(tech music) >> Live, from Bahrain, it's theCUBE. Covering AWS Summit Bahrain. Brought to you by Amazon Web Services. >> Hello, everyone. Welcome back to our live coverage here in Bahrain for theCUBE's exclusive coverage of AWS Summit here in the Middle East in the region. Our first time here, lots of observations, lots of learnings, and also great people we're meeting. I'm John Furrier, your host of theCUBE. Our next guest is Mohammed Ali Al Qaed, who is the Chief Executive of the Information E-Government Authority, also known as IGA. Welcome to theCUBE. >> Thank you very much, thank you for hosting me. >> Saw you last night at dinner. We were talking about all the opportunities. So the first question I have to ask you is, as you guys are bringing in the digital transformation, which is happening, you now have Amazon here with the region, how is that changing things? >> Of course, always we try to innovate, and the technology, if you don't innovate, if you don't make sure that you are ready for the changes coming, it is very difficult. When we announced our first strategy in 2007, we delivered it in 2010 becoming the leader in the region, delivering 200 services over four channels. But as you mentioned, usually in the technology, the longest cycle is the infrastructure and servers and configurations, buying things and configuring. And with the leadership vision of transforming the country the vision 2030, transformations happening in the judicial system and to the commercial legislations and to the customs and to health and education, the pace of change required and the ambition is very much, much higher. And particularly, when you develop something and you were successful. So our leaders say, we are a bit slow. We need to fast things up. So then we looked into the Amazon and the cloud. How it can help us. And usually, in Bahrain, we don't have the luxury of trial and error. Trying things and if it works, you know, try multiple things. So we have to study it well. This is why we looked into the cloud, what it can bring to the country, the agility, the time to market. And when we put the strategy forward, it was a comprehensive one. The leaders decided to go cloud-first policy. Everybody should move. Because that's the way forward, that's the way that the Kingdom can deliver its vision-- >> And the cloud-first decision, when was that made? >> That was last year. And, when it was made, it's not a piece of strategy. You have to look at it in a comprehensive way. >> Yeah. >> You have to look into your laws, legislations, compliance, >> Yeah. >> audits, architectures, unit policies, skills sets available, the procurement process, the tendering process, and you have to review all of that-- >> Yeah. >> and make sure that there is no show blockers or barriers for the implementation. Otherwise, it will take a long time. >> Yeah. (chuckles) >> This is why, in a year's time, we managed to migrate huge workloads to the cloud. >> We were talking last night about how hard it is just to figure out the future, never mind provisioning all the gear you got to do, and the training, and so this cloud-first is very interesting. But I also want to just tell you that we talked about, also, how hard it is. So when I say, oh, go cloud-first, it's so easy! Right? No, it isn't. There's a lot of work involved. >> Yes. >> Take us through some of the things that you guys have done, your learnings prior to cloud-first. And the key learnings now that cloud-first has been under your belt for over a year. >> Of course, always with the governments, the biggest challenge will happen about the security. How I am going to move losing the control, putting my processing and storage outside the government. How I'm going to protect it, somebody else is there. That's the biggest challenge. That's number one. Number two, people doesn't understand, they think it's processing. So okay, I have my processing power, I will save some money, that's not much. >> Yeah. >> But they don't think about the ultimate goal. About the time to market. >> Yeah. >> When you have a new vision, when you have a new service to be delivered, you cannot wait for 18, 24 months for the infrastructure-- >> Yeah. >> Getting there, idle for a couple of years until you know the full utilization of them. So this is why the ultimate goal is much, much bigger. >> Yeah. >> And the thing about the issue, of somebody else, looking into-- >> So speed is critical. >> Yeah. >> And you guys have speed under your belt. You did a Formula One racetrack in what, 14 months I heard, very fast construction. The Amazon region is going up in record time. Is this a cultural thing? Just go fast? People like speed? The need for speed? >> We like that, you know the Formula One, of course that's just part of our DNA. Our leaders always push the citizens. And that's the Bahraini culture. >> Yeah, the other thing, too, about the application, now to get back to our serious conversation, to have innovation, you need to have software development go on in a way that's not the 24-month, oh we built it, and you don't know if you tested it or not. But you got to built the dev-ops model infrastructure as code. How far along are you on the infrastructure as code? Because the developer side is going to be very great. >> [Mohammed Ali Al Qaed] Yeah. >> Amazon's proven that developers love it, easy to get going, lower cost to test, agility, time to market, time to value. The setup for you guys is a little bit different. You've got infrastructure as code-- >> Yeah. >> You're not a startup. But you want to act like one, but what are you going to do for the infrastructure? >> Of course, the infrastructure in terms of processing, that's easy because it's been migrated, most of it's been migrated, our ports, our channels, our mobile channels. >> The networks, you have 5G, do you have 5G here yet? >> We are already experimenting, ready for it. The frequency's already freed up for the telco's to utilize them. It's already there for the 5G. Of course, fiber is everywhere, all the government entities are connected to the fiber. High speed, we have 100-meg, we have a gig, we have 10 gig, we have all kinds of speed available. >> No problem for bandwidth in the country. >> I don't think so. We don't have an issue about the bandwidth. And the processing power, once moved, then the optimization, which already happened. But as you mentioned, coming to the development, we started already, developing into the serverless, using the lamdas, using born-in-the-cloud concepts, that was not there once we started. But now we are already educating and training our employees. >> What's the reaction to it? >> The reaction is excellent. I give an example today about the fingerprint authentication. That's a basic service, but it requires a huge demand return from all the telco's, all transactions happen in telco's, private hospitals, the banking are coming. >> Yeah. >> Any authentication happens, it has to happen in a second. >> Yeah. >> So that requires a huge, massive infrastructure. Once we built it, at the beginning, for only a few customers-- >> Yeah. >> we invested about 250,000 dinar at that time. Now, I think, it's being moved completely to serverless-- >> Yeah. >> Concept, a new development. >> Yeah. >> So... >> A simple idea, hard to implement in the old way, but the new way, you got to wire API's around, sling API's, and connect devices, telco's, environments quickly. So this brings up the integration, this is the benefit of the cloud. >> [Mohammed Ali Al Qaed] Exactly. >> The glue layer is, what, microservices? Is it API's? How are you making that lambda function...? >> You know, that API's, and, just, you call the service and it gets you online and you go back to the storage. I mean, a basic thing. >> Yeah. >> Initially, you know, you need a testing environment, a development environment, a lot of infrastructure, and then you have to secure all of that, secure your data. >> Yeah. >> Now, it's a fraction of that cost and much faster to go to market. >> I'm a huge believer in the services model, and this is why microservices is a big deal right now in clouds, if you look at all the cloud-native conversations, it's containers, we're using it, no problem, very good to use containers. They're great. Kubernetes, now, orchestration. But deal with state and stateless applications is now the new challenge because there are so many new services that are spinning up. Soon, you're going to be like, ordering McDonald's, you know, I'll have a microservice over here, so this is the world we're moving to. This is the services. >> Exactly. And we would like to build a center of excellence, you know? Because we get into this journey, we looked into all our legislations and the ecosystem, trained our employees, their skillset is very important, with the program, with Tamkeen. We looked into the training strategy, all the portfolio training, making sure that our Bahrainees have the ability to develop, to operate, databases and all aspects, even the planning of it. Then institutes, partners, to be ready to train the private sector and everybody. >> You know, Mohammed, I'm really impressed with the entrepreneurial people that I've met. They've got a good mojo to them, because they're kind of cocky, which I love about them, but they're not arrogant. They're like, they're smart, and I so I got a... I see a good community there. The question for you is, as you move to cloud-native, how is that transition? The young kids get it. I mean, it's no problem. >> Yeah. >> Where's the progress on the skillset gap? I've heard that conversation. I just don't see it being a problem if the young kids are eating up the cloud stuff like it's nobody's business. >> Yeah. >> Then I don't see a problem. What's your take on this skills gap thing? I mean, the guys I met, and the entrepreneurs, they're like, they want more action. >> Exactly. The point is the current employees that we have already. Hundreds of government employees that have been trained in a different environment with different technologies. I get a couple of questions from some of the professionals in the market, private sector and government sector: how we can benefit out of that? How we can help? We are experts in the field, but cloud for us is a new thing. So as you mentioned, for them it might be a bit more difficult. So what we did, and the IGA, we created a taskforce of the most brilliant team, and told them okay, you have to migrate the workloads, train, we'll give you the training, and you have to migrate. >> Go. >> Next, you have now to optimize. Give the task of migration to somebody else, a new team, and the old team, they have to optimize. Third, now you have to work on, bore on the cloud, develop on the cloud, create the environment of the cloud, a new concept. So that's how we take-- >> So you're spreading the work around through hands-on training? >> Of course. You train, and then you get them into the action, hands, on, and so on, one by one. But, eventually, the university's already working on training their students. So we want to make sure that part of the curriculum, the cloud is there for the new generations to take it from day one. >> You know, you guys are a learning culture, my observation, first time here, very impressed. Very friendly, which is always cool. But, it's a multilingual culture. So, if you add source code to the new lingua, coding is going to be critical. Are you guys getting at the younger generation really when they're young? How young are you going in terms of the new language, software... Thoughts on that? Where do you see that going? >> Starting from school-- >> Elementary school? >> From elementary school, trying to get them in to coding. Universities as well. >> So you are teaching kids to code? >> Of course. And you know, any citizen they can get any certifications free of charge, according to the agreement with Tamkeen Labor Fund. They are willing to train any Bahrainian on any certifications, professional certifications, free of charge. >> That's great. >> To be ready for the next, and making Bahrainians-- >> So there's no excuses. >> Of course. >> There it is. >> We want to give Bahrainians a choice for employment. >> Yeah. >> You know? If that's the future, we have to make them ready for the future. >> That's great. And the cloud's going to give you all that energy. Talk about the relationship with Amazon a bit, Amazon Web Services. Obviously, Teresa Carlson, really behind this, the whole team. I talk about the whole company, I see them getting behind this and partnering with you guys. They're not just coming in here and being Amazon. There's a real co-development ethos. Talk about the relationship you have with... >> Amazon is impressive. I mean, the way that we work, in a partnership way, everybody should think about the long-term, not the short-term part of the partnership. That they should help the economy, the Bahrainis for employment, making sure that the economy will benefit out of this move to Bahrain. And as well, we have to help with the registrations, with the regulations, with any infrastructure connectivity to the international links. Whatever they need, we try to help them because we believe that eventually it will create the ecosystem for the market. >> I know they open up a lot of doors for you guys, and then for us as well. They attracted us to come and cover the territory here, so we're super excited. And I'm so glad we came because I learned a lot. >> Thank you. >> It's been fantastic. Okay, your big idea... Final question. What's your big idea that's going to come out of the cloud? It doesn't have to be the complete... Your idea, in your personal opinion, what is going to happen five years down the road? What is it going to look like? What will this new magic look like? What's the outcome? >> I think it will be a major restructure and reform in the government. So most of the people working into the routine work of buying and configuring, buying and configuring, they can be more focused into the real problem about the innovation, trying to bring solutions to the problems and issues in the country. Trying to develop software that will help the economy to foster, and to look at what is required, what is the vision of the leaders, try to implement those. So most of the people think business. Before, it was isolation. The technical people only, they had their territory, their environment looking at the wires and hardware and configurations, and somebody else looking into the development and a third group of people who are looking strategically, analytics, and how to utilize it. So, I think what we'll have, we'll merge those people, thinking only about the solutions, and how to analyze and how to come with new solutions out of those analytics. >> And that model has been consolidated, those silos have been broken down. With the cloud, it brings it all together. Developers are now on the front lines. >> Of course. And those-- >> And they're driving the business. >> They're driving the business. >> Mohammed, great to have you on, great to see you. Thanks for sharing your insight. And congratulations. Looking forward to tracking all the great coverage. Amazing opportunity here for everyone in the country, and also for Amazon and for us. Great to meet new people. This is theCUBE, I'm John Furrier. You can reach me on Twitter @Furrier, F-U-R-R-I-E-R, or just search, I'm open. All my channels are open, Telegram, Facebook, LinkedIn, Snapchat, you name it. Say hello, reach out. Stay with us, more all-day coverage after this short break. (tech music)

>> From theCUBE Studios in Palo Alto and Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Spark became a top level Apache project in 2014, and then shortly thereafter, burst onto the big data scene. Spark, along with the cloud, transformed and in many ways, disrupted the big data market. Databricks optimized its tech stack for Spark and took advantage of the cloud to really cleverly deliver a managed service that has become a leading AI and data platform among data scientists and data engineers. However, emerging customer data requirements are shifting into a direction that will cause modern data platform players generally and Databricks, specifically, we think, to make some key directional decisions and perhaps even reinvent themselves. Hello and welcome to this week's wikibon theCUBE Insights, powered by ETR. In this Breaking Analysis, we're going to do a deep dive into Databricks. We'll explore its current impressive market momentum. We're going to use some ETR survey data to show that, and then we'll lay out how customer data requirements are changing and what the ideal data platform will look like in the midterm future. We'll then evaluate core elements of the Databricks portfolio against that vision, and then we'll close with some strategic decisions that we think the company faces. And to do so, we welcome in our good friend, George Gilbert, former equities analyst, market analyst, and current Principal at TechAlpha Partners. George, good to see you. Thanks for coming on. >> Good to see you, Dave. >> All right, let me set this up. We're going to start by taking a look at where Databricks sits in the market in terms of how customers perceive the company and what it's momentum looks like. And this chart that we're showing here is data from ETS, the emerging technology survey of private companies. The N is 1,421. What we did is we cut the data on three sectors, analytics, database-data warehouse, and AI/ML. The vertical axis is a measure of customer sentiment, which evaluates an IT decision maker's awareness of the firm and the likelihood of engaging and/or purchase intent. The horizontal axis shows mindshare in the dataset, and we've highlighted Databricks, which has been a consistent high performer in this survey over the last several quarters. And as we, by the way, just as aside as we previously reported, OpenAI, which burst onto the scene this past quarter, leads all names, but Databricks is still prominent. You can see that the ETR shows some open source tools for reference, but as far as firms go, Databricks is very impressively positioned. Now, let's see how they stack up to some mainstream cohorts in the data space, against some bigger companies and sometimes public companies. This chart shows net score on the vertical axis, which is a measure of spending momentum and pervasiveness in the data set is on the horizontal axis. You can see that chart insert in the upper right, that informs how the dots are plotted, and net score against shared N. And that red dotted line at 40% indicates a highly elevated net score, anything above that we think is really, really impressive. And here we're just comparing Databricks with Snowflake, Cloudera, and Oracle. And that squiggly line leading to Databricks shows their path since 2021 by quarter. And you can see it's performing extremely well, maintaining an elevated net score and net range. Now it's comparable in the vertical axis to Snowflake, and it consistently is moving to the right and gaining share. Now, why did we choose to show Cloudera and Oracle? The reason is that Cloudera got the whole big data era started and was disrupted by Spark. And of course the cloud, Spark and Databricks and Oracle in many ways, was the target of early big data players like Cloudera. Take a listen to Cloudera CEO at the time, Mike Olson. This is back in 2010, first year of theCUBE, play the clip. >> Look, back in the day, if you had a data problem, if you needed to run business analytics, you wrote the biggest check you could to Sun Microsystems, and you bought a great big, single box, central server, and any money that was left over, you handed to Oracle for a database licenses and you installed that database on that box, and that was where you went for data. That was your temple of information. >> Okay? So Mike Olson implied that monolithic model was too expensive and inflexible, and Cloudera set out to fix that. But the best laid plans, as they say, George, what do you make of the data that we just shared? >> So where Databricks has really come up out of sort of Cloudera's tailpipe was they took big data processing, made it coherent, made it a managed service so it could run in the cloud. So it relieved customers of the operational burden. Where they're really strong and where their traditional meat and potatoes or bread and butter is the predictive and prescriptive analytics that building and training and serving machine learning models. They've tried to move into traditional business intelligence, the more traditional descriptive and diagnostic analytics, but they're less mature there. So what that means is, the reason you see Databricks and Snowflake kind of side by side is there are many, many accounts that have both Snowflake for business intelligence, Databricks for AI machine learning, where Snowflake, I'm sorry, where Databricks also did really well was in core data engineering, refining the data, the old ETL process, which kind of turned into ELT, where you loaded into the analytic repository in raw form and refine it. And so people have really used both, and each is trying to get into the other. >> Yeah, absolutely. We've reported on this quite a bit. Snowflake, kind of moving into the domain of Databricks and vice versa. And the last bit of ETR evidence that we want to share in terms of the company's momentum comes from ETR's Round Tables. They're run by Erik Bradley, and now former Gartner analyst and George, your colleague back at Gartner, Daren Brabham. And what we're going to show here is some direct quotes of IT pros in those Round Tables. There's a data science head and a CIO as well. Just make a few call outs here, we won't spend too much time on it, but starting at the top, like all of us, we can't talk about Databricks without mentioning Snowflake. Those two get us excited. Second comment zeros in on the flexibility and the robustness of Databricks from a data warehouse perspective. And then the last point is, despite competition from cloud players, Databricks has reinvented itself a couple of times over the year. And George, we're going to lay out today a scenario that perhaps calls for Databricks to do that once again. >> Their big opportunity and their big challenge for every tech company, it's managing a technology transition. The transition that we're talking about is something that's been bubbling up, but it's really epical. First time in 60 years, we're moving from an application-centric view of the world to a data-centric view, because decisions are becoming more important than automating processes. So let me let you sort of develop. >> Yeah, so let's talk about that here. We going to put up some bullets on precisely that point and the changing sort of customer environment. So you got IT stacks are shifting is George just said, from application centric silos to data centric stacks where the priority is shifting from automating processes to automating decision. You know how look at RPA and there's still a lot of automation going on, but from the focus of that application centricity and the data locked into those apps, that's changing. Data has historically been on the outskirts in silos, but organizations, you think of Amazon, think Uber, Airbnb, they're putting data at the core, and logic is increasingly being embedded in the data instead of the reverse. In other words, today, the data's locked inside the app, which is why you need to extract that data is sticking it to a data warehouse. The point, George, is we're putting forth this new vision for how data is going to be used. And you've used this Uber example to underscore the future state. Please explain? >> Okay, so this is hopefully an example everyone can relate to. The idea is first, you're automating things that are happening in the real world and decisions that make those things happen autonomously without humans in the loop all the time. So to use the Uber example on your phone, you call a car, you call a driver. Automatically, the Uber app then looks at what drivers are in the vicinity, what drivers are free, matches one, calculates an ETA to you, calculates a price, calculates an ETA to your destination, and then directs the driver once they're there. The point of this is that that cannot happen in an application-centric world very easily because all these little apps, the drivers, the riders, the routes, the fares, those call on data locked up in many different apps, but they have to sit on a layer that makes it all coherent. >> But George, so if Uber's doing this, doesn't this tech already exist? Isn't there a tech platform that does this already? >> Yes, and the mission of the entire tech industry is to build services that make it possible to compose and operate similar platforms and tools, but with the skills of mainstream developers in mainstream corporations, not the rocket scientists at Uber and Amazon. >> Okay, so we're talking about horizontally scaling across the industry, and actually giving a lot more organizations access to this technology. So by way of review, let's summarize the trend that's going on today in terms of the modern data stack that is propelling the likes of Databricks and Snowflake, which we just showed you in the ETR data and is really is a tailwind form. So the trend is toward this common repository for analytic data, that could be multiple virtual data warehouses inside of Snowflake, but you're in that Snowflake environment or Lakehouses from Databricks or multiple data lakes. And we've talked about what JP Morgan Chase is doing with the data mesh and gluing data lakes together, you've got various public clouds playing in this game, and then the data is annotated to have a common meaning. In other words, there's a semantic layer that enables applications to talk to the data elements and know that they have common and coherent meaning. So George, the good news is this approach is more effective than the legacy monolithic models that Mike Olson was talking about, so what's the problem with this in your view? >> So today's data platforms added immense value 'cause they connected the data that was previously locked up in these monolithic apps or on all these different microservices, and that supported traditional BI and AI/ML use cases. But now if we want to build apps like Uber or Amazon.com, where they've got essentially an autonomously running supply chain and e-commerce app where humans only care and feed it. But the thing is figuring out what to buy, when to buy, where to deploy it, when to ship it. We needed a semantic layer on top of the data. So that, as you were saying, the data that's coming from all those apps, the different apps that's integrated, not just connected, but it means the same. And the issue is whenever you add a new layer to a stack to support new applications, there are implications for the already existing layers, like can they support the new layer and its use cases? So for instance, if you add a semantic layer that embeds app logic with the data rather than vice versa, which we been talking about and that's been the case for 60 years, then the new data layer faces challenges that the way you manage that data, the way you analyze that data, is not supported by today's tools. >> Okay, so actually Alex, bring me up that last slide if you would, I mean, you're basically saying at the bottom here, today's repositories don't really do joins at scale. The future is you're talking about hundreds or thousands or millions of data connections, and today's systems, we're talking about, I don't know, 6, 8, 10 joins and that is the fundamental problem you're saying, is a new data error coming and existing systems won't be able to handle it? >> Yeah, one way of thinking about it is that even though we call them relational databases, when we actually want to do lots of joins or when we want to analyze data from lots of different tables, we created a whole new industry for analytic databases where you sort of mung the data together into fewer tables. So you didn't have to do as many joins because the joins are difficult and slow. And when you're going to arbitrarily join thousands, hundreds of thousands or across millions of elements, you need a new type of database. We have them, they're called graph databases, but to query them, you go back to the prerelational era in terms of their usability. >> Okay, so we're going to come back to that and talk about how you get around that problem. But let's first lay out what the ideal data platform of the future we think looks like. And again, we're going to come back to use this Uber example. In this graphic that George put together, awesome. We got three layers. The application layer is where the data products reside. The example here is drivers, rides, maps, routes, ETA, et cetera. The digital version of what we were talking about in the previous slide, people, places and things. The next layer is the data layer, that breaks down the silos and connects the data elements through semantics and everything is coherent. And then the bottom layers, the legacy operational systems feed that data layer. George, explain what's different here, the graph database element, you talk about the relational query capabilities, and why can't I just throw memory at solving this problem? >> Some of the graph databases do throw memory at the problem and maybe without naming names, some of them live entirely in memory. And what you're dealing with is a prerelational in-memory database system where you navigate between elements, and the issue with that is we've had SQL for 50 years, so we don't have to navigate, we can say what we want without how to get it. That's the core of the problem. >> Okay. So if I may, I just want to drill into this a little bit. So you're talking about the expressiveness of a graph. Alex, if you'd bring that back out, the fourth bullet, expressiveness of a graph database with the relational ease of query. Can you explain what you mean by that? >> Yeah, so graphs are great because when you can describe anything with a graph, that's why they're becoming so popular. Expressive means you can represent anything easily. They're conducive to, you might say, in a world where we now want like the metaverse, like with a 3D world, and I don't mean the Facebook metaverse, I mean like the business metaverse when we want to capture data about everything, but we want it in context, we want to build a set of digital twins that represent everything going on in the world. And Uber is a tiny example of that. Uber built a graph to represent all the drivers and riders and maps and routes. But what you need out of a database isn't just a way to store stuff and update stuff. You need to be able to ask questions of it, you need to be able to query it. And if you go back to prerelational days, you had to know how to find your way to the data. It's sort of like when you give directions to someone and they didn't have a GPS system and a mapping system, you had to give them turn by turn directions. Whereas when you have a GPS and a mapping system, which is like the relational thing, you just say where you want to go, and it spits out the turn by turn directions, which let's say, the car might follow or whoever you're directing would follow. But the point is, it's much easier in a relational database to say, "I just want to get these results. You figure out how to get it." The graph database, they have not taken over the world because in some ways, it's taking a 50 year leap backwards. >> Alright, got it. Okay. Let's take a look at how the current Databricks offerings map to that ideal state that we just laid out. So to do that, we put together this chart that looks at the key elements of the Databricks portfolio, the core capability, the weakness, and the threat that may loom. Start with the Delta Lake, that's the storage layer, which is great for files and tables. It's got true separation of compute and storage, I want you to double click on that George, as independent elements, but it's weaker for the type of low latency ingest that we see coming in the future. And some of the threats highlighted here. AWS could add transactional tables to S3, Iceberg adoption is picking up and could accelerate, that could disrupt Databricks. George, add some color here please? >> Okay, so this is the sort of a classic competitive forces where you want to look at, so what are customers demanding? What's competitive pressure? What are substitutes? Even what your suppliers might be pushing. Here, Delta Lake is at its core, a set of transactional tables that sit on an object store. So think of it in a database system, this is the storage engine. So since S3 has been getting stronger for 15 years, you could see a scenario where they add transactional tables. We have an open source alternative in Iceberg, which Snowflake and others support. But at the same time, Databricks has built an ecosystem out of tools, their own and others, that read and write to Delta tables, that's what makes the Delta Lake and ecosystem. So they have a catalog, the whole machine learning tool chain talks directly to the data here. That was their great advantage because in the past with Snowflake, you had to pull all the data out of the database before the machine learning tools could work with it, that was a major shortcoming. They fixed that. But the point here is that even before we get to the semantic layer, the core foundation is under threat. >> Yep. Got it. Okay. We got a lot of ground to cover. So we're going to take a look at the Spark Execution Engine next. Think of that as the refinery that runs really efficient batch processing. That's kind of what disrupted the DOOp in a large way, but it's not Python friendly and that's an issue because the data science and the data engineering crowd are moving in that direction, and/or they're using DBT. George, we had Tristan Handy on at Supercloud, really interesting discussion that you and I did. Explain why this is an issue for Databricks? >> So once the data lake was in place, what people did was they refined their data batch, and Spark has always had streaming support and it's gotten better. The underlying storage as we've talked about is an issue. But basically they took raw data, then they refined it into tables that were like customers and products and partners. And then they refined that again into what was like gold artifacts, which might be business intelligence metrics or dashboards, which were collections of metrics. But they were running it on the Spark Execution Engine, which it's a Java-based engine or it's running on a Java-based virtual machine, which means all the data scientists and the data engineers who want to work with Python are really working in sort of oil and water. Like if you get an error in Python, you can't tell whether the problems in Python or where it's in Spark. There's just an impedance mismatch between the two. And then at the same time, the whole world is now gravitating towards DBT because it's a very nice and simple way to compose these data processing pipelines, and people are using either SQL in DBT or Python in DBT, and that kind of is a substitute for doing it all in Spark. So it's under threat even before we get to that semantic layer, it so happens that DBT itself is becoming the authoring environment for the semantic layer with business intelligent metrics. But that's again, this is the second element that's under direct substitution and competitive threat. >> Okay, let's now move down to the third element, which is the Photon. Photon is Databricks' BI Lakehouse, which has integration with the Databricks tooling, which is very rich, it's newer. And it's also not well suited for high concurrency and low latency use cases, which we think are going to increasingly become the norm over time. George, the call out threat here is customers want to connect everything to a semantic layer. Explain your thinking here and why this is a potential threat to Databricks? >> Okay, so two issues here. What you were touching on, which is the high concurrency, low latency, when people are running like thousands of dashboards and data is streaming in, that's a problem because SQL data warehouse, the query engine, something like that matures over five to 10 years. It's one of these things, the joke that Andy Jassy makes just in general, he's really