bond analysts go out. I remember head download our stuff and then try to download the competitors and they'll tell us, you know how easy it as well we were able to download UI path. We, we built some simple automations. We couldn't get ahold of the other other, other companies products we tried. We were told we'll go to the reseller or how much did you have to spend and okay so you bet on simplicity, which was interesting because Daniel last night kind of admitted, look, he tracked the audience. >>He said thank you for taking a chance on us because frankly a couple of years ago this wasn't fully baked right and and so, so I want to talk about last, the last topic is sort of one of the things Craig talked about was consolidation and I've been saying that all week and said this, this market is going to consolidate. You guys are a leader now you've got to get escape velocity cause the leader makes a lot of money and becomes, gets big. The number two does. Okay, number three man, everybody else and the big guys are starting to jump in as well. You saw SAP, you know, makes an announcement and you guys are specialists and so your thoughts on hitting escape velocity, I wouldn't say you're quite there yet. I want to see more on the ecosystem. There's maybe, who knows, maybe there's an IPO coming. I've predicted that there is, but your thoughts on achieving escape velocity and some of the metrics around there, whether it's customer adoption penetration, what are your thoughts? >>Yeah, I mean we definitely don't have a timetable on an IPO, but we have investors, public investors and VCs that at some point are going to want, this is the reality of how, of how it works. Right. Um, you know, I think the, uh, you know, I think the numbers to focus right now are on around, you know, customer outcomes. I think the ecosystem is a good one. Right? You know, we have, I'd say the biggest ecosystem for us to date has been the SAP ecosystem. When we look at our advisory board members, for others, that's really where, where the action is. Supply chain management, ERP, you know, certainly CRM and others, we don't have a view that, so our competitors have, but we have chosen not to take money from our, from ecosystem companies because we don't, our customers here are building processes, all the automation across ecosystems. >>Right? So you know, we don't want to go bet on say just one like Salesforce or Workday. We want to help them across all the ecosystem now. So I think it's a little bit of a different strategy there. Look, I think the interesting thing is the SAP is the world. They bought a small company in France called contexture. They're trying to do this themselves. Microsoft, Microsoft didn't in Mark Benioff and Salesforce are asked on every earnings call now what are you doing for RPA? So they've got pressure. So maybe they invest in one of our competitors or maybe they, you'll take flow in Microsoft and expanded. I think we can't move fast enough because you know, I don't know if Microsoft has, I mean they're a great sponsor by the way. So I don't want to only be careful we swept with what I say. But you know, strategically speaking, these larger companies operate in 18 months, 12 1824 months kind of planning cycles. >>If he did that, he will never keep up with us. There's no one at any of our traditional large enterprise software companies that ever would have bet that we would come out and say that the best way to build applications right to solve problems will be through RPA. Either there'll be a layer on top of all their technologies that makes it easier than ever for business users to build applications and solve problems, that's going to scare them to death. Why? Because you don't have to move all your legacy systems anymore. Yes, you've got tons of databases, but guess what? Don't worry about it. Leave him alone. Stop spending money on ridiculous upgrades right now. Just build a new layer and I'm telling you I there. As they figured this out, they're going to keep looking back and say, Oh my God, why didn't we know? >>Why did we know there's it looked I hopefully we could all partner. We're going to try to go down that route, but there's something much bigger going on here and they haven't figured it out. Well, the SAP data is very interesting to me that I'm starting to connect the dots. I just did a piece on my breaking analysis and SAP, they thank you. They, they've acquired 31 companies over the last nine years, right? And they've not bit the bullet on integration the way Oracle had to with fusion. Right? And so as a result, there's this, they say throw everything into HANA. It's a memory that's not going to work from an integration standpoint, right? Automation is actually a way to connect, you know, the glue across all those disparate systems, right? And so that makes a lot of sense that you're having success inside SAP and there's no reason that can't continue. >>Why there's, you know, there's a number of major kind of trends we've outlined here. One of, uh, we call human in the loop. And you know, today, you know, when each, when an unattended robot could actually stop a process and instead of sending the exception to a, an it person who monitoring, say, orchestrator actually go to an inbox, a task and box of that business user in a call center or wherever, and that robot can go do something else because it's so, so efficient and productive. But once that human has to solve that problem, right, that robot or a robot will take that back on and keep going. This human and robot interaction, it doesn't exist today and we know we're rolling that out in our UI path apps. I think you know that that's kind of mind blowing and then when you add a, I can't go too far into our roadmap and strategy or when you added the app programming layer and you add data science, that's a little bit of a hint into where we're going because we're open and transparent. >>Our data science connection, it's, it's this platform here, this kind of, I'd like to still call it all RPA. I think that that's a good thing, but the reality is this platform does Tam. What it can do is nothing like it was a year ago and it won't be like where it is today. A year from now you've got the tiger by the tail, Bobby, you got work to do, but congratulations on all the success. It's really been great to be able to document this and cover it, so thanks for coming on the cube. Thank you. All right. Thank you for watching everybody back with our next guest. Right after this short break, you're watching the cube live from UI path forward three from Bellagio in Vegas right back.