talking about Azure, but there's no compression algorithm for experience. The Snowflake guy started more than five years earlier, and for a bunch of reasons, that lead is not something that Databricks can shrink. They'll always be behind. So that's why Snowflake has transactional tables now and we can get into that in another show. But the key point is, so near term, it's struggling to keep up with the use cases that are core to business intelligence, which is highly concurrent, lots of users doing interactive query. But then when you get to a semantic layer, that's when you need to be able to query data that might have thousands or tens of thousands or hundreds of thousands of joins. And that's a SQL query engine, traditional SQL query engine is just not built for that. That's the core problem of traditional relational databases. >> Now this is a quick aside. We always talk about Snowflake and Databricks in sort of the same context. We're not necessarily saying that Snowflake is in a position to tackle all these problems. We'll deal with that separately. So we don't mean to imply that, but we're just sort of laying out some of the things that Snowflake or rather Databricks customers we think, need to be thinking about and having conversations with Databricks about and we hope to have them as well. We'll come back to that in terms of sort of strategic options. But finally, when come back to the table, we have Databricks' AI/ML Tool Chain, which has been an awesome capability for the data science crowd. It's comprehensive, it's a one-stop shop solution, but the kicker here is that it's optimized for supervised model building. And the concern is that foundational models like GPT could cannibalize the current Databricks tooling, but George, can't Databricks, like other software companies, integrate foundation model capabilities into its platform? >> Okay, so the sound bite answer to that is sure, IBM 3270 terminals could call out to a graphical user interface when they're running on the XT terminal, but they're not exactly good citizens in that world. The core issue is Databricks has this wonderful end-to-end tool chain for training, deploying, monitoring, running inference on supervised models. But the paradigm there is the customer builds and trains and deploys each model for each feature or application. In a world of foundation models which are pre-trained and unsupervised, the entire tool chain is different. So it's not like Databricks can junk everything they've done and start over with all their engineers. They have to keep maintaining what they've done in the old world, but they have to build something new that's optimized for the new world. It's a classic technology transition and their mentality appears to be, "Oh, we'll support the new stuff from our old stuff." Which is suboptimal, and as we'll talk about, their biggest patron and the company that put them on the map, Microsoft, really stopped working on their old stuff three years ago so that they could build a new tool chain optimized for this new world. >> Yeah, and so let's sort of close with what we think the options are and decisions that Databricks has for its future architecture. They're smart people. I mean we've had Ali Ghodsi on many times, super impressive. I think they've got to be keenly aware of the limitations, what's going on with foundation models. But at any rate, here in this chart, we lay out sort of three scenarios. One is re-architect the platform by incrementally adopting new technologies. And example might be to layer a graph query engine on top of its stack. They could license key technologies like graph database, they could get aggressive on M&A and buy-in, relational knowledge graphs, semantic technologies, vector database technologies. George, as David Floyer always says, "A lot of ways to skin a cat." We've seen companies like, even think about EMC maintained its relevance through M&A for many, many years. George, give us your thought on each of these strategic options? >> Okay, I find this question the most challenging 'cause remember, I used to be an equity research analyst. I worked for Frank Quattrone, we were one of the top tech shops in the banking industry, although this is 20 years ago. But the M&A team was the top team in the industry and everyone wanted them on their side. And I remember going to meetings with these CEOs, where Frank and the bankers would say, "You want us for your M&A work because we can do better." And they really could do better. But in software, it's not like with EMC in hardware because with hardware, it's easier to connect different boxes. With software, the whole point of a software company is to integrate and architect the components so they fit together and reinforce each other, and that makes M&A harder. You can do it, but it takes a long time to fit the pieces together. Let me give you examples. If they put a graph query engine, let's say something like TinkerPop, on top of, I don't even know if it's possible, but let's say they put it on top of Delta Lake, then you have this graph query engine talking to their storage layer, Delta Lake. But if you want to do analysis, you got to put the data in Photon, which is not really ideal for highly connected data. If you license a graph database, then most of your data is in the Delta Lake and how do you sync it with the graph database? If you do sync it, you've got data in two places, which kind of defeats the purpose of having a unified repository. I find this semantic layer option in number three actually more promising, because that's something that you can layer on top of the storage layer that you have already. You just have to figure out then how to have your query engines talk to that. What I'm trying to highlight is, it's easy as an analyst to say, "You can buy this company or license that technology." But the really hard work is making it all work together and that is where the challenge is. >> Yeah, and well look, I thank you for laying that out. We've seen it, certainly Microsoft and Oracle. I guess you might argue that well, Microsoft had a monopoly in its desktop software and was able to throw off cash for a decade plus while it's stock was going sideways. Oracle had won the database wars and had amazing margins and cash flow to be able to do that. Databricks isn't even gone public yet, but I want to close with some of the players to watch. Alex, if you'd bring that back up, number four here. AWS, we talked about some of their options with S3 and it's not just AWS, it's blob storage, object storage. Microsoft, as you sort of alluded to, was an early go-to market channel for Databricks. We didn't address that really. So maybe in the closing comments we can. Google obviously, Snowflake of course, we're going to dissect their options in future Breaking Analysis. Dbt labs, where do they fit? Bob Muglia's company, Relational.ai, why are these players to watch George, in your opinion? >> So everyone is trying to assemble and integrate the pieces that would make building data applications, data products easy. And the critical part isn't just assembling a bunch of pieces, which is traditionally what AWS did. It's a Unix ethos, which is we give you the tools, you put 'em together, 'cause you then have the maximum choice and maximum power. So what the hyperscalers are doing is they're taking their key value stores, in the case of ASW it's DynamoDB, in the case of Azure it's Cosmos DB, and each are putting a graph query engine on top of those. So they have a unified storage and graph database engine, like all the data would be collected in the key value store. Then you have a graph database, that's how they're going to be presenting a foundation for building these data apps. Dbt labs is putting a semantic layer on top of data lakes and data warehouses and as we'll talk about, I'm sure in the future, that makes it easier to swap out the underlying data platform or swap in new ones for specialized use cases. Snowflake, what they're doing, they're so strong in data management and with their transactional tables, what they're trying to do is take in the operational data that used to be in the province of many state stores like MongoDB and say, "If you manage that data with us, it'll be connected to your analytic data without having to send it through a pipeline." And that's hugely valuable. Relational.ai is the wildcard, 'cause what they're trying to do, it's almost like a holy grail where you're trying to take the expressiveness of connecting all your data in a graph but making it as easy to query as you've always had it in a SQL database or I should say, in a relational database. And if they do that, it's sort of like, it'll be as easy to program these data apps as a spreadsheet was compared to procedural languages, like BASIC or Pascal. That's the implications of Relational.ai. >> Yeah, and again, we talked before, why can't you just throw this all in memory? We're talking in that example of really getting down to differences in how you lay the data out on disk in really, new database architecture, correct? >> Yes. And that's why it's not clear that you could take a data lake or even a Snowflake and why you can't put a relational knowledge graph on those. You could potentially put a graph database, but it'll be compromised because to really do what Relational.ai has done, which is the ease of Relational on top of the power of graph, you actually need to change how you're storing your data on disk or even in memory. So you can't, in other words, it's not like, oh we can add graph support to Snowflake, 'cause if you did that, you'd have to change, or in your data lake, you'd have to change how the data is physically laid out. And then that would break all the tools that talk to that currently. >> What in your estimation, is the timeframe where this becomes critical for a Databricks and potentially Snowflake and others? I mentioned earlier midterm, are we talking three to five years here? Are we talking end of decade? What's your radar say? >> I think something surprising is going on that's going to sort of come up the tailpipe and take everyone by storm. All the hype around business intelligence metrics, which is what we used to put in our dashboards where bookings, billings, revenue, customer, those things, those were the key artifacts that used to live in definitions in your BI tools, and DBT has basically created a standard for defining those so they live in your data pipeline or they're defined in their data pipeline and executed in the data warehouse or data lake in a shared way, so that all tools can use them. This sounds like a digression, it's not. All this stuff about data mesh, data fabric, all that's going on is we need a semantic layer and the business intelligence metrics are defining common semantics for your data. And I think we're going to find by the end of this year, that metrics are how we annotate all our analytic data to start adding common semantics to it. And we're going to find this semantic layer, it's not three to five years off, it's going to be staring us in the face by the end of this year. >> Interesting. And of course SVB today was shut down. We're seeing serious tech headwinds, and oftentimes in these sort of downturns or flat turns, which feels like this could be going on for a while, we emerge with a lot of new players and a lot of new technology. George, we got to leave it there. Thank you to George Gilbert for excellent insights and input for today's episode. I want to thank Alex Myerson who's on production and manages the podcast, of course Ken Schiffman as well. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our EIC over at Siliconangle.com, he does some great editing. Remember all these episodes, they're available as podcasts. Wherever you listen, all you got to do is search Breaking Analysis Podcast, we publish each week on wikibon.com and siliconangle.com, or you can email me at David.Vellante@siliconangle.com, or DM me @DVellante. Comment on our LinkedIn post, and please do check out ETR.ai, great survey data, enterprise tech focus, phenomenal. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time on Breaking Analysis.

(bright upbeat music) >> Welcome back, everybody, to the Supercloud 2. My name is Dave Vellante. And I'm pleased to welcome Nir Zuk. He's the founder and CTO of Palo Alto Networks. Nir, good to see you again. Welcome. >> Same here. Good to see you. >> So let's start with the right security architecture in the context of today's fragmented market. You've got a lot of different tools, you've got different locations, on-prem, you've got hardware and software. Tell us about the right security architecture from your standpoint. What's that look like? >> You know, the funny thing is using the word security in architecture rarely works together. (Dave chuckles) If you ask a typical information security person to step up to a whiteboard and draw their security architecture, they will look at you as if you fell from the moon. I mean, haven't you been here in the last 25 years? There's no security architecture. The architecture today is just buying a bunch of products and dropping them into the infrastructure at some relatively random way without really any guiding architecture. And that's a huge challenge in cybersecurity. It's always been, we've always tried to find ways to put an architecture into writing blueprints, whatever you want to call it, and it's always been difficult. Luckily, two things. First, there's something called zero trust, which we can talk a little bit about more, if you want, and zero trust among other things is really a way to create a security architecture, and second, because in the cloud, in the supercloud, we're starting from scratch, we can do things differently. We don't have to follow the way we've always done cybersecurity, again, buying random products, okay, maybe not random, maybe there is some thinking going into it by buying products, one of the other, dropping them in, and doing it over 20 years and ending up with a mess in the cloud, we have an opportunity to do it differently and really have an architecture. >> You know, I love talking to founders and particularly technical founders from StartupNation. I think I saw an article, I think it was Erie Levine, one of the founders or co-founders of Waze, and he had a t-shirt on, it said, "Fall in love with the problem, not the solution." Is that how you approached architecture? You talk about zero trust, it's a relatively new term, but was that in your head when you thought about forming the company? >> Yeah, so when I started Palo Alto Networks, exactly, by the way, 17 years ago, we got funded January, 2006, January 18th, 2006. The idea behind Palo Alto Networks was to create a security platform and over time take more and more cybersecurity functions and deliver them on top of that platform, by the way, as a service, SaaS. Everybody thought we were crazy trying to combine many functions into one platform, best of breed and defense in death and putting all your eggs in the same basket and a bunch of other slogans were flying around, and also everybody thought we were crazy asking customers to send information to the cloud in order to secure themselves. Of course, step forward 17 years, everything is now different. We changed the market. Almost all of cybersecurity today is delivered as SaaS and platforms are ruling more and more the world. And so again, the idea behind the platform was to over time take more and more cybersecurity functions and deliver them together, one brain, one decision being made for each and every packet or system call or file or whatever it is that you're making the decision about and it works really, really well. As a side effect, when you combine that with zero trust and you end up with, let's not call it an architecture yet. You end up with with something where any user, any location, both geographically as well as any location in terms of branch office, headquarters, home, coffee shop, hotel, whatever, so any user, any geographical location, any location, any connectivity method, whether it is SD1 or IPsec or Client VPN or Client SVPN or proxy or browser isolation or whatever and any application deployed anywhere, public cloud, private cloud, traditional data center, SaaS, you secure the same way. That's really zero trust, right? You secure everything, no matter who the user is, no matter where they are, no matter where they go, you secure them exactly the same way. You don't make any assumptions about the user or the application or the location or whatever, just because you trust nothing. And as a side effect, when you do that, you end up with a security architecture, the security architecture I just described. The same thing is true for securing applications. If you try to really think and not just act instinctively the way we usually do in cybersecurity and you say, I'm going to secure my traditional data center applications or private cloud applications and public cloud applications and my SaaS applications the same way, I'm not going to trust something just because it's deployed in the private data center. I'm not going to trust two components of an application or two applications talking to each other just because they're deployed in the same place versus if one component is deployed in one public cloud and the other component is deployed in another public cloud or private cloud or whatever. I'm going to secure all of them the same way without making any trust assumptions. You end up with an architecture for securing your applications, which is applicable for the supercloud. >> It was very interesting. There's a debate I want to pick up on what you said because you said don't call it an architecture yet. So Bob Muglia, I dunno if you know Bob, but he sort of started the debate, said, "Supercloud, think of it as a platform, not an architecture." And there are others that are saying, "No, no, if we do that, then we're going to have a bunch of more stove pipes. So there needs to be standard, almost a purist view. There needs to be a supercloud architecture." So how do you think about it? And it's a bit academic, I know, but do you think of this idea of a supercloud, this layer of value on top of the hyperscalers, do you think of that as a platform approach that each of the individual vendors are responsible for the architecture? Or is there some kind of overriding architecture of standards that needs to emerge to enable the supercloud? >> So we can talk academically or we can talk practically. >> Yeah, let's talk practically. That's who you are. (Dave laughs) >> Practically, this world is ruled by financial interests and none of the public cloud providers, especially the bigger they are has any interest of making it easy for anyone to go multi-cloud, okay? Also, on top of that, if we want to be even more practical, each of those large cloud providers, cloud scale providers have engineers and all these engineers think they're the best in the world, which they are and they all like to do things differently. So you can't expect things in AWS and in Azure and GCP and in the other clouds like Oracle and Ali and so on to be the same. They're not going to be the same. And some things can be abstracted. Maybe cloud storage or bucket storage can be abstracted with the layer that makes them look the same no matter where you're running. And some things cannot be abstracted and unfortunately will not be abstracted because the economical interest and the way engineers work won't let it happen. We as a third party provider, cybersecurity provider, and I'm sure other providers in other areas as well are trying or we're doing our best. We're not trying, we are doing our best, and it's pretty close to being the way you describe the top of your supercloud. We're building something that abstracts the underlying cloud such that securing each of these clouds, and by the way, I would add private cloud to it as well, looks exactly the same. So we use, almost always, whenever possible, the same terminology, no matter which cloud we're securing and the same policy and the same alerts and the same information and so on. And that's also very important because when you look at the people that actually end up using the product, security engineers and more importantly, SOC, security operations center analysts, they're not going to study the details of each and every cloud. It's just going to be too much. So we need to abstract it for them. >> Yeah, we agree by the way that the supercloud definition is inclusive of on-prem, you know, what you call private cloud. And I want to pick up on something else you said. I think you're right that abstracting and making consistent across clouds something like object storage, get put, you know, whether it's an S3 bucket or an Azure Blob, relatively speaking trivial. When you now bring that supercloud concept to something more complex like security, first of all, as a technically feasible and inferring the answer there is yes, and if so, what do you see as the main technical challenges of doing so? >> So it is feasible to the extent that the different cloud provide the same functionality. Then you step into a territory where different cloud providers have different paths services and different cloud providers do things a little bit differently and they have different sets of permissions and different logging that sometimes provides all the information and sometimes it doesn't. So you end up with some differences. And then the question is, do you abstract the lowest common dominator and that's all you support? Or do you find a way to be smarter than that? And yeah, whatever can be abstracted is abstracted and whatever cannot be abstracted, you find an easy way to represent that to your users, security engineers, security analysts, and so on, which is what I believe we do. >> And you do that by what? Inventing or developing technology that presents that experience to users? Could you be more specific there? >> Yeah, so different cloud providers call their storage in different names and you use different ways to configure them and the logs come out the same. So we normalize it. I mean, the keyword is probably normalization. Normalize it. And we try to, you know, then you have to pick a winner here and to use someone's terminology or you need to invent new terminology. So we try to use the terminology of the largest cloud provider so that we have a better chance of doing that but we can't always do that because they don't support everything that other cloud providers provide, but the important thing is, with or thanks to that normalization, our customers both on the engineering side and on the user side, operations side end up having to learn one terminology in order to set policies and understand attacks and investigate incidents. >> I wonder if I could pick your brain on what you see as the ideal deployment model to achieve this supercloud experience. For example, do you think instantiating your stack in multiple regions and multiple clouds is the right way to do it? Or is building a single global instance on top of the clouds a more preferable way? Are maybe other models we should consider? What do you see as the trade off of these different deployment models and which one is ideal in your view? >> Yeah, so first, when you deploy cloud security, you have to decide whether you're going to use agents or not. By agents, I mean something working, something running inside the workload. Inside a virtual machine on the container host attached to function, serverless function and so on and I, of course, recommend using agents because that enables prevention, it enables functionality you cannot get without agents but you have to choose that. Now, of course, if you choose agent, you need to deploy AWS agents in AWS and GCP agents in GCP and Azure agents in Azure and so on. Of course, you don't do it manually. You do it through the CICD pipeline. And then the second thing that you need to do is you need to connect with the consoles. Of course, that can be done over the internet no matter where your security instances is running. You can run it on premise, you can run it in one of the other different clouds. Of course, we don't run it on premise. We prefer not to run it on premise because if you're secured in cloud, you might as well run in the cloud. And then the question is, for example, do you run a separate instance for AWS for GCP or for Azure, or you want to run one instance for all of them in one of these clouds? And there are advantages and disadvantages. I think that from a security perspective, it's always better to run in one place because then when you collect the information, you get information from all the clouds and you can start looking for cross-cloud issues, incidents, attacks, and so on. The downside of that is that you need to send all the information to one of the clouds and you probably know that sending data out of the cloud costs a lot of money versus keeping it in the cloud. So theoretically, you can build an architecture where you keep the data for AWS in AWS, Azure in Azure, GCP in GCP, and then you try to run distributed queries. When you do that, you find out you'd end up paying more for the compute to do that than you would've paid for sending all the data to a central location. So we prefer the approach of running in one place, bringing all the data there, and running all the security, the machine learning or whatever, the rules or whatever it is that you're running in one place versus trying to create a distributed deployment in order to try to save some money on the data, the network data transfers. >> Yeah, thank you for that. That makes a lot of sense. And so basically, should we think about the next layer building security data lake, if you will, and then running machine learning on top of that if I can use that term of a data lake or a lake house? Is that sort of where you're headed? >> Yeah, look, the world is headed in that direction, not just the cybersecurity world. The world is headed from being rule-based to being data-based. So cybersecurity is not different and what we used to do with rules in the past, we're now doing with machine learning. So in the past, you would define rules saying, if you see this, this, and this, it's an attack. Now you just throw the data at the machine, I mean, I'm simplifying it, but you throw data at a machine. You'll tell the machine, find the attack in the data. It's not that simple. You need to build the right machine learning models. It needs to be done by people that are both cybersecurity experts and machine learning experts. We do it mostly with ex-military offensive people that take their offensive knowledge and translate it into machine learning models. But look, the world is moving in that direction and cybersecurity is moving in that direction as well. You need to collect a lot of data. Like I said, I prefer to see all the data in one place so that the machine learning can be much more efficient, pay for transferring the data, save money on the compute. >> I think the drop the mic quote it ignite that you had was within five years, your security operation is going to be AI-powered. And so you could probably apply that to virtually any job over the next five years. >> I don't know if any job. Certainly writing essays for school is automated already as we've seen with ChatGPT and potentially other things. By the way, we need to talk at some point about ChatGPT security. I don't want to think what happens when someone spends a lot of money on creating a lot of fake content and teaches ChatGPT the wrong answer to a question. We start seeing ChatGPT as the oracle of everything. We need to figure out what to do with the security of that. But yeah, things have to be automated in cybersecurity. They have to be automated. They're just too much data to deal with and it's just not even close to being good enough to wait for an incident to happen and then going investigate the incident based on the data that we have. It's better to look at all the data all the time, millions of events per second, and find those incidents before they happen. There's no way to do that without machine learning. >> I'd love to have you back and talk about ChatGPT. I know they're trying to put in some guardrails but there are a lot of unintended consequences, aren't there? >> Look, if they're not going to have a person filtering the data, then with enough money, you can create thousands or tens of thousands of pieces of articles or whatever that look real and teach the machine something that is totally wrong. >> We were talking about the hyper skills before and I agree with you. It's very unlikely they're going to get together, band together, and create these standards. But it's not a static market. It's a moving train, if you will. So assuming you're building this cross cloud experience which you are, what do you want from the hyperscalers? What do you want them to bring to the table? What is a technology supplier like Palo Alto Networks bring? In other words, where do you see ongoing as your unique value add and that moat that you're building and how will that evolve over time vis-a-vis the hyperscaler evolution? >> Yeah, look, we need APIs. The more data we have, the more access we have to more data, the less restricted the access is and the cheaper the access is to the data because someone has to pay today for some reason for accessing that data, the more secure their customers are going to be. So we need help and are helping by the way a lot, all of them in finding easy ways for customers to deploy things in the cloud, access data, and again, a lot of data, very diversified data and do it in a cost-effective way. >> And when we talk about the edge, I presume you look at the edge as just another data center or maybe it's the reverse. Maybe the data center is just another edge location, but you're seeing specific edge security solutions come out. I'm guessing that you would say, that's not what we want. Edge should be part of that architecture that we talked about earlier. Do you agree? >> Correct, it should be part of the architecture. I would also say that the edge provides an opportunity specifically for network security, whereas traditional network security would be deployed on premise. I'm talking about internet security but half network security market, and not just network security but also the other network intelligent functions like routing and QS. We're seeing a trend of pushing those to the edge of the cloud. So what you deploy on premise is technology for bringing packets to the edge of the cloud and then you run your security at the edge, whatever that edge is, whether it's a private edge or public edge, you run it in the edge. It's called SASE, Secure Access Services Edge, pronounced SASE. >> Nir, I got to thank you so much. You're such a clear thinker. I really appreciate you participating in Supercloud 2. >> Thank you. >> All right, keep it right there for more content covering the future of cloud and data. This is Dave Vellante for John Furrier. I'll be right back. (bright upbeat music)