>>Live from Atlanta, Georgia. It's the cube covering Ansible Fest 2019. Brought to you by red hat. >>Welcome back everyone. Live cube coverage here in Atlanta. This is the keeps coverage of Ansible Fest. This is red hat and suppose two days of live coverage. They had a contributor day yesterday before the conference all being covered by the cube. I'm John furrier, Miko Stu Miniman. Our next guest is Chris Gardner, principal analyst at Forrester Gardner. Welcome to the cube. Thanks. See you. Good to talk to you. Hey, analyzing the players in this space is really challenging. You've got a new wave that came out a few months ago. Yep. Laying it all out. Um, certainly the world changed. You go back eight years. Cloud was just hitting the scene on premises. Look good. Data's Stanley was rocking. You're doing network management, you're doing some configuration management now you've got observability, you've got automation apps. The world's changing big time. What's your take? What's this? I mean, it's interesting because the prior versions of that wave focused entirely on configuration management and the feedback I got was, um, the world's a lot bigger than that, right? >>And we have to talk about platforms and you heard it this morning during the keynote about Redhat moving towards an platform and automation platform. And my definition of a platform is things like configuration management, hybrid cloud management, all the various types of automation and orchestration need to be there. But you also need compliance. You need governance, you need the ability to hopefully make a call as to what is actually occurring and have some intelligence behind the automation. And obviously you need the integrations. It's not a situation to simply have as many people as possible, although that's nice as many vendors you work with. But to have real relationships, if you have Microsoft working on automation code with you, if, if Amazon working on automation code with you, that makes a true platform, right? It's John said earlier day a platform needs to be an enabler. And we've even said, if you can't build on top of this, like the collections that Ansible announced here seems like it might fit under that definition. >>And there's an old joke that everything becomes a platform eventually. Right? Um, but I think that, I think it bears it. There's some merit in this one. Um, the other thing is that I'm seeing a lot of folks want a holistic automation solution and the only way you're going to do that is to have a platform that you can build things on top of it and connect the pieces and provide the proper governance. So, um, I'm mostly in agreement with the definition that's been described here and I think you could tackle different ways. Uh, and all the vendors in the space are certainly doing that. Definitely platform thinking is different. Um, you know, the easy way to look at it and the old big data space do, we'll use to cover that was a tool versus a platform, you know, tools, a hammer, everything looks like a nail, did great things. >>One thing great are a few things. Good platform is more of a systems thinking. Yes, yes. And you've got glue layers, you've got data. So it's really more of that systems thinking that separates the winners from the losers, at least at our opinion. Absolutely. I mean, when you looked at who was the leaders in my wave, it wasn't the basics of automating or orchestration and configuration management, they all had that. The, the ones that were winners, where can I do compliance in a different way? Can I actually have people come into the system that aren't it people and make a call on some of these things? Can I apply AI and machine learning to some of this? Can I make some recommendations and hopefully direct people in the right, you know, the way they should go. And you know, the folks that were able to do that Rose to the top, the folks that weren't were average and below. >>Yeah. Chris bring us inside to some of the competitive dynamics here. We understand that, you know, there's a lot of open source here and therefore everybody holds hands and things can buy y'all. But, you know, there's, you know, product tools, there's the public clouds and what they do. And then, you know, Ansible, uh, you know, fit, fits in a lot of different places. Yeah. It's, it's a bit ironic because, uh, you know, this is one of those waves where, and it's very rare that everyone was sitting was, was at least preaching kumbaya. They are all saying that they were friendly with one another. And, and, uh, quite frankly, I, I tend to believe it. We're in a situation right now where you can't get by, especially in a hybrid cloud world. We are going to have resources that live in multiple, you know, AWS and Azure, but also on premises and at the edge. You need to have these integrations. You need to be able to talk to one another. So, um, that said, there's certainly a lot of coopertition going on where people are saying, if I can integrate these tools better, if I could provide a better governance layer, if I can again, hand things off to the enterprise in a way that has not been handed off before that I don't even have to go through an INO group and infrastructure operations group, those are willing, could be the ones that truly succeed in this space. >>Software defined data center, software defined cloud, everything software defined. Yep. These abstraction layers, data and software. We had a guest on the cube a week ago saying, data's the new software I get. Okay, it's nice, nice gimmick. But if you think about it, this abstraction layer, it's like a control plan. Everyone wants to go for these control