(soft techno upbeat music) >> Hi there. Welcome back to Las Vegas. This is Dave Villante with Paul Gillon. Reinvent day one and a half. We started last night, Monday, theCUBE after dark. Now we're going wall to wall. Today. Today was of course the big keynote, Adam Selipsky, kind of the baton now handing, you know, last year when he did his keynote, he was very new. He was sort of still getting his feet wet and finding his guru swing. Settling in a little bit more this year, learning a lot more, getting deeper into the tech, but of course, sharing the love with other leaders like Peter DeSantis. Tomorrow's going to be Swamy in the keynote. Adrian Cockcroft is here. Former AWS, former network Netflix CTO, currently an analyst. You got your own firm now. You're out there. Great to see you again. Thanks for coming on theCUBE. >> Yeah, thanks. >> We heard you on at Super Cloud, you gave some really good insights there back in August. So now as an outsider, you come in obviously, you got to be impressed with the size and the ecosystem and the energy. Of course. What were your thoughts on, you know what you've seen so far, today's keynotes, last night Peter DeSantis, what stood out to you? >> Yeah, I think it's great to be back at Reinvent again. We're kind of pretty much back to where we were before the pandemic sort of shut it down. This is a little, it's almost as big as the, the largest one that we had before. And everyone's turned up. It just feels like we're back. So that's really good to see. And it's a slightly different style. I think there were was more sort of video production things happening. I think in this keynote, more storytelling. I'm not sure it really all stitched together very well. Right. Some of the stories like, how does that follow that? So there were a few things there and some of there were spelling mistakes on the slides, you know that ELT instead of ETL and they spelled ZFS wrong and something. So it just seemed like there was, I'm not quite sure just maybe a few things were sort of rushed at the last minute. >> Not really AWS like, was it? It's kind of remind the Patriots Paul, you know Bill Belichick's teams are fumbling all over the place. >> That's right. That's right. >> Part of it may be, I mean the sort of the market. They have a leader in marketing right now but they're going to have a CMO. So that's sort of maybe as lack of a single threaded leader for this thing. Everything's being shared around a bit more. So maybe, I mean, it's all fixable and it's mine. This is minor stuff. I'm just sort of looking at it and going there's a few things that looked like they were not quite as good as they could have been in the way it was put together. Right? >> But I mean, you're taking a, you know a year of not doing Reinvent. Yeah. Being isolated. You know, we've certainly seen it with theCUBE. It's like, okay, it's not like riding a bike. You know, things that, you know you got to kind of relearn the muscle memories. It's more like golf than is bicycle riding. >> Well I've done AWS keynotes myself. And they are pretty much scrambled. It looks nice, but there's a lot of scrambling leading up to when it actually goes. Right? And sometimes you can, you sometimes see a little kind of the edges of that, and sometimes it's much more polished. But you know, overall it's pretty good. I think Peter DeSantis keynote yesterday was a lot of really good meat there. There was some nice presentations, and some great announcements there. And today I was, I thought I was a little disappointed with some of the, I thought they could have been more. I think the way Andy Jesse did it, he crammed more announcements into his keynote, and Adam seems to be taking sort of a bit more of a measured approach. There were a few things he picked up on and then I'm expecting more to be spread throughout the rest of the day. >> This was more poetic. Right? He took the universe as the analogy for data, the ocean for security. Right? The Antarctic was sort of. >> Yeah. It looked pretty, >> yeah. >> But I'm not sure that was like, we're not here really to watch nature videos >> As analysts and journalists, You're like, come on. >> Yeah, >> Give it the meat >> That was kind the thing, yeah, >> It has always been the AWS has always been Reinvent has always been a shock at our approach. 100, 150 announcements. And they're really, that kind of pressure seems to be off them now. Their position at the top of the market seems to be unshakeable. There's no clear competition that's creeping up behind them. So how does that affect the messaging you think that AWS brings to market when it doesn't really have to prove that it's a leader anymore? It can go after maybe more of the niche markets or fix the stuff that's a little broken more fine tuning than grandiose statements. >> I think so AWS for a long time was so far out that they basically said, "We don't think about the competition, we are listen to the customers." And that was always the statement that works as long as you're always in the lead, right? Because you are introducing the new idea to the customer. Nobody else got there first. So that was the case. But in a few areas they aren't leading. Right? You could argue in machine learning, not necessarily leading in sustainability. They're not leading and they don't want to talk about some of these areas and-- >> Database. I mean arguably, >> They're pretty strong there, but the areas when you are behind, it's like they kind of know how to play offense. But when you're playing defense, it's a different set of game. You're playing a different game and it's hard to be good at both. I think and I'm not sure that they're really used to following somebody into a market and making a success of that. So there's something, it's a little harder. Do you see what I mean? >> I get opinion on this. So when I say database, David Foyer was two years ago, predicted AWS is going to have to converge somehow. They have no choice. And they sort of touched on that today, right? Eliminating ETL, that's one thing. But Aurora to Redshift. >> Yeah. >> You know, end to end. I'm not sure it's totally, they're fully end to end >> That's a really good, that is an excellent piece of work, because there's a lot of work that it eliminates. There's are clear pain points, but then you've got sort of the competing thing, is like the MongoDB and it's like, it's just a way with one database keeps it simple. >> Snowflake, >> Or you've got on Snowflake maybe you've got all these 20 different things you're trying to integrate at AWS, but it's kind of like you have a bag of Lego bricks. It's my favorite analogy, right? You want a toy for Christmas, you want a toy formula one racing car since that seems to be the theme, right? >> Okay. Do you want the fully built model that you can play with right now? Or do you want the Lego version that you have to spend three days building. Right? And AWS is the Lego technique thing. You have to spend some time building it, but once you've built it, you can evolve it, and you'll still be playing those are still good bricks years later. Whereas that prebuilt to probably broken gathering dust, right? So there's something about having an vulnerable architecture which is harder to get into, but more durable in the long term. And so AWS tends to play the long game in many ways. And that's one of the elements that they do that and that's good, but it makes it hard to consume for enterprise buyers that are used to getting it with a bow on top. And here's the solution. You know? >> And Paul, that was always Andy Chassy's answer to when we would ask him, you know, all these primitives you're going to make it simpler. You see the primitives give us the advantage to turn on a dime in the marketplace. And that's true. >> Yeah. So you're saying, you know, you take all these things together and you wrap it up, and you put a snowflake on top, and now you've got a simple thing or a Mongo or Mongo atlas or whatever. So you've got these layered platforms now which are making it simpler to consume, but now you're kind of, you know, you're all stuck in that ecosystem, you know, so it's like what layer of abstractions do you want to tie yourself to, right? >> The data bricks coming at it from more of an open source approach. But it's similar. >> We're seeing Amazon direct more into vertical markets. They spotlighted what Goldman Sachs is doing on their platform. They've got a variety of platforms that are supposedly targeted custom built for vertical markets. How do successful do you see that play being? Is this something that the customers you think are looking for, a fully integrated Amazon solution? >> I think so. There's usually if you look at, you know the MongoDB or data stacks, or the other sort of or elastic, you know, they've got the specific solution with the people that really are developing the core technology, there's open source equivalent version. The AWS is running, and it's usually maybe they've got a price advantage or it's, you know there's some data integration in there or it's somehow easier to integrate but it's not stopping those companies from growing. And what it's doing is it's endorsing that platform. So if you look at the collection of databases that have been around over the last few years, now you've got basically Elastic Mongo and Cassandra, you know the data stacks as being endorsed by the cloud vendors. These are winners. They're going to be around for a very long time. You can build yourself on that architecture. But what happened to Couch base and you know, a few of the other ones, you know, they don't really fit. Like how you going to bait? If you are now becoming an also ran, because you didn't get cloned by the cloud vendor. So the customers are going is that a safe place to be, right? >> But isn't it, don't they want to encourage those partners though in the name of building the marketplace ecosystem? >> Yeah. >> This is huge. >> But certainly the platform, yeah, the platform encourages people to do more. And there's always room around the edge. But the mainstream customers like that really like spending the good money, are looking for something that's got a long term life to it. Right? They're looking for a long commitment to that technology and that it's going to be invested in and grow. And the fact that the cloud providers are adopting and particularly AWS is adopting some of these technologies means that is a very long term commitment. You can base, you know, you can bet your future architecture on that for a decade probably. >> So they have to pick winners. >> Yeah. So it's sort of picking winners. And then if you're the open source company that's now got AWS turning up, you have to then leverage it and use that as a way to grow the market. And I think Mongo have done an excellent job of that. I mean, they're top level sponsors of Reinvent, and they're out there messaging that and doing a good job of showing people how to layer on top of AWS and make it a win-win both sides. >> So ever since we've been in the business, you hear the narrative hardware's going to die. It's just, you know, it's commodity and there's some truth to that. But hardware's actually driving good gross margins for the Cisco's of the world. Storage companies have always made good margins. Servers maybe not so much, 'cause Intel sucked all the margin out of it. But let's face it, AWS makes most of its money. We know on compute, it's got 25 plus percent operating margins depending on the seasonality there. What do you think happens long term to the infrastructure layer discussion? Okay, commodity cloud, you know, we talk about super cloud. Do you think that AWS, and the other cloud vendors that infrastructure, IS gets commoditized and they have to go up market or you see that continuing I mean history would say that still good margins in hardware. What are your thoughts on that? >> It's not commoditizing, it's becoming more specific. We've got all these accelerators and custom chips now, and this is something, this almost goes back. I mean, I was with some micro systems 20,30 years ago and we developed our own chips and HP developed their own chips and SGI mips, right? We were like, the architectures were all squabbling of who had the best processor chips and it took years to get chips that worked. Now if you make a chip and it doesn't work immediately, you screwed up somewhere right? It's become the technology of building these immensely complicated powerful chips that has become commoditized. So the cost of building a custom chip, is now getting to the point where Apple and Amazon, your Apple laptop has got full custom chips your phone, your iPhone, whatever and you're getting Google making custom chips and we've got Nvidia now getting into CPUs as well as GPUs. So we're seeing that the ability to build a custom chip, is becoming something that everyone is leveraging. And the cost of doing that is coming down to startups are doing it. So we're going to see many, many more, much more innovation I think, and this is like Intel and AMD are, you know they've got the compatibility legacy, but of the most powerful, most interesting new things I think are going to be custom. And we're seeing that with Graviton three particular in the three E that was announced last night with like 30, 40% whatever it was, more performance for HPC workloads. And that's, you know, the HPC market is going to have to deal with cloud. I mean they are starting to, and I was at Supercomputing a few weeks ago and they are tiptoeing around the edge of cloud, but those supercomputers are water cold. They are monsters. I mean you go around supercomputing, there are plumbing vendors on the booth. >> Of course. Yeah. >> Right? And they're highly concentrated systems, and that's really the only difference, is like, is it water cooler or echo? The rest of the technology stack is pretty much off the shelf stuff with a few tweets software. >> You point about, you know, the chips and what AWS is doing. The Annapurna acquisition. >> Yeah. >> They're on a dramatically different curve now. I think it comes down to, again, David Floyd's premise, really comes down to volume. The arm wafer volumes are 10 x those of X 86, volume always wins. And the economics of semis. >> That kind of got us there. But now there's also a risk five coming along if you, in terms of licensing is becoming one of the bottlenecks. Like if the cost of building a chip is really low, then it comes down to licensing costs and do you want to pay the arm license And the risk five is an open source chip set which some people are starting to use for things. So your dis controller may have a risk five in it, for example, nowadays, those kinds of things. So I think that's kind of the the dynamic that's playing out. There's a lot of innovation in hardware to come in the next few years. There's a thing called CXL compute express link which is going to be really interesting. I think that's probably two years out, before we start seeing it for real. But it lets you put glue together entire rack in a very flexible way. So just, and that's the entire industry coming together around a single standard, the whole industry except for Amazon, in fact just about. >> Well, but maybe I think eventually they'll get there. Don't use system on a chip CXL. >> I have no idea whether I have no knowledge about whether going to do anything CXL. >> Presuming I'm not trying to tap anything confidential. It just makes sense that they would do a system on chip. It makes sense that they would do something like CXL. Why not adopt the standard, if it's going to be as the cost. >> Yeah. And so that was one of the things out of zip computing. The other thing is the low latency networking with the elastic fabric adapter EFA and the extensions to that that were announced last night. They doubled the throughput. So you get twice the capacity on the nitro chip. And then the other thing was this, this is a bit technical, but this scalable datagram protocol that they've got which basically says, if I want to send a message, a packet from one machine to another machine, instead of sending it over one wire, I consider it over 16 wires in parallel. And I will just flood the network with all the packets and they can arrive in any order. This is why it isn't done normally. TCP is in order, the packets come in order they're supposed to, but this is fully flooding them around with its own fast retry and then they get reassembled at the other end. So they're not just using this now for HPC workloads. They've turned it on for TCP for just without any change to your application. If you are trying to move a large piece of data between two machines, and you're just pushing it down a network, a single connection, it takes it from five gigabits per second to 25 gigabits per second. A five x speed up, with a protocol tweak that's run by the Nitro, this is super interesting. >> Probably want to get all that AIML that stuff is going on. >> Well, the AIML stuff is leveraging it underneath, but this is for everybody. Like you're just copying data around, right? And you're limited, "Hey this is going to get there five times faster, pushing a big enough chunk of data around." So this is turning on gradually as the nitro five comes out, and you have to enable it at the instance level. But it's a super interesting announcement from last night. >> So the bottom line bumper sticker on commoditization is what? >> I don't think so. I mean what's the APIs? Your arm compatible, your Intel X 86 compatible or your maybe risk five one day compatible in the cloud. And those are the APIs, right? That's the commodity level. And the software is now, the software ecosystem is super portable across those as we're seeing with Apple moving from Intel to it's really not an issue, right? The software and the tooling is all there to do that. But underneath that, we're going to see an arms race between the top providers as they all try and develop faster chips for doing more specific things. We've got cranium for training, that instance has they announced it last year with 800 gigabits going out of a single instance, 800 gigabits or no, but this year they doubled it. Yeah. So 1.6 terabytes out of a single machine, right? That's insane, right? But what you're doing is you're putting together hundreds or thousands of those to solve the big machine learning training problems. These super, these enormous clusters that they're being formed for doing these massive problems. And there is a market now, for these incredibly large supercomputer clusters built for doing AI. That's all bandwidth limited. >> And you think about the timeframe from design to tape out. >> Yeah. >> Is just getting compressed It's relative. >> It is. >> Six is going the other way >> The tooling is all there. Yeah. >> Fantastic. Adrian, always a pleasure to have you on. Thanks so much. >> Yeah. >> Really appreciate it. >> Yeah, thank you. >> Thank you Paul. >> Cheers. All right. Keep it right there everybody. Don't forget, go to thecube.net, you'll see all these videos. Go to siliconangle.com, We've got features with Adam Selipsky, we got my breaking analysis, we have another feature with MongoDB's, Dev Ittycheria, Ali Ghodsi, as well Frank Sluman tomorrow. So check that out. Keep it right there. You're watching theCUBE, the leader in enterprise and emerging tech, right back. (soft techno upbeat music)

(outro music) >> Hey, I'm John Furrier, here with an exclusive interview with Ali Ghodsi, who's the CEO of Databricks. Ali, great to see you. Preview for reinvent. We're going to launch this story, exclusive Databricks material on the notes, after the keynotes prior to the keynotes and after the keynotes that reinvent. So great to see you. You know, you've been a partner of AWS for a very, very long time. I think five years ago, I think I first interviewed you, you were one of the first to publicly declare that this was a place to build a company on and not just post an application, but refactor capabilities to create, essentially a platform in the cloud, on the cloud. Not just an ISV; Independent Software Vendor, kind of an old term, we're talking about real platform like capability to change the game. Can you talk about your experience as an AWS partner? >> Yeah, look, so we started in 2013. I swiped my personal credit card on AWS and some of my co-founders did the same. And we started building. And we were excited because we just thought this is a much better way to launch a company because you can just much faster get time to market and launch your thing and you can get the end users much quicker access to the thing you're building. So we didn't really talk to anyone at AWS, we just swiped a credit card. And eventually they told us, "Hey, do you want to buy extra support?" "You're asking a lot of advanced questions from us." "Maybe you want to buy our advanced support." And we said, no, no, no, no. We're very advanced ourselves, we know what we're doing. We're not going to buy any advanced support. So, you know, we just built this, you know, startup from nothing on AWS without even talking to anyone there. So at some point, I think around 2017, they suddenly saw this company with maybe a hundred million ARR pop up on their radar and it's driving massive amounts of compute, massive amounts of data. And it took a little bit in the beginning just us to get to know each other because as I said, it's like we were not on their radar and we weren't really looking, we were just doing our thing. And then over the years the partnership has deepened and deepened and deepened and then with, you know, Andy (indistinct) really leaning into the partnership, he mentioned us at Reinvent. And then we sort of figured out a way to really integrate the two service, the Databricks platform with AWS . And today it's an amazing partnership. You know, we directly connected with the general managers for the services. We're connected at the CEO level, you know, the sellers get compensated for pushing Databricks, we're, we have multiple offerings on their marketplace. We have a native offering on AWS. You know, we're prominently always sort of marketed and you know, we're aligned also vision wise in what we're trying to do. So yeah, we've come a very, very long way. >> Do you consider yourself a SaaS app or an ISV or do you see yourself more of a platform company because you have customers. How would you categorize your category as a company? >> Well, it's a data platform, right? And actually the, the strategy of the Databricks is take what's otherwise five, six services in the industry or five, six different startups, but do them as part of one data platform that's integrated. So in one word, the strategy of data bricks is "unification." We call it the data lake house. But really the idea behind the data lake house is that of unification, or in more words it's, "The whole is greater than the sum of its parts." So you could actually go and buy five, six services out there or actually use five, six services from the cloud vendors, stitch it together and it kind of resembles Databricks. Our power is in doing those integrated, together in a way in which it's really, really easy and simple to use for end users. So yeah, we're a data platform. I wouldn't, you know, ISV that's a old term, you know, Independent Software Vendor. You know, I think, you know, we have actually a whole slew of ISVs on top of Databricks, that integrate with our platform. And you know, in our marketplace as well as in our partner connect, we host those ISVs that then, you know, work on top of the data that we have in the Databricks, data lake house. >> You know, I think one of the things your journey has been great to document and watch from the beginning. I got to give you guys credit over there and props, congratulations. But I think you're the poster child as a company to what we see enterprises doing now. So go back in time when you guys swiped a credit card, you didn't need attending technical support because you guys had brains, you were refactoring, rethinking. It wasn't just banging out software, you had, you were doing some complex things. It wasn't like it was just write some software hosted on server. It was really a lot more. And as a result your business worth billions of dollars. I think 38 billion or something like that, big numbers, big numbers of great revenue growth as well, billions in revenue. You have customers, you have an ecosystem, you have data applications on top of Databricks. So in a way you're a cloud on top of the cloud. So is there a cloud on top of the cloud? So you have ISVs, Amazon has ISVs. Can you take us through what this means and at this point in history, because this seems to be an advanced version of benefits of platforming and refactoring, leveraging say AWS. >> Yeah, so look, when we started, there was really only one game in town. It was AWS. So it was one cloud. And the strategy of the company then was, well Amazon had this beautiful set of services that they're building bottom up, they have storage, compute, networking, and then they have databases and so on. But it's a lot of services. So let us not directly compete with AWS and try to take out one of their services. Let's not do that because frankly we can't. We were not of that size. They had the scale, they had the size and they were the only cloud vendor in town. So our strategy instead was, let's do something else. Let's not compete directly with say, a particular service they're building, let's take a different strategy. What if we had a unified holistic data platform, where it's just one integrated service end to end. So think of it as Microsoft office, which contains PowerPoint, and Word, and Excel and even Access, if you want to use it. What if we build that and AWS has this really amazing knack for releasing things, you know services, lots of them, every reinvent. And they're sort of a DevOps person's dream and you can stitch these together and you know you have to be technical. How do we elevate that and make it simpler and integrate it? That was our original strategy and it resonated with a segment of the market. And the reason it worked with AWS so that we wouldn't butt heads with AWS was because we weren't a direct replacement for this service or for that service, we were taking a different approach. And AWS, because credit goes to them, they're so customer obsessed, they would actually do what's right for the customer. So if the customer said we want this unified thing, their sellers would actually say, okay, so then you should use Databricks. So they truly are customer obsessed in that way. And I really mean it, John. Things have changed over the years. They're not the only cloud anymore. You know, Azure is real, GCP is real, there's also Alibaba. And now over 70% of our customers are on more than one cloud. So now what we hear from them is, not only want, do we want a simplified, unified thing, but we want it also to work across the clouds. Because those of them that are seriously considering multiple clouds, they don't want to use a service on cloud one and then use a similar service on cloud two. But it's a little bit different. And now they have to do twice the work to make it work. You know, John, it's hard enough as it is, like it's this data stuff and analytics. It's not a walk in the park, you know. You hire an administrator in the back office that clicks a button and its just, now you're a data driven digital transformed company. It's hard. If you now have to do it again on the second cloud with different set of services and then again on a third cloud with a different set of services. That's very, very costly. So the strategy then has changed that, how do we take that unified simple approach and make it also the same and standardize across the clouds, but then also integrate it as far down as we can on each of the clouds. So that you're not giving up any of the benefits that the particular cloud has. >> Yeah, I think one of the things that we see, and I want get your reaction to this, is this rise of the super cloud as we call it. I think you were involved in the Sky paper that I saw your position paper came out after we had introduced Super Cloud, which is great. Congratulations to the Berkeley team, wearing the hat here. But you guys are, I think a driver of this because you're creating the need for these things. You're saying, okay, we went on one cloud with AWS and you didn't hide that. And now you're publicly saying there's other clouds too, increased ham for your business. And customers have multiple clouds in their infrastructure for the best of breed that they have. Okay, get that. But there's still a challenge around the innovation, growth that's still around the corner. We still have a supply chain problem, we still have skill gaps. You know, you guys are unique at Databricks as other these big examples of super clouds that are developing. Enterprises don't have the Databricks kind of talent. They need, they need turnkey solutions. So Adam and the team at Amazon are promoting, you know, more solution oriented approaches higher up on the stack. You're starting to see kind of like, I won't say templates, but you know, almost like application specific headless like, low code, no code capability to accelerate clients who are wanting to write code for the modern error. Right, so this kind of, and then now you, as you guys pointed out with these common services, you're pushing the envelope. So you're saying, hey, I need to compete, I don't want to go to my customers and have them to have a staff or this cloud and this cloud and this cloud because they don't have the staff. Or if they do, they're very unique. So what's your reaction? Because this kind is the, it kind of shows your leadership as a partner of AWS and the clouds, but also highlights I think what's coming. But you share your reaction. >> Yeah, look, it's, first of all, you know, I wish I could take credit for this but I can't because it's really the customers that have decided to go on multiple clouds. You know, it's not Databricks that you know, push this or some other vendor, you know, that, Snowflake or someone who pushed this and now enterprises listened to us and they picked two clouds. That's not how it happened. The enterprises picked two clouds or three clouds themselves and we can get into why, but they did that. So this largely just happened in the market. We as data platforms responded to what they're then saying, which is they're saying, "I don't want to redo this again on the other cloud." So I think the writing is on the wall. I think it's super obvious what's going to happen next. They will say, "Any service I'm using, it better work exactly the same on all the clouds." You know, that's what's going to happen. So in the next five years, every enterprise will say, "I'm going to use the service, but you better make sure that this service works equally well on all of the clouds." And obviously the multicloud vendors like us, are there to do that. But I actually think that what you're going to see happening is that you're going to see the cloud vendors changing the existing services that they have to make them work on the other clouds. That's what's goin to happen, I think. >> Yeah, and I think I would add that, first of all, I agree with you. I think that's going to be a forcing function. Because I think you're driving it. You guys are in a way, one, are just an actor in the driving this because you're on the front end of this and there are others and there will be people following. But I think to me, I'm a cloud vendor, I got to differentiate. Adam, If I'm Adam Saleski, I got to say, "Hey, I got to differentiate." So I don't wan to get stuck in the middle, so to speak. Am I just going to innovate on the hardware AKA infrastructure or am I going to innovate at the higher level services? So what we're talking about here is the tail of two clouds within Amazon, for instance. So do I innovate on the silicon and get low level into the physics and squeeze performance out of the hardware and infrastructure? Or do I focus on ease of use at the top of the stack for the developers? So again, there's a channel of two clouds here. So I got to ask you, how do they differentiate? Number one and number two, I never heard a developer ever say, "I want to run my app or workload on the slower cloud." So I mean, you know, back when we had PCs you wanted to go, "I want the fastest processor." So again, you can have common level services, but where is that performance differentiation with the cloud? What do the clouds do in your opinion? >> Yeah, look, I think it's pretty clear. I think that it's, this is, you know, no surprise. Probably 70% or so of the revenue is in the lower infrastructure layers, compute, storage, networking. And they have to win that. They have to be competitive there. As you said, you can say, oh you know, I guess my CPUs are slower than the other cloud, but who cares? I have amazing other services which only work on my cloud by the way, right? That's not going to be a winning recipe. So I think all three are laser focused on, we going to have specialized hardware and the nuts and bolts of the infrastructure, we can do it better than the other clouds for sure. And you can see lots of innovation happening there, right? The Graviton chips, you know, we see huge price performance benefits in those chips. I mean it's real, right? It's basically a 20, 30% free lunch. You know, why wouldn't you, why wouldn't you go for it there? There's no downside. You know, there's no, "got you" or no catch. But we see Azure doing the same thing now, they're also building their own chips and we know that Google builds specialized machine learning chips, TPU, Tenor Processing Units. So their legs are focused on that. I don't think they can give up that or focused on higher levels if they had to pick bets. And I think actually in the next few years, most of us have to make more, we have to be more deliberate and calculated in the picks we do. I think in the last five years, most of us have said, "We'll do all of it." You know. >> Well you made a good bet with Spark, you know, the duke was pretty obvious trend that was, everyone was shut on that bandwagon and you guys picked a big bet with Spark. Look what happened with you guys? So again, I love this betting kind of concept because as the world matures, growth slows down and shifts and that next wave of value coming in, AKA customers, they're going to integrate with a new ecosystem. A new kind of partner network for AWS and the other clouds. But with aws they're going to need to nurture the next Databricks. They're going to need to still provide that SaaS, ISV like experience for, you know, a basic software hosting or some application. But I go to get your thoughts on this idea of multiple clouds because if I'm a developer, the old days was, old days, within our decade, full stack developer- >> It was two years ago, yeah (John laughing) >> This is a decade ago, full stack and then the cloud came in, you kind had the half stack and then you would do some things. It seems like the clouds are trying to say, we want to be the full stack or not. Or is it still going to be, you know, I'm an application like a PC and a Mac, I'm going to write the same application for both hardware. I mean what's your take on this? Are they trying to do full stack and you see them more like- >> Absolutely. I mean look, of course they're going, they have, I mean they have over 300, I think Amazon has over 300 services, right? That's not just compute, storage, networking, it's the whole stack, right? But my key point is, I think they have to nail the core infrastructure storage compute networking because the three clouds that are there competing, they're formidable companies with formidable balance sheets and it doesn't look like any of them is going to throw in the towel and say, we give up. So I think it's going to intensify. And given that they have a 70% revenue on that infrastructure layer, I think they, if they have to pick their bets, I think they'll focus it on that infrastructure layer. I think the layer above where they're also placing bets, they're doing that, the full stack, right? But there I think the demand will be, can you make that work on the other clouds? And therein lies an innovator's dilemma because if I make it work on the other clouds, then I'm foregoing that 70% revenue of the infrastructure. I'm not getting it. The other cloud vendor is going to get it. So should I do that or not? Second, is the other cloud vendor going to be welcoming of me making my service work on their cloud if I am a competing cloud, right? And what kind of terms of service are I giving me? And am I going to really invest in doing that? And I think right now we, you know, most, the vast, vast, vast majority of the services only work on the one cloud that you know, it's built on. It doesn't work on others, but this will shift. >> Yeah, I think the innovators dilemma is also very good point. And also add, it's an integrators dilemma too because now you talk about integration across services. So I believe that the super cloud movement's going to happen before Sky. And I think what explained by that, what you guys did and what other companies are doing by representing advanced, I call platform engineering, refactoring an existing market really fast, time to value and CAPEX is, I mean capital, market cap is going to be really fast. I think there's going to be an opportunity for those to emerge that's going to set the table for global multicloud ultimately in the future. So I think you're going to start to see the same pattern of what you guys did get in, leverage the hell out of it, use it, not in the way just to host, but to refactor and take down territory of markets. So number one, and then ultimately you get into, okay, I want to run some SLA across services, then there's a little bit more complication. I think that's where you guys put that beautiful paper out on Sky Computing. Okay, that makes sense. Now if you go to today's market, okay, I'm betting on Amazon because they're the best, this is the best cloud win scenario, not the most robust cloud. So if I'm a developer, I want the best. How do you look at their bet when it comes to data? Because now they've got machine learning, Swami's got a big keynote on Wednesday, I'm expecting to see a lot of AI and machine learning. I'm expecting to hear an end to end data story. This is what you do, so as a major partner, how do you view the moves Amazon's making and the bets they're making with data and machine learning and AI? >> First I want to lift off my hat to AWS for being customer obsessed. So I know that if a customer wants Databricks, I know that AWS and their sellers will actually help us get that customer deploy Databricks. Now which of the services is the customer going to pick? Are they going to pick ours or the end to end, what Swami is going to present on stage? Right? So that's the question we're getting. But I wanted to start with by just saying, their customer obsessed. So I think they're going to do the right thing for the customer and I see the evidence of it again and again and again. So kudos to them. They're amazing at this actually. Ultimately our bet is, customers want this to be simple, integrated, okay? So yes there are hundreds of services that together give you the end to end experience and they're very customizable that AWS gives you. But if you want just something simply integrated that also works across the clouds, then I think there's a special place for Databricks. And I think the lake house approach that we have, which is an integrated, completely integrated, we integrate data lakes with data warehouses, integrate workflows with machine learning, with real time processing, all these in one platform. I think there's going to be tailwinds because I think the most important thing that's going to happen in the next few years is that every customer is going to now be obsessed, given the recession and the environment we're in. How do I cut my costs? How do I cut my costs? And we learn this from the customers they're adopting the lake house because they're thinking, instead of using five vendors or three vendors, I can simplify it down to one with you and I can cut my cost. So I think that's going to be one of the main drivers of why people bet on the lake house because it helps them lower their TCO; Total Cost of Ownership. And it's as simple as that. Like I have three things right now. If I can get the same job done of those three with one, I'd rather do that. And by the way, if it's three or four across two clouds and I can just use one and it just works across two clouds, I'm going to do that. Because my boss is telling me I need to cut my budget. >> (indistinct) (John laughing) >> Yeah, and I'd rather not to do layoffs and they're asking me to do more. How can I get smaller budgets, not lay people off and do more? I have to cut, I have to optimize. What's happened in the last five, six years is there's been a huge sprawl of services and startups, you know, you know most of them, all these startups, all of them, all the activity, all the VC investments, well those companies sold their software, right? Even if a startup didn't make it big, you know, they still sold their software to some vendors. So the ecosystem is now full of lots and lots and lots and lots of different software. And right now people are looking, how do I consolidate, how do I simplify, how do I cut my costs? >> And you guys have a great solution. You're also an arms dealer and a innovator. So I have to ask this question, because you're a professor of the industry as well as at Berkeley, you've seen a lot of the historical innovations. If you look at the moment we're in right now with the recession, okay we had COVID, okay, it changed how people work, you know, people working at home, provisioning VLAN, all that (indistinct) infrastructure, okay, yeah, technology and cloud health. But we're in a recession. This is the first recession where the Amazon and the other cloud, mainly Amazon Web Services is a major economic puzzle in the piece. So they were never around before, even 2008, they were too small. They're now a major economic enabler, player, they're serving startups, enterprises, they have super clouds like you guys. They're a force and the people, their customers are cutting back but also they can also get faster. So agility is now an equation in the economic recovery. And I want to get your thoughts because you just brought that up. Customers can actually use the cloud and Databricks to actually get out of the recovery because no one's going to say, stop making profit or make more profit. So yeah, cut costs, be more efficient, but agility's also like, let's drive more revenue. So in this digital transformation, if you take this to conclusion, every company transforms, their company is the app. So their revenue is tied directly to their technology deployment. What's your reaction and comment to that because this is a new historical moment where cloud and scale and data, actually could be configured in a way to actually change the nature of a business in such a short time. And with the recession looming, no one's got time to wait. >> Yeah, absolutely. Look, the secular tailwind in the market is that of, you know, 10 years ago it was software is eating the world, now it's AI's going to eat all of software software. So more and more we're going to have, wherever you have software, which is everywhere now because it's eaten the world, it's going to be eaten up by AI and data. You know, AI doesn't exist without data so they're synonymous. You can't do machine learning if you don't have data. So yeah, you're going to see that everywhere and that automation will help people simplify things and cut down the costs and automate more things. And in the cloud you can also do that by changing your CAPEX to OPEX. So instead of I invest, you know, 10 million into a data center that I buy, I'm going to have headcount to manage the software. Why don't we change this to OPEX? And then they are going to optimize it. They want to lower the TCO because okay, it's in the cloud. but I do want the costs to be much lower that what they were in the previous years. Last five years, nobody cared. Who cares? You know what it costs. You know, there's a new brave world out there. Now there's like, no, it has to be efficient. So I think they're going to optimize it. And I think this lake house approach, which is an integration of the lakes and the warehouse, allows you to rationalize the two and simplify them. It allows you to basically rationalize away the data warehouse. So I think much faster we're going to see the, why do I need the data warehouse? If I can get the same thing done with the lake house for fraction of the cost, that's what's going to happen. I think there's going to be focus on that simplification. But I agree with you. Ultimately everyone knows, everybody's a software company. Every company out there is a software company and in the next 10 years, all of them are also going to be AI companies. So that is going to continue. >> (indistinct), dev's going to stop. And right sizing right now is a key economic forcing function. Final question for you and I really appreciate you taking the time. This year Reinvent, what's the bumper sticker in your mind around what's the most important industry dynamic, power dynamic, ecosystem dynamic that people should pay attention to as we move from the brave new world of okay, I see cloud, cloud operations. I need to really make it structurally change my business. How do I, what's the most important story? What's the bumper sticker in your mind for Reinvent? >> Bumper sticker? lake house 24. (John laughing) >> That's data (indistinct) bumper sticker. What's the- >> (indistinct) in the market. No, no, no, no. You know, it's, AWS talks about, you know, all of their services becoming a lake house because they want the center of the gravity to be S3, their lake. And they want all the services to directly work on that, so that's a lake house. We're Bumper see Microsoft with Synapse, modern, you know the modern intelligent data platform. Same thing there. We're going to see the same thing, we already seeing it on GCP with Big Lake and so on. So I actually think it's the how do I reduce my costs and the lake house integrates those two. So that's one of the main ways you can rationalize and simplify. You get in the lake house, which is the name itself is a (indistinct) of two things, right? Lake house, "lake" gives you the AI, "house" give you the database data warehouse. So you get your AI and you get your data warehousing in one place at the lower cost. So for me, the bumper sticker is lake house, you know, 24. >> All right. Awesome Ali, well thanks for the exclusive interview. Appreciate it and get to see you. Congratulations on your success and I know you guys are going to be fine. >> Awesome. Thank you John. It's always a pleasure. >> Always great to chat with you again. >> Likewise. >> You guys are a great team. We're big fans of what you guys have done. We think you're an example of what we call "super cloud." Which is getting the hype up and again your paper speaks to some of the innovation, which I agree with by the way. I think that that approach of not forcing standards is really smart. And I think that's absolutely correct, that having the market still innovate is going to be key. standards with- >> Yeah, I love it. We're big fans too, you know, you're doing awesome work. We'd love to continue the partnership. >> So, great, great Ali, thanks. >> Take care (outro music)

(outro music) >> Hey, I'm John Furrier, here with an exclusive interview with Ali Ghodsi, who's the CEO of Databricks. Ali, great to see you. Preview for reinvent. We're going to launch this story, exclusive Databricks material on the notes, after the keynotes prior to the keynotes and after the keynotes that reinvent. So great to see you. You know, you've been a partner of AWS for a very, very long time. I think five years ago, I think I first interviewed you, you were one of the first to publicly declare that this was a place to build a company on and not just post an application, but refactor capabilities to create, essentially a platform in the cloud, on the cloud. Not just an ISV; Independent Software Vendor, kind of an old term, we're talking about real platform like capability to change the game. Can you talk about your experience as an AWS partner? >> Yeah, look, so we started in 2013. I swiped my personal credit card on AWS and some of my co-founders did the same. And we started building. And we were excited because we just thought this is a much better way to launch a company because you can just much faster get time to market and launch your thing and you can get the end users much quicker access to the thing you're building. So we didn't really talk to anyone at AWS, we just swiped a credit card. And eventually they told us, "Hey, do you want to buy extra support?" "You're asking a lot of advanced questions from us." "Maybe you want to buy our advanced support." And we said, no, no, no, no. We're very advanced ourselves, we know what we're doing. We're not going to buy any advanced support. So, you know, we just built this, you know, startup from nothing on AWS without even talking to anyone there. So at some point, I think around 2017, they suddenly saw this company with maybe a hundred million ARR pop up on their radar and it's driving massive amounts of compute, massive amounts of data. And it took a little bit in the beginning just us to get to know each other because as I said, it's like we were not on their radar and we weren't really looking, we were just doing our thing. And then over the years the partnership has deepened and deepened and deepened and then with, you know, Andy (indistinct) really leaning into the partnership, he mentioned us at Reinvent. And then we sort of figured out a way to really integrate the two service, the Databricks platform with AWS . And today it's an amazing partnership. You know, we directly connected with the general managers for the services. We're connected at the CEO level, you know, the sellers get compensated for pushing Databricks, we're, we have multiple offerings on their marketplace. We have a native offering on AWS. You know, we're prominently always sort of marketed and you know, we're aligned also vision wise in what we're trying to do. So yeah, we've come a very, very long way. >> Do you consider yourself a SaaS app or an ISV or do you see yourself more of a platform company because you have customers. How would you categorize your category as a company? >> Well, it's a data platform, right? And actually the, the strategy of the Databricks is take what's otherwise five, six services in the industry or five, six different startups, but do them as part of one data platform that's integrated. So in one word, the strategy of data bricks is "unification." We call it the data lake house. But really the idea behind the data lake house is that of unification, or in more words it's, "The whole is greater than the sum of its parts." So you could actually go and buy five, six services out there or actually use five, six services from the cloud vendors, stitch it together and it kind of resembles Databricks. Our power is in doing those integrated, together in a way in which it's really, really easy and simple to use for end users. So yeah, we're a data platform. I wouldn't, you know, ISV that's a old term, you know, Independent Software Vendor. You know, I think, you know, we have actually a whole slew of ISVs on top of Databricks, that integrate with our platform. And you know, in our marketplace as well as in our partner connect, we host those ISVs that then, you know, work on top of the data that we have in the Databricks, data lake house. >> You know, I think one of the things your journey has been great to document and watch from the beginning. I got to give you guys credit over there and props, congratulations. But I think you're the poster child as a company to what we see enterprises doing now. So go back in time when you guys swiped a credit card, you didn't need attending technical support because you guys had brains, you were refactoring, rethinking. It wasn't just banging out software, you had, you were doing some complex things. It wasn't like it was just write some software hosted on server. It was really a lot more. And as a result your business worth billions of dollars. I think 38 billion or something like that, big numbers, big numbers of great revenue growth as well, billions in revenue. You have customers, you have an ecosystem, you have data applications on top of Databricks. So in a way you're a cloud on top of the cloud. So is there a cloud on top of the cloud? So you have ISVs, Amazon has ISVs. Can you take us through what this means and at this point in history, because this seems to be an advanced version of benefits of platforming and refactoring, leveraging say AWS. >> Yeah, so look, when we started, there was really only one game in town. It was AWS. So it was one cloud. And the strategy of the company then was, well Amazon had this beautiful set of services that they're building bottom up, they have storage, compute, networking, and then they have databases and so on. But it's a lot of services. So let us not directly compete with AWS and try to take out one of their services. Let's not do that because frankly we can't. We were not of that size. They had the scale, they had the size and they were the only cloud vendor in town. So our strategy instead was, let's do something else. Let's not compete directly with say, a particular service they're building, let's take a different strategy. What if we had a unified holistic data platform, where it's just one integrated service end to end. So think of it as Microsoft office, which contains PowerPoint, and Word, and Excel and even Access, if you want to use it. What if we build that and AWS has this really amazing knack for releasing things, you know services, lots of them, every reinvent. And they're sort of a DevOps person's dream and you can stitch these together and you know you have to be technical. How do we elevate that and make it simpler and integrate it? That was our original strategy and it resonated with a segment of the market. And the reason it worked with AWS so that we wouldn't butt heads with AWS was because we weren't a direct replacement for this service or for that service, we were taking a different approach. And AWS, because credit goes to them, they're so customer obsessed, they would actually do what's right for the customer. So if the customer said we want this unified thing, their sellers would actually say, okay, so then you should use Databricks. So they truly are customer obsessed in that way. And I really mean it, John. Things have changed over the years. They're not the only cloud anymore. You know, Azure is real, GCP is real, there's also Alibaba. And now over 70% of our customers are on more than one cloud. So now what we hear from them is, not only want, do we want a simplified, unified thing, but we want it also to work across the clouds. Because those of them that are seriously considering multiple clouds, they don't want to use a service on cloud one and then use a similar service on cloud two. But it's a little bit different. And now they have to do twice the work to make it work. You know, John, it's hard enough as it is, like it's this data stuff and analytics. It's not a walk in the park, you know. You hire an administrator in the back office that clicks a button and its just, now you're a data driven digital transformed company. It's hard. If you now have to do it again on the second cloud with different set of services and then again on a third cloud with a different set of services. That's very, very costly. So the strategy then has changed that, how do we take that unified simple approach and make it also the same and standardize across the clouds, but then also integrate it as far down as we can on each of the clouds. So that you're not giving up any of the benefits that the particular cloud has. >> Yeah, I think one of the things that we see, and I want get your reaction to this, is this rise of the super cloud as we call it. I think you were involved in the Sky paper that I saw your position paper came out after we had introduced Super Cloud, which is great. Congratulations to the Berkeley team, wearing the hat here. But you guys are, I think a driver of this because you're creating the need for these things. You're saying, okay, we went on one cloud with AWS and you didn't hide that. And now you're publicly saying there's other clouds too, increased ham for your business. And customers have multiple clouds in their infrastructure for the best of breed that they have. Okay, get that. But there's still a challenge around the innovation, growth that's still around the corner. We still have a supply chain problem, we still have skill gaps. You know, you guys are unique at Databricks as other these big examples of super clouds that are developing. Enterprises don't have the Databricks kind of talent. They need, they need turnkey solutions. So Adam and the team at Amazon are promoting, you know, more solution oriented approaches higher up on the stack. You're starting to see kind of like, I won't say templates, but you know, almost like application specific headless like, low code, no code capability to accelerate clients who are wanting to write code for the modern error. Right, so this kind of, and then now you, as you guys pointed out with these common services, you're pushing the envelope. So you're saying, hey, I need to compete, I don't want to go to my customers and have them to have a staff or this cloud and this cloud and this cloud because they don't have the staff. Or if they do, they're very unique. So what's your reaction? Because this kind is the, it kind of shows your leadership as a partner of AWS and the clouds, but also highlights I think what's coming. But you share your reaction. >> Yeah, look, it's, first of all, you know, I wish I could take credit for this but I can't because it's really the customers that have decided to go on multiple clouds. You know, it's not Databricks that you know, push this or some other vendor, you know, that, Snowflake or someone who pushed this and now enterprises listened to us and they picked two clouds. That's not how it happened. The enterprises picked two clouds or three clouds themselves and we can get into why, but they did that. So this largely just happened in the market. We as data platforms responded to what they're then saying, which is they're saying, "I don't want to redo this again on the other cloud." So I think the writing is on the wall. I think it's super obvious what's going to happen next. They will say, "Any service I'm using, it better work exactly the same on all the clouds." You know, that's what's going to happen. So in the next five years, every enterprise will say, "I'm going to use the service, but you better make sure that this service works equally well on all of the clouds." And obviously the multicloud vendors like us, are there to do that. But I actually think that what you're going to see happening is that you're going to see the cloud vendors changing the existing services that they have to make them work on the other clouds. That's what's goin to happen, I think. >> Yeah, and I think I would add that, first of all, I agree with you. I think that's going to be a forcing function. Because I think you're driving it. You guys are in a way, one, are just an actor in the driving this because you're on the front end of this and there are others and there will be people following. But I think to me, I'm a cloud vendor, I got to differentiate. Adam, If I'm Adam Saleski, I got to say, "Hey, I got to differentiate." So I don't wan to get stuck in the middle, so to speak. Am I just going to innovate on the hardware AKA infrastructure or am I going to innovate at the higher level services? So what we're talking about here is the tail of two clouds within Amazon, for instance. So do I innovate on the silicon and get low level into the physics and squeeze performance out of the hardware and infrastructure? Or do I focus on ease of use at the top of the stack for the developers? So again, there's a channel of two clouds here. So I got to ask you, how do they differentiate? Number one and number two, I never heard a developer ever say, "I want to run my app or workload on the slower cloud." So I mean, you know, back when we had PCs you wanted to go, "I want the fastest processor." So again, you can have common level services, but where is that performance differentiation with the cloud? What do the clouds do in your opinion? >> Yeah, look, I think it's pretty clear. I think that it's, this is, you know, no surprise. Probably 70% or so of the revenue is in the lower infrastructure layers, compute, storage, networking. And they have to win that. They have to be competitive there. As you said, you can say, oh you know, I guess my CPUs are slower than the other cloud, but who cares? I have amazing other services which only work on my cloud by the way, right? That's not going to be a winning recipe. So I think all three are laser focused on, we going to have specialized hardware and the nuts and bolts of the infrastructure, we can do it better than the other clouds for sure. And you can see lots of innovation happening there, right? The Graviton chips, you know, we see huge price performance benefits in those chips. I mean it's real, right? It's basically a 20, 30% free lunch. You know, why wouldn't you, why wouldn't you go for it there? There's no downside. You know, there's no, "got you" or no catch. But we see Azure doing the same thing now, they're also building their own chips and we know that Google builds specialized machine learning chips, TPU, Tenor Processing Units. So their legs are focused on that. I don't think they can give up that or focused on higher levels if they had to pick bets. And I think actually in the next few years, most of us have to make more, we have to be more deliberate and calculated in the picks we do. I think in the last five years, most of us have said, "We'll do all of it." You know. >> Well you made a good bet with Spark, you know, the duke was pretty obvious trend that was, everyone was shut on that bandwagon and you guys picked a big bet with Spark. Look what happened with you guys? So again, I love this betting kind of concept because as the world matures, growth slows down and shifts and that next wave of value coming in, AKA customers, they're going to integrate with a new ecosystem. A new kind of partner network for AWS and the other clouds. But with aws they're going to need to nurture the next Databricks. They're going to need to still provide that SaaS, ISV like experience for, you know, a basic software hosting or some application. But I go to get your thoughts on this idea of multiple clouds because if I'm a developer, the old days was, old days, within our decade, full stack developer- >> It was two years ago, yeah (John laughing) >> This is a decade ago, full stack and then the cloud came in, you kind had the half stack and then you would do some things. It seems like the clouds are trying to say, we want to be the full stack or not. Or is it still going to be, you know, I'm an application like a PC and a Mac, I'm going to write the same application for both hardware. I mean what's your take on this? Are they trying to do full stack and you see them more like- >> Absolutely. I mean look, of course they're going, they have, I mean they have over 300, I think Amazon has over 300 services, right? That's not just compute, storage, networking, it's the whole stack, right? But my key point is, I think they have to nail the core infrastructure storage compute networking because the three clouds that are there competing, they're formidable companies with formidable balance sheets and it doesn't look like any of them is going to throw in the towel and say, we give up. So I think it's going to intensify. And given that they have a 70% revenue on that infrastructure layer, I think they, if they have to pick their bets, I think they'll focus it on that infrastructure layer. I think the layer above where they're also placing bets, they're doing that, the full stack, right? But there I think the demand will be, can you make that work on the other clouds? And therein lies an innovator's dilemma because if I make it work on the other clouds, then I'm foregoing that 70% revenue of the infrastructure. I'm not getting it. The other cloud vendor is going to get it. So should I do that or not? Second, is the other cloud vendor going to be welcoming of me making my service work on their cloud if I am a competing cloud, right? And what kind of terms of service are I giving me? And am I going to really invest in doing that? And I think right now we, you know, most, the vast, vast, vast majority of the services only work on the one cloud that you know, it's built on. It doesn't work on others, but this will shift. >> Yeah, I think the innovators dilemma is also very good point. And also add, it's an integrators dilemma too because now you talk about integration across services. So I believe that the super cloud movement's going to happen before Sky. And I think what explained by that, what you guys did and what other companies are doing by representing advanced, I call platform engineering, refactoring an existing market really fast, time to value and CAPEX is, I mean capital, market cap is going to be really fast. I think there's going to be an opportunity for those to emerge that's going to set the table for global multicloud ultimately in the future. So I think you're going to start to see the same pattern of what you guys did get in, leverage the hell out of it, use it, not in the way just to host, but to refactor and take down territory of markets. So number one, and then ultimately you get into, okay, I want to run some SLA across services, then there's a little bit more complication. I think that's where you guys put that beautiful paper out on Sky Computing. Okay, that makes sense. Now if you go to today's market, okay, I'm betting on Amazon because they're the best, this is the best cloud win scenario, not the most robust cloud. So if I'm a developer, I want the best. How do you look at their bet when it comes to data? Because now they've got machine learning, Swami's got a big keynote on Wednesday, I'm expecting to see a lot of AI and machine learning. I'm expecting to hear an end to end data story. This is what you do, so as a major partner, how do you view the moves Amazon's making and the bets they're making with data and machine learning and AI? >> First I want to lift off my hat to AWS for being customer obsessed. So I know that if a customer wants Databricks, I know that AWS and their sellers will actually help us get that customer deploy Databricks. Now which of the services is the customer going to pick? Are they going to pick ours or the end to end, what Swami is going to present on stage? Right? So that's the question we're getting. But I wanted to start with by just saying, their customer obsessed. So I think they're going to do the right thing for the customer and I see the evidence of it again and again and again. So kudos to them. They're amazing at this actually. Ultimately our bet is, customers want this to be simple, integrated, okay? So yes there are hundreds of services that together give you the end to end experience and they're very customizable that AWS gives you. But if you want just something simply integrated that also works across the clouds, then I think there's a special place for Databricks. And I think the lake house approach that we have, which is an integrated, completely integrated, we integrate data lakes with data warehouses, integrate workflows with machine learning, with real time processing, all these in one platform. I think there's going to be tailwinds because I think the most important thing that's going to happen in the next few years is that every customer is going to now be obsessed, given the recession and the environment we're in. How do I cut my costs? How do I cut my costs? And we learn this from the customers they're adopting the lake house because they're thinking, instead of using five vendors or three vendors, I can simplify it down to one with you and I can cut my cost. So I think that's going to be one of the main drivers of why people bet on the lake house because it helps them lower their TCO; Total Cost of Ownership. And it's as simple as that. Like I have three things right now. If I can get the same job done of those three with one, I'd rather do that. And by the way, if it's three or four across two clouds and I can just use one and it just works across two clouds, I'm going to do that. Because my boss is telling me I need to cut my budget. >> (indistinct) (John laughing) >> Yeah, and I'd rather not to do layoffs and they're asking me to do more. How can I get smaller budgets, not lay people off and do more? I have to cut, I have to optimize. What's happened in the last five, six years is there's been a huge sprawl of services and startups, you know, you know most of them, all these startups, all of them, all the activity, all the VC investments, well those companies sold their software, right? Even if a startup didn't make it big, you know, they still sold their software to some vendors. So the ecosystem is now full of lots and lots and lots and lots of different software. And right now people are looking, how do I consolidate, how do I simplify, how do I cut my costs? >> And you guys have a great solution. You're also an arms dealer and a innovator. So I have to ask this question, because you're a professor of the industry as well as at Berkeley, you've seen a lot of the historical innovations. If you look at the moment we're in right now with the recession, okay we had COVID, okay, it changed how people work, you know, people working at home, provisioning VLAN, all that (indistinct) infrastructure, okay, yeah, technology and cloud health. But we're in a recession. This is the first recession where the Amazon and the other cloud, mainly Amazon Web Services is a major economic puzzle in the piece. So they were never around before, even 2008, they were too small. They're now a major economic enabler, player, they're serving startups, enterprises, they have super clouds like you guys. They're a force and the people, their customers are cutting back but also they can also get faster. So agility is now an equation in the economic recovery. And I want to get your thoughts because you just brought that up. Customers can actually use the cloud and Databricks to actually get out of the recovery because no one's going to say, stop making profit or make more profit. So yeah, cut costs, be more efficient, but agility's also like, let's drive more revenue. So in this digital transformation, if you take this to conclusion, every company transforms, their company is the app. So their revenue is tied directly to their technology deployment. What's your reaction and comment to that because this is a new historical moment where cloud and scale and data, actually could be configured in a way to actually change the nature of a business in such a short time. And with the recession looming, no one's got time to wait. >> Yeah, absolutely. Look, the secular tailwind in the market is that of, you know, 10 years ago it was software is eating the world, now it's AI's going to eat all of software software. So more and more we're going to have, wherever you have software, which is everywhere now because it's eaten the world, it's going to be eaten up by AI and data. You know, AI doesn't exist without data so they're synonymous. You can't do machine learning if you don't have data. So yeah, you're going to see that everywhere and that automation will help people simplify things and cut down the costs and automate more things. And in the cloud you can also do that by changing your CAPEX to OPEX. So instead of I invest, you know, 10 million into a data center that I buy, I'm going to have headcount to manage the software. Why don't we change this to OPEX? And then they are going to optimize it. They want to lower the TCO because okay, it's in the cloud. but I do want the costs to be much lower that what they were in the previous years. Last five years, nobody cared. Who cares? You know what it costs. You know, there's a new brave world out there. Now there's like, no, it has to be efficient. So I think they're going to optimize it. And I think this lake house approach, which is an integration of the lakes and the warehouse, allows you to rationalize the two and simplify them. It allows you to basically rationalize away the data warehouse. So I think much faster we're going to see the, why do I need the data warehouse? If I can get the same thing done with the lake house for fraction of the cost, that's what's going to happen. I think there's going to be focus on that simplification. But I agree with you. Ultimately everyone knows, everybody's a software company. Every company out there is a software company and in the next 10 years, all of them are also going to be AI companies. So that is going to continue. >> (indistinct), dev's going to stop. And right sizing right now is a key economic forcing function. Final question for you and I really appreciate you taking the time. This year Reinvent, what's the bumper sticker in your mind around what's the most important industry dynamic, power dynamic, ecosystem dynamic that people should pay attention to as we move from the brave new world of okay, I see cloud, cloud operations. I need to really make it structurally change my business. How do I, what's the most important story? What's the bumper sticker in your mind for Reinvent? >> Bumper sticker? lake house 24. (John laughing) >> That's data (indistinct) bumper sticker. What's the- >> (indistinct) in the market. No, no, no, no. You know, it's, AWS talks about, you know, all of their services becoming a lake house because they want the center of the gravity to be S3, their lake. And they want all the services to directly work on that, so that's a lake house. We're Bumper see Microsoft with Synapse, modern, you know the modern intelligent data platform. Same thing there. We're going to see the same thing, we already seeing it on GCP with Big Lake and so on. So I actually think it's the how do I reduce my costs and the lake house integrates those two. So that's one of the main ways you can rationalize and simplify. You get in the lake house, which is the name itself is a (indistinct) of two things, right? Lake house, "lake" gives you the AI, "house" give you the database data warehouse. So you get your AI and you get your data warehousing in one place at the lower cost. So for me, the bumper sticker is lake house, you know, 24. >> All right. Awesome Ali, well thanks for the exclusive interview. Appreciate it and get to see you. Congratulations on your success and I know you guys are going to be fine. >> Awesome. Thank you John. It's always a pleasure. >> Always great to chat with you again. >> Likewise. >> You guys are a great team. We're big fans of what you guys have done. We think you're an example of what we call "super cloud." Which is getting the hype up and again your paper speaks to some of the innovation, which I agree with by the way. I think that that approach of not forcing standards is really smart. And I think that's absolutely correct, that having the market still innovate is going to be key. standards with- >> Yeah, I love it. We're big fans too, you know, you're doing awesome work. We'd love to continue the partnership. >> So, great, great Ali, thanks. >> Take care (outro music)