planes, which is a feature of platform. As this automation platform becomes ultimately the AI platform, how do you see it evolving and expanding? Because you see organic growth, you see certainly key positions, 6 million stars on get hub. I mean, it's running the plumbing. I mean, come on. Like it's not, it's not like it's just some corner case. >>Yeah, yeah. Infrastructure. Yeah. I mean, you know, in an idealistic way, I'd like to see, we us resolve on singular holistic platforms for enterprises. The reality is that's not not the way you can do it today. What I do try to help clients do is at least rationalize their portfolio. If they have 12 different automation products they're running, chances are that's not the best idea. Um, I've actually had situations where someone will say to me, um, I'm running Ansible in one portion of my organization and chef and another, and I say, well, it's some, they do similar things. And the reason for it was because they were stood up organically. Each group kind of figured out the things along the way. And I have to at least guide them and say, you know, where are the similarities? Where can you potentially, you know, move some stuff from there. >>But the cloud discussion, you know, always debate upon, you know, multi-cloud, Seoul cloud, ultimately the workload needs something underneath. And I think workload definition dictates kind of what might be underneath. So it might be okay to have a couple, you know, automation platforms or it could be great to have one. I mean, this is really the eye of the beholder. Beauty is in the eye of the, >>yeah, in my view. Um, I, I've been an analyst for a couple of years before that I was doing this stuff for a living. I have the worst scars and in my view it's, it's not even a matter of how many tools you use. It's putting the workload where it belongs, that matters. And if you could do that with fewer tools, obviously that from an operational level that makes life a lot easier. Um, but I'm not going to say to somebody, you know, completely dismantle your entire automation and orchestration workflow just because I think this one tool is better. Let's talk about how we can, >>that's the worst case scenario because if you have to dictate workloads based on what tool you have, that's supposed to be the other way around. >>Yes. Setting up a nuclear bomb in the data center or in the cloud has never worked. Note to self, don't do that. Yes. One of the interesting conversations we've already been having here at the show is that the tool is actually helping to drive some of the cultural change in collaboration. So, you know, what are you finding in your research? How is that, you know, kind of this admin role and you know, to the cloud in applications. You know, it's interesting. I, we continued to beat the drum that these folks are becoming developers, but we've been beating that drum for a decade now and quite frankly we had to continue to beat it. But what I think is more even more interesting is we have groups starting to pop up in our research that are separate from it, that focus on automation in a way that no one has done before. >>Some we went into it saying, Oh, that's a center of excellence, right? And the teams that we talked to said no, do not call us a center of excellence. A two reasons. One is that term is tainted. Uh, but secondly, we're not one team. There's multiple automation teams. So we're actually starting to call these groups, strike teams that come in and standardize and say, okay, I have a lead architect, a lead robot architects say it's around infrastructure automation. I'm going to standardize across the board and when other groups need to come on board, I have the principles already laid out. I have the, the process is already laid out. I come in, I accelerate that, I set it up and then I back off. I don't own the process and I'm not part of it either. I T's got operations of its own that's got to worry about. >>I'm going between the two and when we talk to especially the fortune 100 they are setting these groups up. Now when I ask them what do you called them? They don't have a name yet, so I think strike team sounds sexy, but ultimately this is not like a, a section of it that's been severed off and becomes this role. It's a completely true committee. I yeah. Oh yeah. I want our falls slow process. Exactly, exactly. And it better fits what the role is. The role is to come in, nail the process, get it automated and the get out. It's not to stand there and be a standards body forever. Um, there's certainly some groups that in some types of automation like RPA where you want them to stick around because you may want them to manage the bots. There's a whole role called bot masters, which is specifically for that role. But most of the time you want them to be part of that process and then you know, hand it back off. >>Yeah. We've seen some interesting patterns. I want to get your thoughts on this as a little bit of a non-sequitur. Want to bring it in, but in the security space you seeing a CSOs chief information security officers building their own stacks internally, they're picking one cloud, Amazon or Azure and they're building all in maybe some hedge with some people working on some backup cloud, but they don't want to fork their talent all on one cloud and they cause they need to be bad ass responsive strike teams for security pressure. Yeah, yeah, absolutely. Not as critical with the security side with automation, but certainly relevance. Is that the same thing going on here with this development Durham, this being continued to be as much more around core competency and building internally stacks and building some standards? >>I I, I think it is, and you know what's interesting too is that I work with, I'm on the infrastructure and operations team at Forrester. I talk with INO people all day long, but I work alongside the security team and I said to them a couple of years ago, um, you guys are going to have to get your hands dirty with this stuff that I cover. You guys have to know infrastructure, automation, API APIs, you need to know how to code these things. And