(intro upbeat music) >> Hello everyone and welcome to our continued coverage of AWS re:Invent here on theCUBE. My name is Savannah Peterson and I am very excited about the conversation coming up. Not only are we joined by two brilliant minds in the cloud, one of them happens to be a CUBE alumni. Please welcome Mike from AMD and Ali from Dropbox. Ali, welcome back to the show, how you been? >> Thanks Savannah. I'm doing great and really excited to be back on theCUBE. It was great discussion last time and really excited for both re:Invent and also to see how this video turns out. >> Hey, that makes two of us and probably three of us. How are you doing today, Mike? >> Doing great. It's really nice to be getting back to in-person events again and to be out solving problems with customers and partners like Dropbox. >> I know, isn't it? We've all missed each other. Was a lonely couple of years. Mike, I'm going to open it up with you. I'm sure a lot of people are curious. What's new at AMD? >> Well, there's a lot that's new at AMD, so I'll share a subset of what's new and what we've been working on. We've expanded our global coverage in Amazon EC2 with new regions and instance types. So users can deploy any application pretty much anywhere AWS has a presence. Our partner ecosystems for solutions and services has expanded quite a bit. We're currently focused on enabling partners and solutions that focus on cloud cost optimization, modernizing infrastructure, and pushing performance to the limit, especially for HPC. But the biggest buzz, of course, is around AMD's new fourth generation of our EPYC CPU Genoa. It's the world's fastest data center CPU with transformative energy efficiency and that's a really interesting combination, highest performance and most efficient. So on launch day, AWS announced their plans to roll out AMD EPYC Genoa processor-based EC2 instances. So we're pretty excited about that and that's what we'll be working on in the near term. >> Wow, that's a big deal and certainly not a casual announcement. Obviously, power and efficiency hot topics here at re:Invent but also looking at the greater impact on the planet is a big conversation we've been having here as well. So this is exciting and timely and congratulations to you and the team on all that seems to be going on. Ali, what's going on at Dropbox? >> Yeah, thanks Savannah. The Q3 2022 was actually a very strong quarter for Dropbox during a very difficult macroeconomic backdrop. Our focus has continued to be on innovation and this is around both new products and also driving multi-product adoption which is paying a lot of dividends for us, so essentially, bringing products like Dropbox Sign, DocSend, Capture, and other exciting products to our customers. On the infra side, it's all about how do we scale our infrastructure to meet the business needs, right? How do we keep up with the accelerated growth during the pandemic and also leveraging both AMD and AWS for investments in our public cloud? >> Let's talk about the cloud a bit. You are both cloud experts and I'm glad that you brought that up. We'll keep it there with Ali. When, why, and how should users leverage public cloud? >> Yeah, so Dropbox is hybrid cloud which means we are running applications both in private and public cloud and within a unique position to leverage the best of both worlds. And Savannah, this is a decision we continue to reevaluate on a regular basis. And there are really three key factors that come into play here. First is scale and scale, are we operating at a scale where customization is cost-efficient for us? Next is uniqueness. Is our workload unique compared to what the public cloud supports? And lastly, innovation. Do we have the expertise to innovate faster than public cloud or not? So based on these three key factors, we try and balance all of them and then come up with the best option for us at Dropbox. And kind of elaborating over here, things like international storage, we're leveraging public cloud, things like AI and ML, we're leveraging public cloud, but when we talk about Magic Pocket, which is our multi-exabyte storage system, that has the scale which is why we are doing that on our own private cloud. >> Wow, I think you just gave everybody a fantastic framework for thinking about their decision matrix there if nothing else. Mike, is there anything that you'd like to add to that? Anything that AMD considers when contemplating public cloud versus private? >> Yeah, so there's really three main drivers that I see when users consider when, why, and how should they leverage public cloud. Three main drivers: establishing a global footprint, accelerating product release cycles, and efficiently rightsizing infrastructure. So customers looking to establish a global footprint often turn to public cloud deployments to quickly reach their clients in workforces around the world, most importantly with minimal capital expense. I understand Dropbox uses public cloud to establish their global presence scaling out from their core data centers in North America. And then a lot of industries have tremendous pressure to accelerate product release cycles. With public cloud, organizations can immediately deploy new applications without a long site and hardware acquisition cycle and then the associated ongoing maintenance and operational overhead. And the third thing is customers that need to rightsize and dynamically scale their infrastructure and application deployments are drawn to public cloud, for example, customers that have cyclical compute or application load peaks can efficiently deploy in the cloud without overdeploying their on-prem infrastructure for most of the year which is off-peak during those off-peak times. That infrastructure idle time is a waste of resources and OPEX. So scalable rightsizing draws a lot of users to cloud deployment. >> Yeah, wow. I think there's a lot of factors to consider but also it seems like a pretty streamlined process for navigating that or at least you two both made it sound that way. Another hot topic in the space right now is security. Mike, let's start with you a little bit. What are the most important security issues for AMD right now that you can talk about? >> Yeah, sure. So, well, first of all, AWS provides a wide variety of really good security services to protect customers that are working in the cloud. Like from a processor technology perspective, there's three main security aspects to consider, two of which are common practice today and one of which AMD brings significant differentiation and value. The first two are protecting data at rest and data in transit. And these two are part of the prevalent security models of today where AMD provides distinct value and differentiation is in protecting data in use. So EPYC Milan and Genoa processors support a function called SEV-SNP and this enables users to reside and their applications to reside within their own cryptographic context and environment with data integrity protection to accomplish what's called comprehensive confidential computing. Ethics confidential computing solution is hardware-based. So it's easy to leverage, there's no code rewrite required unlike comparable solutions that are software-based that require recoding to a proprietary SDK and come with a significant performance trade-off. So with EPYC processors, you can protect your data at rest, in transit, and most importantly, in use. >> Everybody needs to protect their data everywhere it is. So I love that. That's fantastic to hear and I'm sure gives your customers a lot of confidence. What about over at Dropbox? What security issues are you facing, Ali? >> Yeah, so the first company value at Dropbox is actually being worthy of trust, and what this really means from a security perspective is how do we keep all of our users content safe? And this means keeping everything down to all of the infrastructure hardware secure. So partnering with AMD, which is one of our strongest partners out there, the new security features that AMD have and the hardware are critical for us and we are able to take advantage of some of these best security practices within our compute infrastructure by leveraging AMD's secure ship architecture. >> How important, you just touched on it a little bit, and I want to ask, how important are partnerships like the one you have with each other as you innovate at scale? Ali, you're nodding, I'm going to go to you first. >> Yeah, so like I mentioned, the partnership with with AMD is one of the strongest that we have and it just goes beyond like a regular partnership where it's just buy and sell. We talk about technology together, we talk about innovation together, we talk about partnership together, and for us, as I look look at our hybrid cloud strategy, we would not be able to get the benefits in terms of efficiency, scale, or liability performance without having a strong partner like AMD. >> That's awesome. Mike, anything you want to add there? >> I'd reiterate some of what Ali had to say. One of my favorite parts about my job is getting together with partners and customers to figure out how to optimize their applications and deployments around the world to get the most efficient use of the cloud infrastructure for servers that are based on AMD technology. In many cases, we can find 10% or better performance or cost optimization by working closely with partners like Dropbox. And then in addition, if we keep in lock step together to look at what's coming on the roadmap, by the time the latest and greatest technology is finally deployed, our customers and our partners are ready to take advantage of it. So that's the fun part of the job and I really appreciate the Dropbox's cooperation, optimizing their infrastructure, and using AMD products >> Well, what a synergistic relationship of mutual admiration and support. We love to hear it here in the tech world. Mike, last question for you. What's next for AMD? >> Well, heading into 2023, considering the current challenge macroeconomic environment and geopolitical instability, doing more with less will be top of mind for many CFOs and CEOs in 2023. And AMD can help accomplish that. AMD's EPYC processors, leadership performance, and lower EC2 retail costs can help users reduce costs without impacting performance, or the flip side of that, they can scale capacity without increasing costs. And because of EPYC's higher core counts, really high core density, applications can be deployed with fewer servers or smaller instances that has both economic and environmental benefits that reduce usage costs as well as environmental impacts. And that allows customers to optimize their application and infrastructure spend. And then the second thing that I've seen over the last couple of years and I see this trajectory continuing is increased geographic distribution of our colleagues and workforces is here to stay, people work from everywhere. In modern cross platform, collaboration platforms, that bring teams, tools, and content together have a really important role to play to enable that new, more flexible style of working. And those tools need to be really agile and easy to use. I think Dropbox is really well positioned to enable this new style of working. AMD's really happy to work closely with Dropbox to enable these modern work styles, both on premises, hybrid, and fully in the public cloud. >> Well, it sounds like a very exciting and optimistically, bright future for you all at AMD. We love to hear that here at theCUBE. Ali, what about you? What is 2023 going to hold for Dropbox? >> Yeah, so I think we're going to continue on this journey of transformation where our focus is on new products and also multi-product adoption. And from a cloud perspective, how do we continue to evolve our hybrid cloud so that we remain a competitive advantage for our business and also for our customers? I think right now, Savannah, we're in a very unique position to utilize some of the best AMD technology that's out there and that's both on premise and in the cloud. Some of the AMD Epic processors delivered the performance that we need for our hybrid cloud and we want to continue to leverage these also in public cloud which is the EC2 instances that are powered by AMD in the long run. So overall, Dropbox is looking forward to continue to evaluate some of the AMD's Genoa CPUs that are coming out but also want to continue to grow our EC2 footprint powered by AMD in the long run. >> Fantastic. Well, it sounds like this second showing here on theCUBE is just the tee up for your third and we'll definitely have to have Mike back on for the second time around to hear how things are going. Thank you both so much for taking the time today to join me here. Mike and Ali, it was fantastic getting to chat to you and thank you to our audience for tuning into theCUBE's special coverage of AWS re:Invent. My name's Savannah Peterson and I hope we can learn together soon. (outro upbeat music)

(upbeat music) >> Welcome back everyone to The Cubes coverage here in Seattle, Washington. For AWS's Marketplace Seller Conference. It's the big news within the Amazon partner network, combining with marketplace, forming the Amazon partner organization. Part of a big reorg as they grow to the next level, NextGen cloud, mid-game on the chessboard. Cube's got it covered. I'm John Furry, your host at Cube. Great guests here from Data bricks. Both cube alumni's. Jack Anderson, GM and VP of the Databricks partnership team for AWS. You handle that relationship and Joel Minick vice president of product and partner marketing. You guys have the keys to the kingdom with Databricks and AWS. Thanks for joining. Good to see you again. >> Thanks for having us back. >> Yeah, John, great to be here. >> So I feel like we're at Reinvent 2013. Small event, no stage, but there's a real shift happening with procurement. Obviously it's a no brainer on the micro, you know, people should be buying online. Self-service, Cloud Scale. But Amazon's got billions being sold through their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website, marketplace. Merge our partner organizations, have more synergy and frictionless experiences so everyone can make more money and customer's are going to be happier. >> Yeah, that's right. >> I mean, you're running relationship. You're in the middle of it. >> Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do, is to work with Amazon on these really, you know, unique use cases. >> You know, I interviewed Ali many times over the years. I remember many years ago, maybe six, seven years ago, we were talking. He's like, "we're all in on AWS." Obviously now the success of Databricks, you've got multiple clouds, see that. Customers have choice. But I remember the strategy early on. It was like, we're going to be deep. So this is, speaks volumes to the relationship you have. Years. Jack, take us through the relationship that Databricks has with AWS from a partner perspective. Joel, and from a product perspective. Because it's not like you guys are Johnny come lately, new to the scene. >> Right. >> You've been there, almost president creation of this wave. What's the relationship and how does it relate to what's going on today? >> So most people may not know that Databricks was born on AWS. We actually did our first $100 million of revenue on Amazon. And today we're obviously available on multiple clouds. But we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon, and marketplace broadens our reach. And so, we think of marketplace in three different aspects. We've got the marketplace private offer business, which we've been doing for a number of years. Matter of fact, we were driving well over a hundred percent year over year growth in private offers. And we have a nine figure business. So it's a very significant business. And when a customer uses a private offer, that private offer counts against their private pricing agreement with AWS. So they get pricing power against their private pricing. So it's really important it goes on their Amazon bill. In may we launched our pay as you go, on demand offering. And in five short months, we have well over a thousand subscribers. And what this does, is it really reduces the barriers to entry. It's low friction. So anybody in an enterprise or startup or public sector company can start to use Databricks on AWS, in a consumption based model, and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation, pilots, POCs. They're really learning the value of that first, use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offer, CPPO. Super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with Databricks on behalf of customers. >> So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data, coming in with the buyers you get. And obviously the integration piece all fitting in there. >> Exactly. >> Okay, so those are the offers, that's current, what's in marketplace today. Is that the products... What are people buying? >> Yeah. >> I mean, I guess what's the... Joel, what are people buying in the marketplace? And what does it mean for them? >> So fundamentally what they're buying is the ability to take silos out of their organization. And that is the problem that Databricks is out there to solve. Which is, when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data. And your teams are trying to use all of this data to solve really complicated problems. And as Databricks, as the Lakehouse Company, what we're helping customers do is, how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find, through the marketplace, those rapid adoption use cases where they can get rid of these data warehousing, data lake silos they've had in the past. Get their unstructured and structured data onto one data platform, an open data platform, that is no longer adherent to any proprietary formats and standards and something they can, very much, very easily, integrate into the rest of their data environment. Apply one common data governance layer on top of that. So that from the time they ingest that data, to the time they use that data, to the time they share that data, inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform, with that common governance solution, they'd being able to bring all of those use cases together. Across their real time streaming, their data engineering, their BI, their AI. All of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges they just couldn't solve before. A good example of this, you know, one of the world's now largest data streaming platforms runs on Databricks with AWS. And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses. That they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data, from what the customers are doing with their platform and the recommendations they want to push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex. But by building it on Databricks, they were able to release it in record time and have grown at a record pace to now be the number one platform. >> And this product, it's impacting product development. >> Absolutely. >> I mean, this is like the difference between lagging months of product development, to like days. >> Yes. >> Pretty much what you're getting at. >> Yes. >> So total agility. >> Mm-hmm. >> I got that. Okay, now, I'm a customer I want to buy in the marketplace, but you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from AWS's leadership, Chris, was up there speaking, and Mona was, "Hey, he's a CRO conference chief revenue officer" conversation. Which means someone's getting compensated. So, if I'm the sales rep at Databricks, what's my motion to the customer? Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Or, how do you handle it? >> Well, I'd add what Joel just talked about with, you know, with the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, it's the entire Data Bricks offering. And- >> The flagship, all the, the top stuff. >> Everything, the flagship, the complete offering. So it's not segmented. It's not a sub segment. >> Okay. >> It's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we view this two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend Databricks for the right situation. Same thing with Databricks, our sales force wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get Databricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars. You have $5 million of spend. You put that spend through the flywheel with AWS marketplace, and then you can use that in your negotiations with AWS to get better pricing overall. So that's how we view it. >> So customers are driving. This sounds like. >> Correct. For sure. >> So they're looking at this as saying, Hey, I'm going to just get purchasing power with all my relationships. Because it's a solution architectural market, right? >> Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider. If they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing power. >> Okay, Joel, we're going to date ourselves. At least I will. So back in the old days, (group laughter) It used to be, do a Barney deal with someone, Hey, let's go to market together. You got to get paper, you do a biz dev deal. And then you got to say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for Databricks, or any company is, to go find those partners and do deals, versus now Amazon is the center point for the customer. So you can still do those joint deals, but this seems to be flipping the script a little bit. >> Well, it is, but we still have vars and consulting partners that are doing implementation work. Very valuable work, advisory work, that can actually work with marketplace through the CPPO offering. So the marketplace allows multiple ways to procure your solution. >> So it doesn't change your business structure. It just makes it more efficient. >> That's correct. >> That's a great way to say it. >> Yeah, that's great. >> Okay. So, that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >> Yes. >> Absolutely. >> Economically. >> Economically, it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon. Especially when it comes back to co-selling, right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution. And our teams are working backwards from those use cases, you know, to collaborate and land them. >> Yeah. I want to get that out there. Go ahead, Joel. >> So one of the other things I might add to that too, you know, and why this is advantageous for companies like Databricks to work through the marketplace. Is it makes it so much easier for customers to deploy a solution. It's very, literally, one click through the marketplace to get Databricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. >> You know, it's interesting. I want to bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself, EDI back in the old days, you know, all that craziness. Now this is all the internet, basically through the console. I get the infrastructure side, you know, spin up and provision some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage. A little nuanced. Most enterprises aren't yet there of integrating tech, on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrators dilemma, not an innovator's dilemma. So like, I want to integrate. So now I have integration points with Databricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you got to build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or am I off because, no one's going to be buying software like they used to. They buy software to integrate it. >> Yeah, no- >> Because everything's integrated. >> I think AWS has done a great job at creating a partner ecosystem, right? To give customers the right tools for the right jobs. And those might be with third parties. Databricks is doing the same thing with our partner connect program, right? We've got customer partners like Five Tran and DBT that, you know, augment and enhance our platform. And so you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >> Yeah. It's almost like, you know, bundling and un bundling. I was talking about this with, with Dave Alante about Supercloud. Which is why wouldn't a customer want the best solution in their architecture? Period. In its class. If someone's got API security or an API gateway. Well, you know, I don't want to be forced to buy something because it's part of a suite. And that's where you see things get sub optimized. Where someone dominates a category and they have, oh, you got to buy my version of this. >> Joel and I were talking, we were actually saying, what's really important about Databricks, is that customers control the data, right? You want to comment on that? >> Yeah. I was going to say, you know, what you're pushing on there, we think is extraordinarily, you know, the way the market is going to go. Is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically, I think, really strong places, Databricks and AWS have lined up, is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the Lakehouse, one thing we've always been extremely committed to, as a company, is building the data platform on an open foundation. And we do that primarily through Delta Lake and making sure that, to Jack's point, with Databricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed Databricks to have the breadth of integrations that it has with all the other data tools out there. Because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >> When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what am I foreclosing if I go with something else that's not as open? What's the customer's downside as you think about what's around the corner in the industry? Because if you believe it's going to be open, open source, which I think open source software is the software industry, and integration is a big deal. Because software's going to be plentiful. >> Sure. >> Let's face it. It's a good time to be in software business. But Cloud's booming. So what's the downside, from your Databricks perspective? You see a buyer clicking on Databricks versus that alternative. What's potentially should they be a nervous about, down the road, if they go with a more proprietary or locked in approach? >> Yeah. >> Well, I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also, then, beholden to the pace at which that provider is able to innovate. >> Mm-hmm. >> And I think we've seen lots of times over history where, you know, a proprietary format may run ahead, for a while, on a lot of innovation. But as that market control begins to solidify, that desire to innovate begins to degrade. Whereas in the open formats- >> So extract rents versus innovation. (John laughs) >> Exactly. Yeah, exactly. >> I'll say it. >> But in the open world, you know, you have to continue to innovate. >> Yeah. >> And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you are always going to be at the forefront of what is the latest. >> You know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked with proprietary. >> Yeah. >> You know, SNA and IBM, deck net was digital. You know the rest. And then TCPIP was part of the open systems interconnect. >> Mm-hmm. >> Revolutionary (indistinct) a big part of that, as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. >> Yeah. >> But that helped inter operate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid-game, then you get the end game. You're not there at the end game yet at Cloud. But Cloud- >> There's, always some form of lock in, right? Andy Jazzy will address it, you know, when making a decision. But if you're going to make a decision you want to reduce- You don't want to be limited, right? So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now, is an AI driven business, right? And so it has to do with, can you get that data out of silos? Can you organize it and secure it? And then can you work with data scientists to feed those models? >> Yeah. >> In a very consistent manner. And so the tools of tomorrow will, to Joel's point, will be open and we want interoperability with those tools. >> And choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds, is that they have to compete directly too. Redshift competes directly with a lot of other stuff. But they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're going to be, they're onto it. This is the- >> To Amazon's credit by having these solutions that may compete with native services in marketplace, they are providing customers with choice, low price- >> And access to the core value. Which is the hardware- >> Exactly. >> Which is their platform. Okay. So I want to get you guys thought on something else I see emerging. This is, again, kind of Cube rumination moment. So on stage, Chris unpacked a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing, compensation, workflows, services behind the curtain. And one of those things he mentioned was, they talk about resellers or channel partners, depending upon what you talk about. We believe, Dave and I believe on the Cube, that the entire indirect sales channel of the industry is going to be disrupted radically. Because those players were selling hardware in the old days and software. That game is going to change. You mentioned you guys have a program, let me get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in. Which means that the old reseller channels are going to be rewritten. They're going to be refactored with this new kinds of access. Because you've got scale, you've got money and you've got product. And you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, a value added reseller or VAB or business. >> You've got to evolve. >> You got to, you got to be here. >> Yes. >> Yeah. >> How are you guys working with those partners? Because you say you have a product in your marketplace there. How do I make money if I'm a reseller with Databricks, with Amazon? Take me through that use case. >> Well I'll let Joel comment, but I think it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations. They need expertise from consulting and SI partners. If those consulting and SI partners happen to resell the solution as well. Well, that's another aspect of their business. But I really think it is the expertise that the partners bring to help customers get outcomes. >> Joel, channel big opportunity for Amazon to reimagine this. >> For sure. Yeah. And I think, you know, to your comment about how do resellers take advantage of that, I think what Jack was pushing on is spot on. Which is, it's becoming more and more about the expertise you bring to the table. And not just transacting the software. But now actually helping customers make the right choices. And we're seeing, you know, both SIs begin to be able to resell solutions and finding a lot of opportunity in that. >> Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's going to be the evolution that this goes. >> At the end of the day, it's about services, right? >> For sure. Yeah. >> I mean... >> You've got a great service. You're going to have high gross profits. >> Yeah >> Managed service provider business is alive and well, right? Because there are a number of customers that want that type of a service. >> I think that's going to be a really hot, hot button for you guys. I think being the way you guys are open, this channel, partner services model coming in, to the fold, really kind of makes for kind of that Supercloud like experience, where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on, within Databricks. >> For sure. >> On top of this ecosystem. How does that work? This is kind of like, hasn't been written up in business school and case studies yet. This is new. What is this? >> I think, you know, what it comes down to is, you're seeing ecosystems begin to evolve around the data platforms. And that's going to be one of the big, kind of, new horizons for us as we think about what drives ecosystems. It's going to be around, well, what's the data platform that I'm using? And then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around Databricks in its Lakehouse platform as well. As customers are looking at well, if I'm standing these Lakehouses up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >> I mean you think about ecosystem theory, we're living a whole nother dream. And I'm not kidding. It hasn't yet been written up and for business school case studies is that, we're now in a whole nother connective tissue, ecology thing happening. Where you have dependencies and value proposition. Economics, connectedness. So you have relationships in these ecosystems. >> And I think one of the great things about the relationships with these ecosystems, is that there's a high degree of overlap. >> Yeah. >> So you're seeing that, you know, the way that the cloud business is evolving, the ecosystem partners of Databricks, are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for you. >> Joel, Jack, I love it because you know what it means? The best ecosystem will win, if you keep it open. >> Sure, sure. >> You can see everything. If you're going to do it in the dark, you know, you don't know the outcome. I mean, this is really kind of what we're talking about. >> And John, can I just add that when I was at Amazon, we had a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? >> Yeah. >> And so there's a platform decision being made and that ecosystem is going to evolve around the platform. >> Yeah, and I totally agree. And the word innovation gets kicked around. That's why, you know, when we had our Supercloud panel, it was called the innovators dilemma, with a slash through it, called the integrater's dilemma. Innovation is the digital transformation. So- >> Absolutely. >> Like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating? If APIs are connective tissue, what's automation, what's the service messages look like? I mean, a whole nother set of, kind of thinking, goes on in these new ecosystems and these new products. >> And that thinking is, has been born in Delta Sharing, right? So the idea that you can have a multi-cloud implementation of Databricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud solution. >> Well, Databricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great growing ecosystem. And again, I think a shining example of what every enterprise is going to do. Build on top of something, operating model, get that operating model, driving revenue. >> Mm-hmm. >> Yeah. >> Whether, you're Goldman Sachs or capital one or XYZ corporation. >> S and P global, NASDAQ. >> Yeah. >> We've got, you know, the biggest verticals in the world are solving tough problems with Databricks. I think we'd be remiss because if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions. Whether it's the relationship we have with our engineering and service teams. Our marketing teams, you know, product development. And we're going to be at Reinvent. A big presence at Reinvent. We're looking forward to seeing you there, again. >> Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening. This NextGen Cloud is here. We're seeing this evolve, kind of new economics, new value propositions kind of scaling up. Producing more. So you guys are doing a great job. Thanks for coming on the Cube and taking the time. Joel, great to see you at the check. >> Thanks for having us, John. >> Okay. Cube coverage here. The world's changing as APN comes together with the marketplace for a new partner organization at Amazon web services. The Cube's got it covered. This should be a very big, growing ecosystem as this continues. Billions of being sold through the marketplace. And of course the buyers are happy as well. So we've got it all covered. I'm John Furry. your host of the cube. Thanks for watching. (upbeat music)

>>Welcome back everyone to the cubes coverage here in Seattle, Washington, AWS's marketplace seller conference. It's the big news within the Amazon partner network, combining with marketplaces, forming the Amazon partner organization, part of a big reorg as they grow the next level NextGen cloud mid-game on the chessboard. Cube's got cover. I'm John fur, host of Cub, a great guests here from data bricks, both cube alumnis, Jack Anderson, GM of the and VP of the data bricks partnership team. For ADOS, you handle that relationship and Joel Minick vice president of product and partner marketing. You guys are the, have the keys to the kingdom with data, bricks, and AWS. Thanks for joining. Thanks for good to see you again. Thanks for >>Having us back. Yeah, John, great to be here. >>So I feel like we're at reinvent 2013 small event, no stage, but there's a real shift happening with procurement. Obviously it makes it's a no brainer on the micro, you know, people should be buying online self-service cloud scale, but Amazon's got billions being sold to their marketplace. They've reorganized their partner network. You can see kind of what's going on. They've kind of figured it out. Like let's put everything together and simplify and make it less of a website marketplace merge our partner to have more synergy and friction, less experiences so everyone can make more money and customer's gonna be happier. >>Yeah, that's right. >>I mean, you're run relationship. You're in the middle of it. >>Well, Amazon's mental model here is that they want the world's best ISVs to operate on AWS so that we can collaborate and co architect on behalf of customers. And that's exactly what the APO and marketplace allow us to do is to work with Amazon on these really, you know, unique use cases. >>You know, I interviewed Ali many times over the years. I remember many years ago, I think six, maybe six, seven years ago, we were talking. He's like, we're all in ons. Obviously. Now the success of data bricks, you've got multiple clouds. See that customers have choice, but I remember the strategy early on. It was like, we're gonna be deep. So this is speaks volumes to the, the relationship you have years. Jack take us through the relationship that data bricks has with AWS from a, from a partner perspective, Joel, and from a product perspective, because it's not like you got to Johnny come lately new to the new, to the scene, right? We've been there almost president creation of this wave. What's the relationship and has it relate to what's going on today? >>So, so most people may not know that data bricks was born on AWS. We actually did our first 100 million of revenue on Amazon. And today we're obviously available on multiple clouds, but we're very fond of our Amazon relationship. And when you look at what the APN allows us to do, you know, we're able to expand our reach and co-sell with Amazon and marketplace broadens our reach. And so we think of marketplace in three different aspects. We've got the marketplace, private offer business, which we've been doing for a number of years. Matter of fact, we we're driving well over a hundred percent year over year growth in private offers and we have a nine figure business. So it's a very significant business. And when a customer uses a private offer that private offer counts against their private pricing agreement with AWS. So they get pricing power against their, their private pricing. >>So it's really important. It goes on their Amazon bill in may. We launched our pay as you go on demand offering. And in five short months, we have well over a thousand subscribers. And what this does is it really reduces the barriers to entry it's low friction. So anybody in an enterprise or startup or public sector company can start to use data bricks on AWS and pay consumption based model and have it go against their monthly bill. And so we see customers, you know, doing rapid experimentation pilots, POCs, they're, they're really learning the value of that first use case. And then we see rapid use case expansion. And the third aspect is the consulting partner, private offers C P O super important in how we involve our partner ecosystem of our consulting partners and our resellers that are able to work with data bricks on behalf of customers. >>So you got the big contracts with the private offer. You got the product market fit, kind of people iterating with data coming in with, with the buyers you go. And obviously the integration piece all fitting in there. Exactly. Exactly. Okay. So that's that those are the offers that's current and what's in marketplace today. Is that the products, what are, what are people buying? I mean, I guess what's the Joel, what are, what are people buying in the marketplace and what does it mean for >>Them? So fundamentally what they're buying is the ability to take silos out of their organization. And that's, that is the problem that data bricks is out there to solve, which is when you look across your data landscape today, you've got unstructured data, you've got structured data, you've got real time streaming data, and your teams are trying to use all of this data to solve really complicated problems. And as data bricks as the lake house company, what we're helping customers do is how do they get into the new world? How do they move to a place where they can use all of that data across all of their teams? And so we allow them to begin to find through the marketplace, those rapid adoption use cases where they can get rid of these data, warehousing data lake silos they've had in the past, get their unstructured and structured data onto one data platform and open data platform that is no longer adherent to any proprietary formats and standards and something. >>They can very much, very easily integrate into the rest of their data environment, apply one common data governance layer on top of that. So that from the time they ingest that data to the time they use that data to the time they share that data inside and outside of their organization, they know exactly how it's flowing. They know where it came from. They know who's using it. They know who has access to it. They know how it's changing. And then with that common data platform with that common governance solution, they'd being able to bring all of those use cases together across their real time, streaming their data engineering, their BI, their AI, all of their teams working on one set of data. And that lets them move really, really fast. And it also lets them solve challenges. They just couldn't solve before a good example of this, you know, one of the world's now largest data streaming platforms runs on data bricks with AWS. >>And if you think about what does it take to set that up? Well, they've got all this customer data that was historically inside of data warehouses, that they have to understand who their customers are. They have all this unstructured data, they've built their data science model, so they can do the right kinds of recommendation engines and forecasting around. And then they've got all this streaming data going back and forth between click stream data from what the customers are doing with their platform and the recommendations they wanna push back out. And if those teams were all working in individual silos, building these kinds of platforms would be extraordinarily slow and complex, but by building it on data bricks, they were able to release it in record time and have grown at, at record pace >>To not be that's product platform that's impacting product development. Absolutely. I mean, this is like the difference between lagging months of product development to like days. Yes. Pretty much what you're getting at. Yeah. So total agility. I got that. Okay. Now I'm a customer I wanna buy in the marketplace, but I also, you got direct Salesforce up there. So how do you guys look at this? Is there channel conflict? Are there comp programs? Because one of the things I heard today in on the stage from a Davis's leadership, Chris was up there speaking and, and, and moment I was, Hey, he's a CRO conference, chief revenue officer conversation, which means someone's getting compensated. So if I'm the sales rep at data bricks, what's my motion to the customer. Do I get paid? Does Amazon sell it? Take us through that. Is there channel conflict? Is there or an audio lift? >>Well, I I'd add what Joel just talked about with, with, you know, what the solution, the value of the solution our entire offering is available on AWS marketplace. So it's not a subset, the entire data bricks offering and >>The flagship, all the, the top, >>Everything, the flagship, the complete offering. So it's not, it's not segmented. It's not a sub segment. It's it's, you know, you can use all of our different offerings. Now when it comes to seller compensation, we, we, we view this two, two different ways, right? One is that AWS is also incented, right? Versus selling a native service to recommend data bricks for the right situation. Same thing with data bricks. Our Salesforce wants to do the right thing for the customer. If the customer wants to use marketplace as their procurement vehicle. And that really helps customers because if you get data bricks and five other ISVs together, and let's say each ISV is spending, you're spending a million dollars, you have $5 million of spend, you put that spend through the flywheel with AWS marketplace. And then you can use that in your negotiations with AWS to get better pricing overall. So that's how we, >>We do it. So customers are driving. This sounds like, correct. For sure. So they're looking at this as saying, Hey, I'm gonna just get purchasing power with all my relationships because it's a solution architectural market, right? >>Yeah. It makes sense. Because if most customers will have a primary and secondary cloud provider, if they can consolidate, you know, multiple ISV spend through that same primary provider, you get pricing >>Power, okay, Jill, we're gonna date ourselves. At least I will. So back in the old days, it used to be, do a Barney deal with someone, Hey, let's go to market together. You gotta get paper, you do a biz dev deal. And then you gotta say, okay, now let's coordinate our sales teams, a lot of moving parts. So what you're getting at here is that the alternative for data bricks or any company is to go find those partners and do deals versus now Amazon is the center point for the customer so that you can still do those joint deals. But this seems to be flipping the script a little bit. >>Well, it is, but we still have VAs and consulting partners that are doing implementation work very valuable work advisory work that can actually work with marketplace through the C PPO offering. So the marketplace allows multiple ways to procure your >>Solution. So it doesn't change your business structure. It just makes it more efficient. That's >>Correct. >>That's a great way to say it. Yeah, >>That's great. So that's so that's it. So that's just makes it more efficient. So you guys are actually incented to point customers to the marketplace. >>Yes, >>Absolutely. Economically. Yeah. >>E economically it's the right thing to do for the customer. It's the right thing to do for our relationship with Amazon, especially when it comes back to co-selling right? Because Amazon now is leaning in with ISVs and making recommendations for, you know, an ISV solution and our teams are working backwards from those use cases, you know, to collaborate, land them. >>Yeah. I want, I wanna get that out there. Go ahead, Joel. >>So one of the other things I might add to that too, you know, and why this is advantageous for, for companies like data bricks to, to work through the marketplace, is it makes it so much easier for customers to deploy a solution. It's, it's very, literally one click through the marketplace to get data bricks stood up inside of your environment. And so if you're looking at how do I help customers most rapidly adopt these solutions in the AWS cloud, the marketplace is a fantastic accelerator to that. You >>Know, it's interesting. I wanna bring this up and get your reaction to it because to me, I think this is the future of procurement. So from a procurement standpoint, I mean, again, dating myself EDI back in the old days, you know, all that craziness. Now this is all the, all the internet, basically through the console, I get the infrastructure side, you know, spin up and provision. Some servers, all been good. You guys have played well there in the marketplace. But now as we get into more of what I call the business apps, and they brought this up on stage little nuance, most enterprises aren't yet there of integrating tech on the business apps, into the stack. This is where I think you guys are a use case of success where you guys have been successful with data integration. It's an integrator's dilemma, not an innovator's dilemma. So like, I want to integrate, so now I have integration points with data bricks, but I want to put an app in there. I want to provision an application, but it has to be built. It's not, you don't buy it. You build, you gotta build stuff. And this is the nuance. What's your reaction to that? Am I getting this right? Or, or am I off because no, one's gonna be buying software. Like they used to, they buy software to integrate it. >>Yeah, >>No, I, cause everything's integrated. >>I think AWS has done a great job at creating a partner ecosystem, right. To give customers the right tools for the right jobs. And those might be with third parties, data bricks is doing the same thing with our partner connect program. Right. We've got customer, customer partners like five tra and D V T that, you know, augment and enhance our platform. And so you, you're looking at multi ISV architectures and all of that can be procured through the AWS marketplace. >>Yeah. It's almost like, you know, bundling and unbundling. I was talking about this with, with Dave ante about Supercloud, which is why wouldn't a customer want the best solution in their architecture period. And it's class. If someone's got API security or an API gateway. Well, you know, I don't wanna be forced to buy something because it's part of a suite and that's where you see things get suboptimized where someone dominates a category and they have, oh, you gotta buy my version of this. Yeah. >>Joel, Joel. And that's Joel and I were talking, we're actually saying what what's really important about Databricks is that customers control the data. Right? You wanna comment on that? >>Yeah. I was say the, you know what you're pushing on there we think is extraordinarily, you know, the way the market is gonna go is that customers want a lot of control over how they build their data stack. And everyone's unique in what tools are the right ones for them. And so one of the, you know, philosophically I think really strong places, data, bricks, and AWS have lined up is we both take an approach that you should be able to have maximum flexibility on the platform. And as we think about the lake house, one thing we've always been extremely committed to as a company is building the data platform on an open foundation. And we do that primarily through Delta lake and making sure that to Jack's point with data bricks, the data is always in your control. And then it's always stored in a completely open format. And that is one of the things that's allowed data bricks to have the breadth of integrations that it has with all the other data tools out there, because you're not tied into any proprietary format, but instead are able to take advantage of all the innovation that's happening out there in the open source ecosystem. >>When you see other solutions out there that aren't as open as you guys, you guys are very open by the way, we love that too. We think that's a great strategy, but what's the, what am I foreclosing? If I go with something else that's not as open what what's the customer's downside as you think about what's around the corner in the industry. Cuz if you believe it's gonna be open, open source, which I think opens our software is the software industry and integration is a big deal, cuz software's gonna be plentiful. Let's face it. It's a good time to be in software business, but cloud's booming. So what's the downside from your data bricks perspective, you see a buyer clicking on data bricks versus that alternative what's potentially is should they be a nervous about down the road if they go with a more proprietary or locked in approach? Well, >>I think the challenge with proprietary ecosystems is you become beholden to the ability of that provider to both build relationships and convince other vendors that they should invest in that format. But you're also then beholden to the pace at which that provider is able to innovate. And I think we've seen lots of times over history where, you know, a proprietary format may run ahead for a while on a lot of innovation. But as that market control begins to solidify that desire to innovate begins to, to degrade, whereas in the open format. So >>Extract rents versus innovation. Exactly. >>Yeah, exactly. >>But >>I'll say it in the open world, you know, you have to continue to innovate. Yeah. And the open source world is always innovating. If you look at the last 10 to 15 years, I challenge you to find, you know, an example where the innovation in the data and AI world is not coming from open source. And so by investing in open ecosystems, that means you were always going to be at the forefront of what is the >>Latest, you know, again, not to date myself again, but you look back at the eighties and nineties, the protocol stacked for proprietary. Yeah. You know, SNA at IBM deck net was digital, you know, the rest is, and then TCP, I P was part of the open systems, interconnect, revolutionary Oly, a big part of that as well as my school did. And so like, you know, that was, but it didn't standardize the whole stack. It stopped at IP and TCP. Yeah. But that helped interoperate, that created a nice defacto. So this is a big part of this mid game. I call it the chessboard, you know, you got opening game and mid game. Then you got the end game and we're not there. The end game yet cloud the cloud. >>There's, there's always some form of lock in, right. Andy jazzy will, will address it, you know, when making a decision. But if you're gonna make a decision you want to reduce as you don't wanna be limited. Right. So I would advise a customer that there could be limitations with a proprietary architecture. And if you look at what every customer's trying to become right now is an AI driven business. Right? And so it has to do with, can you get that data outta silos? Can you, can you organize it and secure it? And then can you work with data scientists to feed those models? Yeah. In a, in a very consistent manner. And so the tools of tomorrow will to Joel's point will be open and we want interoperability with those >>Tools and, and choice is a matter too. And I would say that, you know, the argument for why I think Amazon is not as locked in as maybe some other clouds is that they have to compete directly too. Redshift competes directly with a lot of other stuff, but they can't play the bundling game because the customers are getting savvy to the fact that if you try to bundle an inferior product with something else, it may not work great at all. And they're gonna be they're onto it. This is >>The Amazon's credit by having these, these solutions that may compete with native services in marketplace, they are providing customers with choice, low >>Price and access to the S and access to the core value. Exactly. Which the >>Hardware, which is their platform. Okay. So I wanna get you guys thought on something else. I, I see emerging, this is again kind of cube rumination moment. So on stage Chris unpacked, a lot of stuff. I mean this marketplace, they're touching a lot of hot buttons here, you know, pricing compensation, workflows services behind the curtain. And one of the things he mentioned was they talk about resellers or channel partners, depending upon what you talk about. We believe Dave and I believe on the cube that the entire indirect sales channel of the industry is gonna be disrupted radically because those players were selling hardware in the old days and software, that game is gonna change. You know, you mentioned you guys have a program, want to get your thoughts on this. We believe that once this gets set up, they can play in this game and bring their services in which means that the old reseller channels are gonna be rewritten. They're gonna be refactored with this new kinds of access. Cuz you've got scale, you've got money and you've got product and you got customers coming into the marketplace. So if you're like a reseller that sold computers to data centers or software, you know, value added reseller or V or business, >>You've gotta evolve. >>You gotta, you gotta be here. Yes. How are you guys working with those partners? Cuz you say you have a part in your marketplace there. How do I make money? If I'm a reseller with data bricks with eight Amazon, take me through that use case. >>Well I'll let Joel comment, but I think it's, it's, it's pretty straightforward, right? Customers need expertise. They need knowhow. When we're seeing customers do mass migrations to the cloud or Hadoop specific migrations or data transformation implementations, they need expertise from consulting and SI partners. If those consulting SI partners happen to resell the solution as well. Well, that's another aspect of their business, but I really think it is the expertise that the partners bring to help customers get outcomes. >>Joel, channel big opportunity for re re Amazon to reimagine this. >>For sure. Yeah. And I think, you know, to your comment about how to resellers take advantage of that, I think what Jack was pushing on is spot on, which is it's becoming more about more and more about the expertise you bring to the table and not just transacting the software, but now actually helping customers make the right choices. And we're seeing, you know, both SI begin to be able to resell solutions and finding a lot of opportunity in that. Yeah. And I think we're seeing traditional resellers begin to move into that SI model as well. And that's gonna be the evolution that >>This gets at the end of the day. It's about services for sure, for sure. You've got a great service. You're gonna have high gross profits. And >>I think that the managed service provider business is alive and well, right? Because there are a number of customers that want that, that type of a service. >>I think that's gonna be a really hot, hot button for you guys. I think being the way you guys are open this channel partner services model coming in to the fold really kind of makes for kind of that super cloudlike experience where you guys now have an ecosystem. And that's my next question. You guys have an ecosystem going on within data bricks for sure. On top of this ecosystem, how does that work? This is kinda like hasn't been written up in business school and case studies yet this is new. What is this? >>I think, you know, what it comes down to is you're seeing ecosystems begin to evolve around the data platforms and that's gonna be one of the big kind of new horizons for us as we think about what drives ecosystems it's going to be around. Well, what is the, what's the data platform that I'm using and then all the tools that have to encircle that to get my business done. And so I think there's, you know, absolutely ecosystems inside of the AWS business on all of AWS's services, across data analytics and AI. And then to your point, you are seeing ecosystems now arise around data bricks in its Lakehouse platform, as well as customers are looking at well, if I'm standing these Lakehouse up and I'm beginning to invest in this, then I need a whole set of tools that help me get that done as well. >>I mean you think about ecosystem theory, we're living a whole nother dream and I'm, and I'm not kidding. It hasn't yet been written up and for business school case studies is that we're now in a whole nother connective tissue ecology thing happening where you have dependencies and value proposition economics connectedness. So you have relationships in these ecosystems. >>And I think one of the great things about relationships with these ecosystems is that there's a high degree of overlap. Yeah. So you're seeing that, you know, the way that the cloud business is evolving, the, the ecosystem partners of data bricks are the same ecosystem partners of AWS. And so as you build these platforms out into the cloud, you're able to really take advantage of best of breed, the broadest set of solutions out there for >>You. Joel, Jack, I love it because you know what it means the best ecosystem will win. If you keep it open. Sure. You can see everything. If you're gonna do it in the dark, you know, you don't know the outcome. I mean, this is really kind we're talking about. >>And John, can I just add that when I was in Amazon, we had a, a theory that there's buyers and builders, right? There's very innovative companies that want to build things themselves. We're seeing now that that builders want to buy a platform. Right? Yeah. And so there's a platform decision being made and that ecosystem gonna evolve around the >>Platform. Yeah. And I totally agree. And, and, and the word innovation get kicks around. That's why, you know, when we had our super cloud panel was called the innovators dilemma with a slash through it called the integrated dilemma, innovation is the digital transformation. So absolutely like that becomes cliche in a way, but it really becomes more of a, are you open? Are you integrating if APIs are the connective tissue, what's automation, what's the service message look like. I mean, a whole nother set of kind of thinking goes on and these new ecosystems and these new products >>And that, and that thinking is, has been born in Delta sharing. Right? So the idea that you can have a multi-cloud implementation of data bricks, and actually share data between those two different clouds, that is the next layer on top of the native cloud >>Solution. Well, data bricks has done a good job of building on top of the goodness of, and the CapEx gift from AWS. But you guys have done a great job taking that building differentiation into the product. You guys have great customer base, great grow ecosystem. And again, I think in a shining example of what every enterprise is going to do, build on top of something operating model, get that operating model, driving revenue. >>Yeah. >>Well we, whether whether you're Goldman Sachs or capital one or XYZ corporation >>S and P global NASDAQ, right. We've got, you know, these, the biggest verticals in the world are solving tough problems with data breaks. I think we'd be remiss cuz if Ali was here, he would really want to thank Amazon for all of the investments across all of the different functions, whether it's the relationship we have with our engineering and service teams. Yeah. Our marketing teams, you know, product development and we're gonna be at reinvent the big presence of reinvent. We're looking forward to seeing you there again. >>Yeah. We'll see you guys there. Yeah. Again, good ecosystem. I love the ecosystem evolutions happening this next gen cloud is here. We're seeing this evolve kind of new economics, new value propositions kind of scaling up, producing more so you guys are doing a great job. Thanks for coming on the Cuban, taking time. Chill. Great to see you at the check. Thanks for having us. Thanks. Going. Okay. Cube coverage here. The world's changing as APN comes to give the marketplace for a new partner organization at Amazon web services, the Cube's got a covered. This should be a very big growing ecosystem as this continues, billions of being sold through the marketplace. Of course the buyers are happy as well. So we've got it all covered. I'm John furry, your host of the cube. Thanks for watching.

(bright music) >> "Supermetafragilisticexpialadotious." What's in a name? In an homage to the inimitable Charles Fitzgerald, we've chosen this title for today's session because of all the buzz surrounding "supercloud," a term that we introduced last year to signify a major architectural trend and shift that's occurring in the technology industry. Since that time, we've published numerous videos and articles on the topic, and on August 9th, kicked off "Supercloud22," an open industry event designed to advance the supercloud conversation, gathering input from more than 30 experienced technologists and business leaders in "The Cube" and broader technology community. We're talking about individuals like Benoit Dageville, Kit Colbert, Ali Ghodsi, Mohit Aron, David McJannet, and dozens of other experts. And today, we're pleased to welcome David Linthicum, who's a Chief Strategy Officer of Cloud Services at Deloitte Consulting. David is a technology visionary, a technical CTO. He's an author and a frequently sought after keynote speaker at high profile conferences like "VMware Explore" next week. David Linthicum, welcome back to "The Cube." Good to see you again. >> Oh, it's great to be here. Thanks for the invitation. Thanks for having me. >> Yeah, you're very welcome. Okay, so this topic of supercloud, what you call metacloud, has created a lot of interest. VMware calls it cross-cloud services, Snowflake calls it their data cloud, there's a lot of different names, but recently, you published a piece in "InfoWorld" where you said the following. "I really don't care what we call it, "and I really don't care if I put "my own buzzword into the mix. "However, this does not change the fact "that metacloud is perhaps the most important "architectural evolution occurring right now, "and we need to get this right out of the gate. "If we do that, who cares what it's named?" So very cool. And you also mentioned in a recent article that you don't like to put out new terms out in the wild without defining them. So what is a metacloud, or what we call supercloud? What's your definition? >> Yeah, and again, I don't care what people call it. The reality is it's the ability to have a layer of cross-cloud services. It sits above existing public cloud providers. So the idea here is that instead of building different security systems, different governance systems, different operational systems in each specific cloud provider, using whatever native features they provide, we're trying to do that in a cross-cloud way. So in other words, we're pushing out data integration, security, all these other things that we have to take care of as part of deploying a particular cloud provider. And in a multicloud scenario, we're building those in and between the clouds. And so we've been tracking this for about five years. We understood that multicloud is not necessarily about the particular public cloud providers, it's about things that you build in and between the clouds. >> Got it, okay. So I want to come back to that, to the definition, but I want to tie us to the so-called multicloud. You guys did a survey recently. We've said that multicloud was mostly a symptom of multi-vendor, Shadow Cloud, M&A, and only recently has become a strategic imperative. Now, Deloitte published a survey recently entitled "Closing the Cloud Strategy, Technology, Innovation Gap," and I'd like to explore that a little bit. And so in that survey, you showed data. What I liked about it is you went beyond what we all know, right? The old, "Our research shows that on average, "X number of clouds are used at an individual company." I mean, you had that too, but you really went deeper. You identified why companies are using multiple clouds, and you developed different categories of practitioners across 500 survey respondents. But the reasons were very clear for "why multicloud," as this becomes more strategic. Service choice scale, negotiating leverage, improved business resiliency, minimizing lock-in, interoperability of data, et cetera. So my question to you, David, is what's the problem supercloud or metacloud solves, and what's different from multicloud? >> That's a great question. The reality is that if we're... Well, supercloud or metacloud, whatever, is really something that exists above a multicloud, but I kind of view them as the same thing. It's an architectural pattern. We can name it anything. But the reality is that if we're moving to these multicloud environments, we're doing so to leverage best of breed things. In other words, best of breed technology to provide the innovators within the company to take the business to the next level, and we determine that in the survey. And so if we're looking at what a multicloud provides, it's the ability to provide different choices of different services or piece parts that allows us to build anything that we need to do. And so what we found in the survey and what we found in just practice in dealing with our clients is that ultimately, the value of cloud computing is going to be the innovation aspects. In other words, the ability to take the company to the next level from being more innovative and more disruptive in the marketplace that they're in. And the only way to do that, instead of basically leveraging the services of a particular walled garden of a single public cloud provider, is to cast a wider net and get out and leverage all kinds of services to make these happen. So if you think about that, that's basically how multicloud has evolved. In other words, it wasn't planned. They didn't say, "We're going to go do a multicloud." It was different developers and innovators in the company that went off and leveraged these cloud services, sometimes with the consent of IT leadership, sometimes not. And now we have these multitudes of different services that we're leveraging. And so many of these enterprises are going from 1000 to, say, 3000 services under management. That creates a complexity problem. We have a problem of heterogeneity, different platforms, different tools, different services, different AI technology, database technology, things like that. So the metacloud, or the supercloud, or whatever you want to call it, is the ability to deal with that complexity on the complexity's terms. And so instead of building all these various things that we have to do individually in each of the cloud providers, we're trying to do so within a cross-cloud service layer. We're trying to create this layer of technology, which removes us from dealing with the complexity of the underlying multicloud services and makes it manageable. Because right now, I think we're getting to a point of complexity we just can't operate it at the budgetary limits that we are right now. We can't keep the number of skills around, the number of operators around, to keep these things going. We're going to have to get creative in terms of how we manage these things, how we manage a multicloud. And that's where the supercloud, metacloud, whatever they want to call it, comes that. >> Yeah, and as John Furrier likes to say, in IT, we tend to solve complexity with more complexity, and that's not what we're talking about here. We're talking about simplifying, and you talked about the abstraction layer, and then it sounds like I'm inferring more. There's value that's added on top of that. And then you also said the hyperscalers are in a walled garden. So I've been asked, why aren't the hyperscalers superclouds? And I've said, essentially, they want to put your data into their cloud and keep it there. Now, that doesn't mean they won't eventually get into that. We've seen examples a little bit, Outposts, Anthos, Azure Arc, but the hyperscalers really aren't building superclouds or metaclouds, at least today, are they? >> No, they're not. And I always have the predictions for every major cloud conference that this is the conference that the hyperscaler is going to figure out some sort of a multicloud across-cloud strategy. In other words, building services that are able to operate across clouds. That really has never happened. It has happened in dribs and drabs, and you just mentioned a few examples of that, but the ability to own the space, to understand that we're not going to be the center of the universe in how people are going to leverage it, is going to be multiple things, including legacy systems and other cloud providers, and even industry clouds that are emerging these days, and SaaS providers, and all these things. So we're going to assist you in dealing with complexity, and we're going to provide the core services of being there. That hasn't happened yet. And they may be worried about conflicting their market, and the messaging is a bit different, even actively pushing back on the concept of multicloud, but the reality is the market's going to take them there. So in other words, if enough of their customers are asking for this and asking that they take the lead in building these cross-cloud technologies, even if they're participating in the stack and not being the stack, it's too compelling of a market that it's not going to drag a lot of the existing public cloud providers there. >> Well, it's going to be interesting to see how that plays out, David, because I never say never when it comes to a company like AWS, and we've seen how fast they move. And at the same time, they don't want to be commoditized. There's the layer underneath all this infrastructure, and they got this ecosystem that's adding all this tremendous value. But I want to ask you, what are the essential elements of supercloud, coming back to the definition, if you will, and what's different about metacloud, as you call it, from plain old SaaS or PaaS? What are the key elements there? >> Well, the key elements would be holistic management of all of the IT infrastructure. So even though it's sitting above a multicloud, I view metacloud, supercloud as the ability to also manage your existing legacy systems, your existing security stack, your existing network operations, basically everything that exists under the purview of IT. If you think about it, we're moving our infrastructure into the clouds, and we're probably going to hit a saturation point of about 70%. And really, if the supercloud, metacloud, which is going to be expensive to build for most of the enterprises, it needs to support these things holistically. So it needs to have all the services, that is going to be shareable across the different providers, and also existing legacy systems, and also edge computing, and IoT, and all these very diverse systems that we're building there right now. So if complexity is a core challenge to operate these things at scale and the ability to secure these things at scale, we have to have commonality in terms of security architecture and technology, commonality in terms of our directory services, commonality in terms of network operations, commonality in term of cloud operations, commonality in terms of FinOps. All these things should exist in some holistic cross-cloud layer that sits above all this complexity. And you pointed out something very profound. In