I said, are you comfortable telling your sec ops folks, your clients that they go, no, by all means they have to be part of this. So they're okay with them talking to me, talking to them and saying that you need to be part of the infrastructure design process and need to be part of this decision making process. Right. Um, which is different than their sec ops role used to be. So my point is, is that these worlds are not that dissimilar as some people might think they are sec dev ops or whatever we're going to call it. We keep tacking letters onto this thing, uhm, is a actual discipline. And it is a reality in most organizations I talked to the people should. >>So a system has all of these things as data across the system. They have high blood subsystem you're talking about and yet it's this holistic system security and data. Yeah. >>And we're in a world now, especially around things like edge computing where data gravity matters. So all these pieces, you know, it's, if you go back to the old school kind of computer science folks from the, you know, 50 sixties and seventies, they're like, this is not new. We've been thinking systems thinking for awhile, but I think we're finally at a place where we're actually now breaking down the silos that we've been championing to do. So for, >>I got to ask you the analyst questions since you're watching the landscape. Sue wants to jump in, but I want to get this out. So observability became a category at a network management. I mean, network management was like this boring kind of plotting along white space. I mean, super important. People need to do network management. Then in comes the cloud becomes a data problem. Whether it's observability you get to microservices, you got security signal FX, all these companies going public. Um, well a lot of M and a activities basically large segment, a lot of frothiness automation feels like it's growing to be big. Is there startup opportunities here? If, if platforms are becoming being a combination of things, is there room for startups and if so, what would you say? Um, those stars would look like? There are, I think >>what we're seeing is, and it speaks to the observer, observe the word you just said. Um, uh, I can, I can S I can know what it is, but I can't say it. Um, we're seeing the APM vendors move down the stack. We're seeing the infrastructure monitoring vendors move up the stack and in the middle we're seeing them both try to automate the same things. Um, you cannot pull off some of the infrastructure as code automation that we need to pull off without observability, but you can't get that observability unless you are able to pull it from the top of the stack. Um, what we're going to see is consolidation and we're already starting to see it, um, where you're gonna have different groups come together and say, why did have to tools to do this? Why not do one? Um, the reason why you do multiple tools today is because no one is truly strong at the entire stack. >>A lot of the folks that are going down the stack to say that they're not quite infrastructure automation players just yet, but watch this space, they will eventually, Oh, this change happening. Absolutely. Startups getting funded. Do you think there's opportunity to take some territory down? If there's any opportunity? And, and I'm, I'm pushing for this, it's in the AI AI ops space when it comes to these things is actually going beyond where we stand today. So I want to be clear that, um, AI ops is a great concept. The reality of is that we're still a ways away from being practical. I'd like to see not just recommendations from these tools that the startups are providing, but actually trust in them to make the changes necessary. So Chris, it sounds like the antibody automation platform announcement today fits with what you've been saying for the last couple of years. >>So the question is, what's next? Where does the Ansible need to mature and expand and you know, what, what are users asking for that Ansible is not doing today? So a couple things. Um, they did okay, but not fantastic at infrastructure modeling. Ansible. They did okay, but not amazing at what we call comprehension, which is making a call as to, you know, using AI and machine learning to make a call and what the infrastructure layers should look like. To be Frank, no one did really well in that one. So not too, not too bad on that. Um, and the other thing is they need to improve slightly. Is there integration story? They actually have a really good one. You see all the folks that are here. Um, it's just, it's, it's just as hair away from being the best. They're not quite there yet. So, and when, again, when I mean integrations, I don't mean having a laundry list of vendors you work with. >>I mean actually working with them to build code and you saw that this morning where there's the best, uh, right now surprisingly is VMware, but for you Morris built that relationship off for a long time. Um, they work right alongside Microsoft and Google and all these folks to build the code together in the industry. Uh, I think the darkest source of all is probably, and it remains to be seen if they can actually do something that is HashiCorp. Um, Terraform is an interesting player in this entire space. I actually included them in our wave on infrastructure automation platforms and you can argue is it even an automation platform? Quite frankly. Um, uh, I think HashiCorp itself was trying to figure out exactly what it is. But the bottom line is it's got tremendous Mindshare and it works well. So I think that if you watch, if you see the strategy going forward and look at, you know, what they're putting their investments into, they could become a really serious damaging player in this space. Chris Gardner, thanks for coming on the cube, sharing your insights and your research at Forrester forced wave. Check it out. Just came out a couple of months ago. Uh, infrastructure automation platforms. Q three 2019. Chris Gardner, the author here in the Q, breaking it down. I'm John furrier. There's too many men. We'll be back with more after the short break. Thank you.