other words, that is going to mean that we're hiding a lot of the existing cloud providers in terms of their interfaces and dashboards and things like that that we're dealing with today, their APIs. But the reality is that if we're able to manage these things at scale, the public cloud providers are going to benefit greatly from that. They're going to sell more services because people are going to find they're able to leverage them easier. And so in other words, if we're removing the complexity wall, which many in the industry are calling it right now, then suddenly we're moving from, say, the 25 to 30% migrated in the cloud, which most enterprises are today, to 50, 60, 70%. And we're able to do this at scale, and we're doing it at scale because we're providing some architectural optimization through the supercloud, metacloud layer. >> Okay, thanks for that. David, I just want to tap your CTO brain for a minute. At "Supercloud22," we came up with these three deployment models. Kit Colbert put forth the idea that one model would be your control planes running in one cloud, let's say AWS, but it interacts with and can manage and deploy on other clouds, the Kubernetes Cluster Management System. The second one, Mohit Aron from Cohesity laid out, where you instantiate the stack on different clouds and different cloud regions, and then you create a layer, a common interface across those. And then Snowflake was the third deployment model where it's a single global instance, it's one instantiation, and basically building out their own cloud across these regions. Help us parse through that. Do those seem like reasonable deployment models to you? Do you have any thoughts on that? >> Yeah, I mean, that's a distributed computing trick we've been doing, which is, in essence, an agent of the supercloud that's carrying out some of the cloud native functions on that particular cloud, but is, in essence, a slave to the metacloud, or the supercloud, whatever, that's able to run across the various cloud providers. In other words, when it wants to access a service, it may not go directly to that service. It goes directly to the control plane, and that control plane is responsible... Very much like Kubernetes and Docker works, that control plane is responsible for reaching out and leveraging those native services. I think that that's thinking that's a step in the right direction. I think these things unto themselves, at least initially, are going to be a very complex array of technology. Even though we're trying to remove complexity, the supercloud unto itself, in terms of the ability to build this thing that's able to operate at scale across-cloud, is going to be a collection of many different technologies that are interfacing with the public cloud providers in different ways. And so we can start putting these meta architectures together, and I certainly have written and spoke about this for years, but initially, this is going to be something that may escape the detail or the holistic nature of these meta architectures that people are floating around right now. >> Yeah, so I want to stay on this, because anytime I get a CTO brain, I like to... I'm not an engineer, but I've been around a long time, so I know a lot of buzzwords and have absorbed a lot over the years, but so you take those, the second two models, the Mohit instantiate on each cloud and each cloud region versus the Snowflake approach. I asked Benoit Dageville, "Does that mean if I'm in "an AWS east region and I want to do a query on Azure West, "I can do that without moving data?" And he said, "Yes and no." And the answer was really, "No, we actually take a subset of that data," so there's the latency problem. From those deployment model standpoints, what are the trade-offs that you see in terms of instantiating the stack on each individual cloud versus that single instance? Is there a benefit of the single instance for governance and security and simplicity, but a trade-off on latency, or am I overthinking this? >> Yeah, you hit it on the nose. The reality is that the trade-off is going to be latency and performance. If we get wiggy with the distributed nature, like the distributed data example you just provided, we have to basically separate the queries and communicate with the databases on each instance, and then reassemble the result set that goes back to the people who are recording it. And so we can do caching systems and things like that. But the reality is, if it's distributed system, we're going to have latency and bandwidth issues that are going to be limiting us. And also security issues, because if we're removing lots of information over the open internet, or even private circuits, that those are going to be attack vectors that hackers can leverage. You have to keep that in mind. We're trying to reduce those attack vectors. So it would be, in many instances, and I think we have to think about this, that we're going to keep the data in the same physical region for just that. So in other words, it's going to provide the best performance and also the most simplistic access to dealing with security. And so we're not, in essence, thinking about where the data's going, how it's moving across things, things like that. So the challenge is going to be is when you're dealing with a supercloud or metacloud is, when do you make those decisions? And I think, in many instances, even though we're leveraging multiple databases across multiple regions and multiple public cloud providers, and that's the idea of it, we're still going to localize the data for performance reasons. I mean, I just wrote a blog in "InfoWorld" a couple of months ago and talked about, people who are trying to distribute data across different public cloud providers for different reasons, distribute an application development system, things like that, you can do it. With enough time and money, you can do anything. I think the challenge is going to be operating that thing, and also providing a viable business return based on the application. And so why it may look like a good science experiment, and it's cool unto itself as an architect, the reality is the more pragmatic approach is going to be a leavitt in a single region on a single cloud. >> Very interesting. The other reason I like to talk to companies like Deloitte and experienced people like you is 'cause I can get... You're agnostic, right? I mean, you're technology agnostic, vendor agnostic. So I want to come back with another question, which is, how do you deal with what I call the lowest common denominator problem? What I mean by that is if one cloud has, let's say, a superior service... Let's take an example of Nitro and Graviton. AWS seems to be ahead on that, but let's say some other cloud isn't quite quite there yet, and you're building a supercloud or a metacloud. How do you rationalize that? Does it have to be like a caravan in the army where you slow down so all the slowest trucks can keep up, or are the ways to adjudicate that that are advantageous to hide that deficiency? >> Yeah, and that's a great thing about leveraging a supercloud or a metacloud is we're putting that management in a single layer. So as far as a user or even a developer on those systems, they shouldn't worry about the performance that may come back, because we're dealing with the... You hit the nail on the head with that one. The slowest component is the one that dictates performance. And so we have to have some sort of a performance management layer. We're also making dynamic decisions to move data, to move processing, from one server to the other to try to minimize the amount of latency that's coming from a single component. So the great thing about that is we're putting that volatility into a single domain, and it's making architectural decisions in terms of where something will run and where it's getting its data from, things are stored, things like that, based on the performance feedback that's coming back from the various cloud services that are under management. And so if you're running across clouds, it becomes even more interesting, because ultimately, you're going to make some architectural choices on the fly in terms of where that stuff runs based on the active dynamic performance that that public cloud provider is providing. So in other words, we may find that it automatically shut down a database service, say MySQL, on one cloud instance, and moved it to a MySQL instance on another public cloud provider because there was some sort of a performance issue that it couldn't work around. And by the way, it does so dynamically. Away from you making that decision, it's making that decision on your behalf. Again, this is a matter of abstraction, removing complexity, and dealing with complexity through abstraction and automation, and this is... That would be an example of fixing something with automation, self-healing. >> When you meet with some of the public cloud providers and they talk about on-prem private cloud, the general narrative from the hyperscalers is, "Well, that's not a cloud." Should on-prem be inclusive of supercloud, metacloud? >> Absolutely, I mean, and they're selling private cloud instances with the edge cloud that they're selling. The reality is that we're going to have to keep a certain amount of our infrastructure, including private clouds, on premise. It's something that's shrinking as a market share, and it's going to be tougher and tougher to justify as the public cloud providers become better and better at what they do, but we certainly have edge clouds now, and hyperscalers have examples of that where they run a instance of their public cloud infrastructure on premise on physical hardware and software. And the reality is, too, we have data centers and we have systems that just won't go away for another 20 or 30 years. They're just too sticky. They're uneconomically viable to move into the cloud. That's the core thing. It's not that we can't do it. The fact of the matter is we shouldn't do it, because there's not going to be an economic... There's not going to be an economic incentive of making that happen. So if we're going to create this meta layer or this infrastructure which is going to run across clouds, and everybody agrees on, that's what the supercloud is, we have to include the on-premise systems, including private clouds, including legacy systems. And by the way, include the rising number of IoT systems that are out there, and edge-based systems out there. So we're managing it using the same infrastructure into cloud services. So they have metadata systems and they have specialized services, and service finance and retail and things like doing risk analytics. So it gets them further down that path, but not necessarily giving them a SaaS application where they're forced into all of the business processes. We're giving you piece parts. So we'll give you 1000 different parts that are related to the finance industry. You can assemble anything you need, but the thing is, it's not going to be like building it from scratch. We're going to give you risk analytics, we're giving you the financial analytics, all these things that you can leverage within your applications how you want to leverage them. We'll maintain them. So in other words, you don't have to maintain 'em just like a cloud service. And suddenly, we can build applications in a couple of weeks that used to take a couple of months, in some cases, a couple of years. So that seems to be a large take of it moving forward. So get it up in the supercloud. Those become just other services that are under managed... That are under management on the supercloud, the metacloud. So we're able to take those services, abstract them, assemble them, use them in different applications. And the ability to manage where those services are originated versus where they're consumed is going to be managed by the supercloud layer, which, you're dealing with the governance, the service governance, the security systems, the directory systems, identity access management, things like that. They're going to get you further along down the pike, and that comes back as real value. If I'm able to build something in two weeks that used to take me two months, and I'm able to give my creators in the organization the ability to move faster, that's a real advantage. And suddenly, we are going to be valued by our digital footprint, our ability to do things in a creative and innovative way. And so organizations are able to move that fast, leveraging cloud computing for what it should be leveraged, as a true force multiplier for the business. They're going to win the game. They're going to get the most value. They're going to be around in 20 years, the others won't. >> David Linthicum, always love talking. You have a dangerous combination of business and technology expertise. Let's tease. "VMware Explore" next week, you're giving a keynote, if they're going to be there. Which day are you? >> Tuesday. Tuesday, 11 o'clock. >> All right, that's a big day. Tuesday, 11 o'clock. And David, please do stop by "The Cube." We're in Moscone West. Love to get you on and continue this conversation. I got 100 more questions for you. Really appreciate your time. >> I always love talking to people at "The Cube." Thank you very much. >> All right, and thanks for watching our ongoing coverage of "Supercloud22" on "The Cube," your leader in enterprise tech and emerging tech coverage. (bright music)

(gentle upbeat music) >> Welcome back everyone, to "theCUBE"'s live stage performance here in Palo Alto, California at "theCUBE" Studios. I'm John Furrier with Dave Vellante, kicking off our first inaugural Supercloud event. It's an editorial event, we wanted to bring together the best in the business, the smartest, the biggest, the up-and-coming startups, venture capitalists, everybody, to weigh in on this new Supercloud trend, this structural change in the cloud computing business. We're about to run the Ecosystem Speaks, which is a bunch of pre-recorded companies that wanted to get their voices on the record, so stay tuned for the rest of the day. We'll be replaying all that content and they're going to be having some really good commentary and hear what they have to say. I had a chance to interview and so did Dave. Dave, this is our closing segment where we kind of unpack everything or kind of digest and report. So much to kind of digest from the conversations today, a wide range of commentary from Supercloud operating system to developers who are in charge to maybe it's an ops problem or maybe Oracle's a Supercloud. I mean, that was debated. So so much discussion, lot to unpack. What was your favorite moments? >> Well, before I get to that, I think, I go back to something that happened at re:Invent last year. Nick Sturiale came up, Steve Mullaney from Aviatrix; we're going to hear from him shortly in the Ecosystem Speaks. Nick Sturiale's VC said "it's happening"! And what he was talking about is this ecosystem is exploding. They're building infrastructure or capabilities on top of the CapEx infrastructure. So, I think it is happening. I think we confirmed today that Supercloud is a thing. It's a very immature thing. And I think the other thing, John is that, it seems to me that the further you go up the stack, the weaker the business case gets for doing Supercloud. We heard from Marianna Tessel, it's like, "Eh, you know, we can- it was easier to just do it all on one cloud." This is a point that, Adrian Cockcroft just made on the panel and so I think that when you break out the pieces of the stack, I think very clearly the infrastructure layer, what we heard from Confluent and HashiCorp, and certainly VMware, there's a real problem there. There's a real need at the infrastructure layer and then even at the data layer, I think Benoit Dageville did a great job of- You know, I was peppering him with all my questions, which I basically was going through, the Supercloud definition and they ticked the box on pretty much every one of 'em as did, by the way Ali Ghodsi you know, the big difference there is the philosophy of Republicans and Democrats- got open versus closed, not to apply that to either one side, but you know what I mean! >> And the similarities are probably greater than differences. >> Berkely, I would probably put them on the- >> Yeah, we'll put them on the Democrat side we'll make Snowflake the Republicans. But so- but as we say there's a lot of similarities as well in terms of what their objectives are. So, I mean, I thought it was a great program and a really good start to, you know, an industry- You brought up the point about the industry consortium, asked Kit Colbert- >> Yep. >> If he thought that was something that was viable and what'd they say? That hyperscale should lead it? >> Yeah, they said hyperscale should lead it and there also should be an industry consortium to get the voices out there. And I think VMware is very humble in how they're putting out their white paper because I think they know that they can't do it all and that they do not have a great track record relative to cloud. And I think, but they have a great track record of loyal installed base ops people using VMware vSphere all the time. >> Yeah. >> So I think they need a catapult moment where they can catapult to the cloud native which they've been working on for years under Raghu and the team. So the question on VMware is in the light of Broadcom, okay, acquisition of VMware, this is an opportunity or it might not be an opportunity or it might be a spin-out or something, I just think VMware's got way too much engineering culture to be ignored, Dave. And I think- well, I'm going to watch this very closely because they can pull off some sort of rallying moment. I think they could. And then you hear the upstarts like Platform9, Rafay Systems and others they're all like, "Yes, we need to unify behind something. There needs to be some sort of standard". You know, we heard the argument of you know, more standards bodies type thing. So, it's interesting, maybe "theCUBE" could be that but we're going to certainly keep the conversation going. >> I thought one of the most memorable statements was Vittorio who said we- for VMware, we want our cake, we want to eat it too and we want to lose weight. So they have a lot of that aspirations there! (John laughs) >> And then I thought, Adrian Cockcroft said you know, the devs, they want to get married. They were marrying everybody, and then the ops team, they have to deal with the divorce. >> Yeah. >> And I thought that was poignant. It's like, they want consistency, they want standards, they got to be able to scale And Lori MacVittie, I'm not sure you agree with this, I'd have to think about it, but she was basically saying, all we've talked about is devs devs devs for the last 10 years, going forward we're going to be talking about ops. >> Yeah, and I think one of the things I learned from this day and looking back, and some kind of- I've been sauteing through all the interviews. If you zoom out, for me it was the epiphany of developers are still in charge. And I've said, you know, the developers are doing great, it's an ops security thing. Not sure I see that the way I was seeing before. I think what I learned was the refactoring pattern that's emerging, In Sik Rhee brought this up from Vertex Ventures with Marianna Tessel, it's a nuanced point but I think he's right on which is the pattern that's emerging is developers want ease-of-use tooling, they're driving the change and I think the developers in the devs ops ethos- it's never going to be separate. It's going to be DevOps. That means developers are driving operations and then security. So what I learned was it's not ops teams leveling up, it's devs redefining what ops is. >> Mm. And I think that to me is where Supercloud's going to be interesting- >> Forcing that. >> Yeah. >> Forcing the change because the structural change is open sources thriving, devs are still in charge and they still want more developers, Vittorio "we need more developers", right? So the developers are in charge and that's clear. Now, if that happens- if you believe that to be true the domino effect of that is going to be amazing because then everyone who gets on the wrong side of history, on the ops and security side, is going to be fighting a trend that may not be fight-able, you know, it might be inevitable. And so the winners are the ones that are refactoring their business like Snowflake. Snowflake is a data warehouse that had nothing to do with Amazon at first. It was the developers who said "I'm going to refactor data warehouse on AWS". That is a developer-driven refactorization and a business model. So I think that's the pattern I'm seeing is that this concept refactoring, patterns and the developer trajectory is critical. >> I thought there was another great comment. Maribel Lopez, her Lord of the Rings comment: "there will be no one ring to rule them all". Now at the same time, Kit Colbert, you know what we asked him straight out, "are you the- do you want to be the, the Supercloud OS?" and he basically said, "yeah, we do". Now, of course they're confined to their world, which is a pretty substantial world. I think, John, the reason why Maribel is so correct is security. I think security's a really hard problem to solve. You've got cloud as the first layer of defense and now you've got multiple clouds, multiple layers of defense, multiple shared responsibility models. You've got different tools for XDR, for identity, for governance, for privacy all within those different clouds. I mean, that really is a confusing picture. And I think the hardest- one of the hardest parts of Supercloud to solve. >> Yeah, and I thought the security founder Gee Rittenhouse, Piyush Sharrma from Accurics, which sold to Tenable, and Tony Kueh, former head of product at VMware. >> Right. >> Who's now an investor kind of looking for his next gig or what he is going to do next. He's obviously been extremely successful. They brought up the, the OS factor. Another point that they made I thought was interesting is that a lot of the things to do to solve the complexity is not doable. >> Yeah. >> It's too much work. So managed services might field the bit. So, and Chris Hoff mentioned on the Clouderati segment that the higher level services being a managed service and differentiating around the service could be the key competitive advantage for whoever does it. >> I think the other thing is Chris Hoff said "yeah, well, Web 3, metaverse, you know, DAO, Superclouds" you know, "Stupercloud" he called it and this bring up- It resonates because one of the criticisms that Charles Fitzgerald laid on us was, well, it doesn't help to throw out another term. I actually think it does help. And I think the reason it does help is because it's getting people to think. When you ask people about Supercloud, they automatically- it resonates with them. They play back what they think is the future of cloud. So Supercloud really talks to the future of cloud. There's a lot of aspects to it that need to be further defined, further thought out and we're getting to the point now where we- we can start- begin to say, okay that is Supercloud or that isn't Supercloud. >> I think that's really right on. I think Supercloud at the end of the day, for me from the simplest way to describe it is making sure that the developer experience is so good that the operations just happen. And Marianna Tessel said, she's investing in making their developer experience high velocity, very easy. So if you do that, you have to run on premise and on the cloud. So hybrid really is where Supercloud is going right now. It's not multi-cloud. Multi-cloud was- that was debunked on this session today. I thought that was clear. >> Yeah. Yeah, I mean I think- >> It's not about multi-cloud. It's about operationally seamless operations across environments, public cloud to on-premise, basically. >> I think we got consensus across the board that multi-cloud, you know, is a symptom Chuck Whitten's thing of multi-cloud by default versus multi- multi-cloud has not been a strategy, Kit Colbert said, up until the last couple of years. Yeah, because people said, "oh we got all these multiple clouds, what do we do with it?" and we got this mess that we have to solve. Whereas, I think Supercloud is something that is a strategy and then the other nuance that I keep bringing up is it's industries that are- as part of their digital transformation, are building clouds. Now, whether or not they become superclouds, I'm not convinced. I mean, what Goldman Sachs is doing, you know, with AWS, what Walmart's doing with Azure connecting their on-prem tools to those public clouds, you know, is that a supercloud? I mean, we're going to have to go back and really look at that definition. Or is it just kind of a SAS that spans on-prem and cloud. So, as I said, the further you go up the stack, the business case seems to wane a little bit but there's no question in my mind that from an infrastructure standpoint, to your point about operations, there's a real requirement for super- what we call Supercloud. >> Well, we're going to keep the conversation going, Dave. I want to put a shout out to our founding supporters of this initiative. Again, we put this together really fast kind of like a pilot series, an inaugural event. We want to have a face-to-face event as an industry event. Want to thank the founding supporters. These are the people who donated their time, their resource to contribute content, ideas and some cash, not everyone has committed some financial contribution but we want to recognize the names here. VMware, Intuit, Red Hat, Snowflake, Aisera, Alteryx, Confluent, Couchbase, Nutanix, Rafay Systems, Skyhigh Security, Aviatrix, Zscaler, Platform9, HashiCorp, F5 and all the media partners. Without their support, this wouldn't have happened. And there are more people that wanted to weigh in. There was more demand than we could pull off. We'll certainly continue the Supercloud conversation series here on "theCUBE" and we'll add more people in. And now, after this session, the Ecosystem Speaks session, we're going to run all the videos of the big name companies. We have the Nutanix CEOs weighing in, Aviatrix to name a few. >> Yeah. Let me, let me chime in, I mean you got Couchbase talking about Edge, Platform 9's going to be on, you know, everybody, you know Insig was poopoo-ing Oracle, but you know, Oracle and Azure, what they did, two technical guys, developers are coming on, we dig into what they did. Howie Xu from Zscaler, Paula Hansen is going to talk about going to market in the multi-cloud world. You mentioned Rajiv, the CEO of Nutanix, Ramesh is going to talk about multi-cloud infrastructure. So that's going to run now for, you know, quite some time here and some of the pre-record so super excited about that and I just want to thank the crew. I hope guys, I hope you have a list of credits there's too many of you to mention, but you know, awesome jobs really appreciate the work that you did in a very short amount of time. >> Well, I'm excited. I learned a lot and my takeaway was that Supercloud's a thing, there's a kind of sense that people want to talk about it and have real conversations, not BS or FUD. They want to have real substantive conversations and we're going to enable that on "theCUBE". Dave, final thoughts for you. >> Well, I mean, as I say, we put this together very quickly. It was really a phenomenal, you know, enlightening experience. I think it confirmed a lot of the concepts and the premises that we've put forth, that David Floyer helped evolve, that a lot of these analysts have helped evolve, that even Charles Fitzgerald with his antagonism helped to really sharpen our knives. So, you know, thank you Charles. And- >> I like his blog, by the I'm a reader- >> Yeah, absolutely. And it was great to be back in Palo Alto. It was my first time back since pre-COVID, so, you know, great job. >> All right. I want to thank all the crew and everyone. Thanks for watching this first, inaugural Supercloud event. We are definitely going to be doing more of these. So stay tuned, maybe face-to-face in person. I'm John Furrier with Dave Vellante now for the Ecosystem chiming in, and they're going to speak and share their thoughts here with "theCUBE" our first live stage performance event in our studio. Thanks for watching. (gentle upbeat music)

(light hearted music) >> Okay, welcome back to Supercloud '22. I'm John Furrier, host of theCUBE. We got Ali Ghodsi here, co-founder and CEO of Databricks. Ali, Great to see you. Thanks for spending your valuable time to come on and talk about Supercloud and the future of all the structural change that's happening in cloud computing. >> My pleasure, thanks for having me. >> Well, first of all, congratulations. We've been talking for many, many years, and I still go back to the video that we have in archive, you talking about cloud. And really, at the beginning of the big reboot, I called the post Hadoop, a revitalization of data. Congratulations, you've been cloud-first, now on multiple clouds. Congratulations to you and your team for achieving what looks like a billion dollars in annualized revenue as reported by the Wall Street Journal, so first, congratulations. >> Thank you so much, appreciate it. >> So I was talking to some young developers and I asked a random poll, what do you think about Databricks? Oh, we love those guys, they're AI and ML-native, and that's their advantage over the competition. So I pressed why. I don't think they knew why, but that's an interesting perspective. This idea of cloud native, AI/ML-native, ML Ops, this has been a big trend and it's continuing. This is a big part of how this change and this structural change is happening. How do you react to that? And how do you see Databricks evolving into this new Supercloud-like multi-cloud environment? >> Yeah, look, I think it's a continuum. It starts with having data, but they want to clean it, you know, and they want to get insights out of it. But then, eventually, you'd like to start asking questions, doing reports, maybe ask questions about what was my revenue yesterday, last week, but soon you want to start using the crystal ball, predictive technology. Okay, but what will my revenue be next week? Next quarter? Who's going to churn? And if you can finally automate that completely so that you can act on the predictions, right? So this credit card that got swiped, the AI thinks it's fraud, we're going to deny it. That's when you get real value. So we're trying to help all these organizations move through this data AI maturity curve, all the way to that, the prescriptive, automated AI machine learning. That's when you get real competitive advantage. And you know, we saw that with the fans, right? I mean, Google wouldn't be here today if it wasn't for AI. You know, we'd be using AltaVista or something. We want to help all organizations to be able to leverage data and AI that way that the fans did. >> One of the things we're looking at with supercloud and why we call it supercloud versus other things like multi-cloud is that today a lot of the successful companies have started in the cloud have been successful, but have realized and even enterprises who have gotten by accident, and maybe have done nothing with cloud have just some cloud projects on multiple clouds. So, people have multiple cloud operational things going on but it hasn't necessarily been a strategy per se. It's been more of kind of a default reaction to things but the ones that are innovating have been successful in one native cloud because the use cases that drove that got scale got value, and then they're making that super by bringing it on premise, putting in a modern data stack, for the modern application development, and kind of dealing with the things that you guys are in the middle of with data bricks is that, that is where the action is, and they don't want to go, lose the trajectory in all the economies of scale. So we're seeing another structural change where the evolutionary nature of the cloud has solved a bunch of use cases, but now other use cases are emerging that's on premises and edge that have been driven by applications because of the developer boom, that's happening. You guys are in the middle of it. What is happening with this structural change? Are people looking for the modern data stack? Are they looking for more AI? What's the, what's your perspective on this supercloud kind of position? >> Look, it started with not AR on multiple clouds, right? So multi-cloud has been a thing. It became a thing 70, 80% of our customers when you ask them, they're more than one cloud. But then soon to start realizing that, hey, you know, if I'm on multiple clouds, this data stuff is hard enough as it is. Do I want to redo it again and again with different proprietary technologies, on each of the clouds. And that's when I started thinking about let's standardize this, let's figure out a way which just works across them. That's where I think open source comes in, becomes really important. Hey, can we leverage open standards because then we can make it work in these different environments, as we said so that we can actually go super, as you said, that's one. The second thing is, can we simplify it? You know, and I think today, the data landscape is complicated. Conceptually it's simple. You have data which is essentially customer data that you have, maybe employee data. And you want to get some kind of insights from that. But how you do that is very complicated. You have to buy data warehouse, hire data analysts. You have to buy, store stuff in the Delta Lake you know, get your data engineers. If you want streaming real time thing that's another complete different set of technologies you have to buy. And then you have to stitch all these together, and you have to do again and again on every cloud. So they just want simplification. So that's why we're big believers in this Delta Lakehouse concept. Which is an open standard to simplifying this data stack and help people to just get value out of their data in any environment. So they can do that in this sort of supercloud as you call it. >> You know, we've been talking about that in previous interviews, do the heavy lifting let them get the value. I have to ask you about how you see that going forward, Because if I'm a customer, I have a lot of operational challenges. Cause the developers are are kicking butt right now. We see that clearly. Open sources growing at, and continue to be great. But ops and security teams they really care about this stuff. And most companies don't want to spin up multiple ops teams to deal with different stacks. This is one big problem that I think that's leading into the multi-cloud viability. How do you guys deal with that? How do you talk to customers when they say, I want to have less complications on operations? >> Yeah, you're absolutely right. You know, it's easy for a developer to adopt all these technologies and new things are coming out all the time. The ops teams are the ones that have to make sure this works. Doing that in multiple different environments is super hard. especially when there's a proprietary stack in each environment that's different. So they just want standardization. They want open source, that's super important. We hear that all the time from them. They want open the source technologies. They believe in the communities around it. You know, they know that source code is open. So you can also see if there's issues with it. If there's security breaches, those kind of things that they can have a community around it. So they can actually leverage that. So they're the ones that are really pushing this, and we're seeing it across the board. You know, it starts first with the digital natives you know, the companies that are, but slowly it's also now percolating to the other organizations, we're hearing across the board. >> Where are we, Ali on the innovation strategies for customers? Where are they on the trajectory around how they're building out their teams? How are they looking at the open source? How are they extending the value proposition of Databricks, and data at scale, as they start to build out their teams and operations, because some are like kind of starting, crawl, walk, run, kind of vibe. Some are big companies, they're dealing with data all the time. Where are they in their journey? What's the core issues that they're solving? What are some of the use cases that you see that are most pressing in customer? >> Yeah, what I've seen, that's really exciting about this Delta Lakehouse concept is that we're now seeing a lot of use cases around real time. So real time fraud detection, real time stock ticker pricing, anyone that's doing trading, they want that to work real time. Lots of use cases around that. Lots of use cases around how do we in real time drive more engagement on our web assets if we're a media company, right? We have all these assets how do we get people to get engaged? Stay on our sites. Continue engaging with the material we have. Those are real time use cases. And the interesting thing is, they're real time. So, you know, it's really important that you that now you don't want to recommend someone, hey, you should go check out this restaurant if they just came from that restaurant, half an hour ago. So you want it to be real time, but B, that it's also all based on machine learning. These are a lot of this is trying to predict what you want to see, what you want to do, is it fraudulent? And that's also interesting because basically more and more machine learning is coming in. So that's super exciting to see, the combination of real time and machine learning on the Lakehouse. And finally, I would say the Lakehouse is really important for this because that's where the data is flowing in. If they have to take that data that's flowing into the lake and actually copy it into a separate warehouse, that delays the real time use cases. And then it can't hit those real time deadlines. So that's another catalyst for this Lakehouse pattern. >> Would that be an example of how the metrics are changing? Cause I've been looking at some people saying, well you can tell if someone's doing well there's a lot of data being transferred. And then I was saying, well, wait a minute. Data transfer costs money, right? And time. So this is interesting dynamic, in a way you don't want to have a lot of movement, right? >> Yeah, movement actually decreases for a lot of these real time use cases. 'Cause what we saw in the past was that they would run a batch processing to process all the data. So once they process all the data. But actually if you look at the things that have changed since the data that we have yesterday it's actually not that much. So if you can actually incrementally process it in real time, you can actually reduce the cost of transfers and storage and processing. So that's actually a great point. That's also one of the main things that we're seeing with the use cases, the bill shrinks and the cost goes down, and they can process less. >> Yeah, and it'd be interesting to see how those KPIs evolve into industry metrics down the road around the supercloud of evolution. I got to ask you about the open source concept of data platforms. You guys have been a pioneer in there doing great work, kind of picking the baton off where the Hadoop World left off as Dave Vellante always points out. But if working across clouds is super important. How are you guys looking at the ability to work across the different clouds with data bricks? Are you going to build that abstraction yourself? Does data sharing and model sharing kind of come into play there? How do you see this data bricks capability across the clouds? >> 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, decades, hundreds of years, and it's going to slowly replace all proprietary code in its way. We saw that, 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 Delta Lakehouse is that slowly the open source community is building a replacement for the proprietary data warehouse, Delta 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 layout your data in the cloud. And when it comes a really important protocol called data sharing, that enables you in a 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 Databricks customer. You don't need to 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 you're playing the long game on open source. >> Absolutely. I mean, this is a force it's going to be there if if you deny it, before you know it there's going to be, something like Linux, that is going to be a threat to your propriety. >> I totally agree by the way. I was just talking to somebody the other day and they're like hey, the software industry someone made the comment, the software industry, the software industry is open source. There's no more software industry, it's called open source. It's integrations that become interesting. And I was looking at integrations now is really where the action is. And we had a panel with the Clouderati we called it, the people have been around for a long time. And it was called the innovator's dilemma. And one of the comments was it's the integrator's dilemma, not the innovator's dilemma. And this is a big part of this piece of supercloud. Can you share your thoughts on how cloud and integration need to be tightened up to really make it super? >> Actually that's a great point. I think the beauty of this is, look the ecosystem of data today is vast, there's this picture that someone puts together every year of all the different vendors and how they relate, and it gets bigger and bigger and messy and messier. So, we see customers use all kinds of different aspects of what's existing in the ecosystem and they want it to be integrated in whatever you're selling them. And that's where I think the power of open source comes in. Open source, you get integrations that people will do without you having to push it. So us, Databricks as a vendor, we don't have to go tell people please integrate with Databricks. The open source technology that we contribute to, automatically, people are integrating with it. Delta Lake has integrations with lots of different software out there and Databricks as a company doesn't have to push that. So I think open source is also another thing that really helps with the ecosystem integrations. Many of these companies in this data space actually have employees that are full-time dedicated to make sure make sure our software works well with Spark. Make sure our software works well with Delta and they contribute back to that community. And that's the way you get this sort of ecosystem to further sort of flourish. >> Well, I really appreciate your time. And I, my final question for you is, as we're kind of unpack and and kind of shape and frame supercloud for the future, how would you see a roadmap or architecture or outcome for companies that are going to clearly be in the cloud where it's open source is going to be dominating. Integrations has got to be seamless and frictionless. Abstraction layer make things super easy and take away the complexity. What is supercloud to them? What does the outcome look like? How would you define a supercloud environment for an enterprise? >> Yeah, for me, it's the simplification that you get where you standardize an open source. You get your data in one place, in one format in one standardized way, and then you can get your insights from it, without having to buy lots of different idiosyncratic proprietary software from different vendors. That's different in each environment. So it's this slow standardization that's happening. And I think it's going to happen faster than we think. And I think in a couple years it's going to be a requirement that, does your software work on all these different departments? Is it based on open source? Is it using this Delta Lake house pattern? And if it's not, I think they're going to demand it. >> Yeah, I feel like we're close to some sort of defacto standard coming and you guys are a big part of it, once that clicks in, it's going to highly accelerate in the open, and I think it's going to be super valuable. Ali, thank you so much for your time, and congratulations to you and your team. Like we've been following you guys since the beginning. Remember the early days and look how far it's come. And again, you guys are really making a big difference in making a super cool environment out there. Thanks for coming on sharing. >> Thank you so much John. >> Okay, this is supercloud 22. I'm John Furrier stay with more for more coverage and more commentary after this break. (light hearted music)

>> From theCUBE studios in Palo Alto in Boston bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> The term Supercloud is somewhat new, but the concepts behind it have been bubbling for years, early last decade when NIST put forth a definition of cloud computing it said services had to be accessible over a public network essentially cutting the on-prem crowd out of the cloud conversation. Now a guy named Chuck Hollis, who was a field CTO at EMC at the time and a prolific blogger objected to that criterion and laid out his vision for what he termed a private cloud. Now, in that post, he showed a workload running both on premises and in a public cloud sharing the underlying resources in an automated and seamless manner. What later became known more broadly as hybrid cloud that vision as we now know, really never materialized, and we were left with multi-cloud sets of largely incompatible and disconnected cloud services running in separate silos. The point is what Hollis laid out, IE the ability to abstract underlying infrastructure complexity and run workloads across multiple heterogeneous estates with an identical experience is what super cloud is all about. Hello and welcome to this week's Wikibon cube insights powered by ETR and this breaking analysis. We share what we hope to learn from super cloud 22 next week, next Tuesday at 9:00 AM Pacific. The community is gathering for Supercloud 22 an inclusive pilot symposium hosted by theCUBE and made possible by VMware and other founding partners. It's a one day single track event with more than 25 speakers digging into the architectural, the technical, structural and business aspects of Supercloud. This is a hybrid event with a live program in the morning running out of our Palo Alto studio and pre-recorded content in the afternoon featuring industry leaders, technologists, analysts and investors up and down the technology stack. Now, as I said up front the seeds of super cloud were sewn early last decade. After the very first reinvent we published our Amazon gorilla post, that scene in the upper right corner here. And we talked about how to differentiate from Amazon and form ecosystems around industries and data and how the cloud would change IT permanently. And then up in the upper left we put up a post on the old Wikibon Wiki. Yeah, it used to be a Wiki. Check out my hair by the way way no gray, that's how long ago this was. And we talked about in that post how to compete in the Amazon economy. And we showed a graph of how IT economics were changing. And cloud services had marginal economics that looked more like software than hardware at scale. And this would reset, we said opportunities for both technology sellers and buyers for the next 20 years. And this came into sharper focus in the ensuing years culminating in a milestone post by Greylock's Jerry Chen called Castles in the Cloud. It was an inspiration and catalyst for us using the term Supercloud in John Furrier's post prior to reinvent 2021. So we started to flesh out this idea of Supercloud where companies of all types build services on top of hyperscale infrastructure and across multiple clouds, going beyond multicloud 1.0, if you will, which was really a symptom, as we said, many times of multi-vendor at least that's what we argued. And despite its fuzzy definition, it resonated with people because they knew something was brewing, Keith Townsend the CTO advisor, even though he frankly, wasn't a big fan of the buzzy nature of the term Supercloud posted this awesome Blackboard on Twitter take a listen to how he framed it. Please play the clip. >> Is VMware the right company to make the super cloud work, term that Wikibon came up with to describe the taking of discreet services. So it says RDS from AWS, cloud compute engines from GCP and authentication from Azure to build SaaS applications or enterprise applications that connect back to your data center, is VMware's cross cloud vision 'cause it is just a vision today, the right approach. Or should you be looking towards companies like HashiCorp to provide this overall capability that we all agree, or maybe you don't that we need in an enterprise comment below your thoughts. >> So I really like that Keith has deep practitioner knowledge and lays out a couple of options. I especially like the examples he uses of cloud services. He recognizes the need for cross cloud services and he notes this capability is aspirational today. Remember this was eight or nine months ago and he brings HashiCorp into the conversation as they're one of the speakers at Supercloud 22 and he asks the community, what they think, the thing is we're trying to really test out this concept and people like Keith are instrumental as collaborators. Now I'm sure you're not surprised to hear that mot everyone is on board with the Supercloud meme, in particular Charles Fitzgerald has been a wonderful collaborator just by his hilarious criticisms of the concept. After a couple of super cloud posts, Charles put up his second rendition of "Supercloudifragilisticexpialidoucious". I mean, it's just beautiful, but to boot, he put up this picture of Baghdad Bob asking us to just stop, Bob's real name is Mohamed Said al-Sahaf. He was the minister of propaganda for Sadam Husein during the 2003 invasion of Iraq. And he made these outrageous claims of, you know US troops running in fear and putting down their arms and so forth. So anyway, Charles laid out several frankly very helpful critiques of Supercloud which has led us to really advance the definition and catalyze the community's thinking on the topic. Now, one of his issues and there are many is we said a prerequisite of super cloud was a super PaaS layer. Gartner's Lydia Leong chimed in saying there were many examples of successful PaaS vendors built on top of a hyperscaler some having the option to run in more than one cloud provider. But the key point we're trying to explore is the degree to which that PaaS layer is purpose built for a specific super cloud function. And not only runs in more than one cloud provider, Lydia but runs across multiple clouds simultaneously creating an identical developer experience irrespective of a state. Now, maybe that's what Lydia meant. It's hard to say from just a tweet and she's a sharp lady, so, and knows more about that market, that PaaS market, than I do. But to the former point at Supercloud 22, we have several examples. We're going to test. One is Oracle and Microsoft's recent announcement to run database services on OCI and Azure, making them appear as one rather than use an off the shelf platform. Oracle claims to have developed a capability for developers specifically built to ensure high performance low latency, and a common experience for developers across clouds. Another example we're going to test is Snowflake. I'll be interviewing Benoit Dageville co-founder of Snowflake to understand the degree to which Snowflake's recent announcement of an application development platform is perfect built, purpose built for the Snowflake data cloud. Is it just a plain old pass, big whoop as Lydia claims or is it something new and innovative, by the way we invited Charles Fitz to participate in Supercloud 22 and he decline saying in addition to a few other somewhat insulting things there's definitely interesting new stuff brewing that isn't traditional cloud or SaaS but branding at all super cloud doesn't help either. Well, indeed, we agree with part of that and we'll see if it helps advanced thinking and helps customers really plan for the future. And that's why Supercloud 22 has going to feature some of the best analysts in the business in The Great Supercloud Debate. In addition to Keith Townsend and Maribel Lopez of Lopez research and Sanjeev Mohan from former Gartner analyst and principal at SanjMo participated in this session. Now we don't want to mislead you. We don't want to imply that these analysts are hopping on the super cloud bandwagon but they're more than willing to go through the thought experiment and mental exercise. And, we had a great conversation that you don't want to miss. Maribel Lopez had what I thought was a really excellent way to think about this. She used TCP/IP as an historical example, listen to what she said. >> And Sanjeev Mohan has some excellent thoughts on the feasibility of an open versus de facto standard getting us to the vision of Supercloud, what's possible and what's likely now, again, I don't want to imply that these analysts are out banging the Supercloud drum. They're not necessarily doing that, but they do I think it's fair to say believe that something new is bubbling and whether it's called Supercloud or multicloud 2.0 or cross cloud services or whatever name you choose it's not multicloud of the 2010s and we chose Supercloud. So our goal here is to advance the discussion on what's next in cloud and Supercloud is meant to be a term to describe that future of cloud and specifically the cloud opportunities that can be built on top of hyperscale, compute, storage, networking machine learning, and other services at scale. And that is why we posted this piece on Answering the top 10 questions about Supercloud. Many of which were floated by Charles Fitzgerald and others in the community. Why does the industry need another term what's really new and different? And what is hype? What specific problems does Supercloud solve? What are the salient characteristics of Supercloud? What's different beyond multicloud? What is a super pass? Is it necessary to have a Supercloud? How will applications evolve on superclouds? What workloads will run? All these questions will be addressed in detail as a way to advance the discussion and help practitioners and business people understand what's real today. And what's possible with cloud in the near future. And one other question we'll address is who will build super clouds? And what new entrance we can expect. This is an ETR graphic that we showed in a previous episode of breaking analysis, and it lays out some of the companies we think are building super clouds or in a position to do so, by the way the Y axis shows net score or spending velocity and the X axis depicts presence in the ETR survey of more than 1200 respondents. But the key callouts to this slide in addition to some of the smaller firms that aren't yet showing up in the ETR data like Chaossearch and Starburst and Aviatrix and Clumio but the really interesting additions are industry players Walmart with Azure, Capital one and Goldman Sachs with AWS, Oracle, with Cerner. These we think are early examples, bubbling up of industry clouds that will eventually become super clouds. So we'll explore these and other trends to get the community's input on how this will all play out. These are the things we hope you'll take away from Supercloud 22. And we have an amazing lineup of experts to answer your question. Technologists like Kit Colbert, Adrian Cockcroft, Mariana Tessel, Chris Hoff, Will DeForest, Ali Ghodsi, Benoit Dageville, Muddu Sudhakar and many other tech athletes, investors like Jerry Chen and In Sik Rhee the analyst we featured earlier, Paula Hansen talking about go to market in a multi-cloud world Gee Rittenhouse talking about cloud security, David McJannet, Bhaskar Gorti of Platform9 and many, many more. And of course you, so please go to theCUBE.net and register for Supercloud 22, really lightweight reg. We're not doing this for lead gen. We're doing it for collaboration. If you sign in you can get the chat and ask questions in real time. So don't miss this inaugural event Supercloud 22 on August 9th at 9:00 AM Pacific. We'll see you there. Okay. That's it for today. Thanks for watching. Thank you to Alex Myerson who's on production and manages the podcast. Kristen Martin and Cheryl Knight. They help get the word out on social media and in our newsletters. And Rob Hof is our editor in chief over at SiliconANGLE. Does some really wonderful editing. Thank you to all. Remember these episodes are all available as podcasts wherever you listen, just search breaking analysis podcast. I publish each week on wikibon.com and Siliconangle.com. And you can email me at David.Vellantesiliconangle.com or DM me at Dvellante, comment on my LinkedIn post. Please do check out ETR.AI for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching. And we'll see you next week in Palo Alto at Supercloud 22 or next time on breaking analysis. (calm music)

(upbeat music) >> On August 9th at 9:00 am Pacific, we'll be broadcasting live from theCUBE Studios in Palo Alto, California. Supercloud22, an open industry event made possible by VMware. Supercloud22 will lay out the future of multi-cloud services in the 2020s. John Furrier and I will be hosting a star lineup, including Kit Colbert, VMware CTO, Benoit Dageville, co-founder of Snowflake, Marianna Tessel, CTO of Intuit, Ali Ghodsi, CEO of Databricks, Adrian Cockcroft, former CTO of Netflix, Jerry Chen of Greylock, Chris Hoff aka Beaker, Maribel Lopez, Keith Townsend, Sanjiv Mohan, and dozens of thought leaders. A full day track with 17 sessions. You won't want to miss Supercloud22. Go to thecube.net to mark your calendar and learn more about this free hybrid event. We'll see you there. (upbeat music)

>> Announcer: theCUBE presents Dell Technologies World, brought to you by Dell. >> Hey everyone. Welcome back to theCUBE's live coverage of Dell Technologies World 2022, live from the Venetian in Las Vegas. Lisa Martin here with Dave Vellante. This is our second full day of coverage of theCUBE. Lots going on, lots of announcements. We always love talking to customers, hearing the voice of the customer, and we have a couple of guests, one from Dell Customer at Lowe's, John Dabek is here, the senior director of infrastructure. Ali Biers also joins us, marketing director of edge solutions at Dell Technologies. Welcome to the program. >> Thank you so much. >> Thank you for inviting us. Appreciate it. >> So John, let's go ahead and start with you. Let's talk about what the heck is going on in retail. Tremendous change, tremendous transformation, lot of pressures. The last two years have been quite influential. Talk to us about some of the trends that you're seeing in retail, some of the challenges that are going on. >> Absolutely, so COVID has put everything on steroids in terms of the omnichannel experience, so we no longer think of digital as something that's separate. It's all integrated with the store experience. So, interestingly enough, two thirds of our customers shop online before they come into the store, so that shows you the power of having the digital working in harmony with the store. >> So how does that affect your technology strategy? What changes do you see? >> That's a very good question, so we've had to accelerate a number of our new technologies to really create that frictionless experience for the customer. So for example, I'll give you a great example of a technology that we deploy today called pickup lockers. So you order online and then there's a set of pickup lockers right in the vestibule of the store. You go up and you scan it, the locker opens, and then you can take your merchandise and go on, so it's a great experience as to how the technology has changed, and everything from utilizing the mobile applications where customers can now text us when they're in the parking lot, we can deliver their merchandise. Michael Dell put it very well in terms of the strategy in his keynote yesterday. What he talked about was today it's the public cloud, it's the private cloud within the data centers, and it's the edge, and the edge has become very, very important for us because that's where we want to put all of our technologies in the store, closer to the store. >> Ali talk to us about from an overall a Dell vision lens perspective the challenges overall that you're seeing in retail and where the edge is really advantageous for organizations to be competitive. >> Yeah, I mean, really what you're seeing is you've got these incredibly savvy customers who really want to have an experience when they go into the store, and on the other hand, you have the retailer that wants to develop that loyalty but yet they're dealing with tremendous complexity in their footprint, as well as just the pace of change, so trying to modernize and do that at a really fast pace just like what John was talking about and still stick to all the imperatives like being secure and manageable at scale. It's really a big challenge. >> Yeah, and when you talk, Ali, about modernizing at a fast pace, the first 600 stores that we did with VxRail, and we'll go into a little more detail I'm sure about that, we did in three months with the help of Dell technology. >> Lisa: 600 stores in three months? >> In three months, right, and the key was zero disruptions in the store. Now we're talking about 100,000+ square foot stores, so we're talking big stores, and we have a very short window. We can go from midnight to 5:00 AM because 5:00 AM the contractors are there to pick up their materials and we have to be open and ready, so we didn't miss a beat. >> So that's interesting. I heard your CEO the other day talking about how you guys really focused on the contractors, especially during COVID. So that was also another shift. I mean, the volume from contractors probably increased 'cause we give them such great focus. So there's this concept of the intelligent factory. Is there a similar one with the intelligent store? >> John: Oh, without a doubt. So I'll give you an example. We have 140,000 mobile devices deployed in our stores for our employees that can do everything from find merchandise, talk, receive calls. You're going to the store to pick up mulch, and they can take the device and do a checkout from the device instead of you having to come into the store and then go out to pick up your mulch. It doesn't get better than that. >> I love that example cause that one's so relatable, and I think like once you start thinking about how all this to technology in the store can really help, so all of a sudden you know where your customers are spending their time in the store. You can position your customer service people to help in the aisles where people are getting stuck, so it really just puts so many more insights in the hands of retailers to be able to action and make decisions. >> You know, it's funny, sometimes people, when they talk to people in IT, technology like ourselves say, "You know, you guys always talk about, oh, permanent changes. Nah, it's going to be the same. You watch in a few years." Here's an example, there's no way we're ever going back. You know, it's permanent. >> It's permanent, and you know what? All the bad things about about COVID and the pandemic, the great thing is it really accelerated that omnichannel journey. It forced many retailers to do that, including Lowe's. >> Silver lining, but it also, from a forcing factor perspective, it was critical from a competitive standpoint. I mean, we have these expectations as consumers that we can have this consumer experience everywhere which means I want to be able to do my transaction in real time. I want to go onto the website and make sure that they have what I want inventory wise in real time. Real time we learned in the pandemic, not a nice to have anymore. >> No, absolutely. >> Lisa: That is a competitive advantage for every industry, especially retail. >> Yeah, and if you think about it, we have a mini data center inside the store with the VxRail, so it was very important for us because we were not able to leverage the new application development on the old platform, so we absolutely need the power of the new platform to enable the stores. It's very, very, critical. >> Paint a picture of what it's like inside of a store. I mean, what's the infrastructure look like, the apps that are running, the data flow? >> John: So if you picture a dedicated room for the technology, unfortunately in a store you don't build a data center, so it's a concrete floor, as you can imagine. But through the help of Dell, they've really helped us harden the environment as well, to put in technologies that help with intelligent power distribution units and other types of technology because we're making such a big investment that we don't want to have power be a disrupter. You get six nines on our network, six nines on our, on our compute infrastructure. We don't want power to be an impact. But in terms of the apps, everything that you need to run a store from a POS perspective runs in the environment, and it's being enhanced every day, because now the communication from the mobile device of the consumer to what happens in the store is integrating, so it really requires a lot of compute power. >> What I really like about the way you guys have done it too is that you guys have really thought about it in terms of planning for the future. So you thought about how to create that foundation that's really going to scale over time. >> And Ali you've brought up a good point because one of the things that we didn't anticipate when we started was the fact that we would need GPUs in the future. and the power of the GPU was required for things like video analytics, AI, and it came to light as we had one of our innovator, person in the lab saying, "Hey in the test system, we want 300 gigs of memory to do a test," and we're going like, oh my God, this would never run in production. So that's when we got into the whole concept of GPU so all of our stores are GPU enabled, so as we need them, we can add that to the store, but thanks for bringing that. >> That's really interesting. So for what, security? Other use cases? AI, you're saying. How are you applying that? Dig into that. >> It could be security, so think of having cameras in the store that watch what people do from a checkout perspective, and it's tied in with the system so it knows the weight of an item, it knows the cost of an item, and it's able to spot potential frauds and alert people. But to do that, you need video analytics, and that requires a lot of processing power. >> How much of that data do you persist? >> We could talk about that for another hour. >> Oh, okay. >> With respect to that. But generally we utilize the data to handle what we're looking to accomplish. We do capture other data for AI and other analytic purposes as well. >> Ali I think I interrupted you. >> Ali: Oh, no worries. I think one of the things about the edges, people have a tendency to go build a technology stack to address the business problem that they're trying to address in that moment, and it's usually driven by the people that are working in the store. They see an opportunity for advancement, but all of a sudden, if you have a lot of those, how now are you going to deploy it, secure it, manage it, and do them all separately? So I think what you're talking about is you've really figured out a way to do that across all those different use cases, and maybe even for the ones that you don't know exist yet so. >> And that's the good point is that we don't know what exists, because we have to, as we build it, we have to build the business case for what makes sense to put into the stores. So you you'll see a lot of continued innovation with inventory aids to help stock shelves, applications that help the customer journey. I saw some deployment of some new apps in the stores where we can tell where people are located real time in the store, so wouldn't it be great if you know that you can dispatch customer service personnel to that area and great opportunity to plus sell in that environment. >> I can't wait for my next trip to Lowe's. This is going to be so fantastic. But John, I got to ask you, you're sitting here with the marketing director, I'm a marketing girl myself, future proof. It's a term that is always interests me because it can mean so many different things. You're working with Dell, I've been working with Dell for a while, how is what you've architected for the connected store? Intelligence store, excuse me. How do you feel like when you don't know what's coming, but do you really feel like we've got a future-proof architecture capabilities and a partner that's going to allow us to scale and grow as things, obviously we couldn't have predicted what happened in the last two years. >> So not too recent in the past where you would primarily have appliances in stores and single purpose servers, separate storage. So now with the VxRail technology, you have hyper converged infrastructure. So things are virtualized, your storage is virtualized, your server host infrastructure is virtualized, and the power of the VxRail is that as we grow and have different needs, we can change out the processor, we can add memory, we can add storage all while we're still running in a store. >> Dave: Bring a GPU in if you need to, right? >> Bring a GPU in, so it was architected to handle the growth and the simplicity of running the store. So we only have a handful of people that manage the stores from a technology standpoint, and thanks to the the technologies that are provided. >> So you could scale it, and you got the blueprint, what's the network look like? >> And that's some good advice for folks who are looking at this. You have to address the network first, so we deployed a software defined network that gave us the capacity and the future growth capacity and the backup we're using. We're transferring to from 4G to 5G for backup purposes, and we're trying to figure out what's the role of 5G in the future? 'Cause it gives you tremendous flexibility. But remember the VxRail and the edge can run independently, so if the network goes down, we operate a store. >> Lisa: And you had that frictionless experience which as consumers, we all had this expectation that it's going to be frictionless, it's going to be seamless, I'm going to be able to get what I want. >> Absolutely. >> Not quite 24/7. Well, yeah, with online, yeah. >> With online, 24/7. >> So last question as we grab, and I wish we had more time to dig into this. What's next? What are some of the future directions as hopefully things return back to "normal". What are some of the things that Lowe's and Dell are going to do next together? >> We have to finish the stores. We'll be done by October. And by the way, we're experiencing supply chain issue, but not with Dell. We're having trouble getting network switches, but last week we had a breakthrough, and right now we're on track to finish all of the stores by October of 2022. But what's next? Continuing to now leverage the platform that we've put in place, to bring the applications and to start working with our innovators to experiment with the GPUs and put it into effect, and I'm sure Ali's got some great things planned as well on the edge with the technology which we're look to take advantage of. >> Yeah I mean, our goal is really to help customers to simplify their edge because it's incredibly complex. They're dealing with an ecosystem of partners, software, hardware, networking, so really being that partner that they can rely on, having that broad end to end portfolio, and being the person and the company that can architect and bring all of that together in a way that you can life cycle manage it over time. >> John: And the great thing is by being software defined, it's all seems very complicated, but it's simple to manage, and that's the key and that's the power that Dell brings to us. >> Simple to manage, famous last words. John, thank you, Ali you as well for joining us, sharing what Dell and Lowe's are doing together to really enable this intelligent store. I really can't wait for my next trip. (everyone laughing) >> Thank you so much. >> Yes, I got to hit the mulch pile. Want 'em to bring into my car's, it's too heavy to carry. Guys thank you so much for sharing your insights. We appreciate the story. >> Thank you. >> For our guests and Dave Vellante, I'm Lisa Martin. You're watching theCUBE live from Las Vegas at the Venetian. Day two of our coverage of Dell Tech World continues right after this short break. (soft music)