>> 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.

(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

(soft electronic music) >> Hey everyone, welcome to this CUBE conversation as part of the AWS Startup Showcase, season three, episode one, featuring Astronomer. I'm your host, Lisa Martin. I'm in the CUBE's Palo Alto Studios, and today excited to be joined by a couple of guests, a couple of co-founders from Astronomer. Viraj Parekh is with us, as is Paola Peraza-Calderon. Thanks guys so much for joining us. Excited to dig into Astronomer. >> Thank you so much for having us. >> Yeah, thanks for having us. >> Yeah, and we're going to be talking about the role of data orchestration. Paola, let's go ahead and start with you. Give the audience that understanding, that context about Astronomer and what it is that you guys do. >> Mm-hmm. Yeah, absolutely. So, Astronomer is a, you know, we're a technology and software company for modern data orchestration, as you said, and we're the driving force behind Apache Airflow. The Open Source Workflow Management tool that's since been adopted by thousands and thousands of users, and we'll dig into this a little bit more. But, by data orchestration, we mean data pipeline, so generally speaking, getting data from one place to another, transforming it, running it on a schedule, and overall just building a central system that tangibly connects your entire ecosystem of data services, right. So what, that's Redshift, Snowflake, DVT, et cetera. And so tangibly, we build, we at Astronomer here build products powered by Apache Airflow for data teams and for data practitioners, so that they don't have to. So, we sell to data engineers, data scientists, data admins, and we really spend our time doing three things. So, the first is that we build Astro, our flagship cloud service that we'll talk more on. But here, we're really building experiences that make it easier for data practitioners to author, run, and scale their data pipeline footprint on the cloud. And then, we also contribute to Apache Airflow as an open source project and community. So, we cultivate the community of humans, and we also put out open source developer tools that actually make it easier for individual data practitioners to be productive in their day-to-day jobs, whether or not they actually use our product and and pay us money or not. And then of course, we also have professional services and education and all of these things around our commercial products that enable folks to use our products and use Airflow as effectively as possible. So yeah, super, super happy with everything we've done and hopefully that gives you an idea of where we're starting. >> Awesome, so when you're talking with those, Paola, those data engineers, those data scientists, how do you define data orchestration and what does it mean to them? >> Yeah, yeah, it's a good question. So, you know, if you Google data orchestration you're going to get something about an automated process for organizing silo data and making it accessible for processing and analysis. But, to your question, what does that actually mean, you know? So, if you look at it from a customer's perspective, we can share a little bit about how we at Astronomer actually do data orchestration ourselves and the problems that it solves for us. So, as many other companies out in the world do, we at Astronomer need to monitor how our own customers use our products, right? And so, we have a weekly meeting, for example, that goes through a dashboard and a dashboarding tool called Sigma where we see the number of monthly customers and how they're engaging with our product. But, to actually do that, you know, we have to use data from our application database, for example, that has behavioral data on what they're actually doing in our product. We also have data from third party API tools, like Salesforce and HubSpot, and other ways in which our customer, we actually engage with our customers and their behavior. And so, our data team internally at Astronomer uses a bunch of tools to transform and use that data, right? So, we use FiveTran, for example, to ingest. We use Snowflake as our data warehouse. We use other tools for data transformations. And even, if we at Astronomer don't do this, you can imagine a data team also using tools like, Monte Carlo for data quality, or Hightouch for Reverse ETL, or things like that. And, I think the point here is that data teams, you know, that are building data-driven organizations have a plethora of tooling to both ingest the right data and come up with the right interfaces to transform and actually, interact with that data. And so, that movement and sort of synchronization of data across your ecosystem is exactly what data orchestration is responsible for. Historically, I think, and Raj will talk more about this, historically, schedulers like KRON and Oozie or Control-M have taken a role here, but we think that Apache Airflow has sort of risen over the past few years as the defacto industry standard for writing data pipelines that do tasks, that do data jobs that interact with that ecosystem of tools in your organization. And so, beyond that sort of data pipeline unit, I think where we see it is that data acquisition is not only writing those data pipelines that move your data, but it's also all the things around it, right, so, CI/CD tool and Secrets Management, et cetera. So, a long-winded answer here, but I think that's how we talk about it here at Astronomer and how we're building our products. >> Excellent. Great context, Paola. Thank you. Viraj, let's bring you into the conversation. Every company these days has to be a data company, right? They've got to be a software company- >> Mm-hmm. >> whether it's my bank or my grocery store. So, how are companies actually doing data orchestration today, Viraj? >> Yeah, it's a great question. So, I think one thing to think about is like, on one hand, you know, data orchestration is kind of a new category that we're helping define, but on the other hand, it's something that companies have been doing forever, right? You need to get data moving to use it, you know. You've got it all in place, aggregate it, cleaning it, et cetera. So, when you look at what companies out there are doing, right. Sometimes, if you're a more kind of born in the cloud company, as we say, you'll adopt all these cloud native tooling things your cloud provider gives you. If you're a bank or another sort of institution like that, you know, you're probably juggling an even wider variety of tools. You're thinking about a cloud migration. You might have things like Kron running in one place, Uzi running somewhere else, Informatics running somewhere else, while you're also trying to move all your workloads to the cloud. So, there's quite a large spectrum of what the current state is for companies. And then, kind of like Paola was saying, Apache Airflow started in 2014, and it was actually started by Airbnb, and they put out this blog post that was like, "Hey here's how we use Apache Airflow to orchestrate our data across all their sources." And really since then, right, it's almost been a decade since then, Airflow emerged as the open source standard, and there's companies of all sorts using it. And, it's really used to tie all these tools together, especially as that number of tools increases, companies move to hybrid cloud, hybrid multi-cloud strategies, and so on and so forth. But you know, what we found is that if you go to any company, especially a larger one and you say like, "Hey, how are you doing data orchestration?" They'll probably say something like, "Well, I have five data teams, so I have eight different ways I do data orchestration." Right. This idea of data orchestration's been there but the right way to do it, kind of all the abstractions you need, the way your teams need to work together, and so on and so forth, hasn't really emerged just yet, right? It's such a quick moving space that companies have to combine what they were doing before with what their new business initiatives are today. So, you know, what we really believe here at Astronomer is Airflow is the core of how you solve data orchestration for any sort of use case, but it's not everything. You know, it needs a little more. And, that's really where our commercial product, Astro comes in, where we've built, not only the most tried and tested airflow experience out there. We do employ a majority of the Airflow Core Committers, right? So, we're kind of really deep in the project. We've also built the right things around developer tooling, observability, and reliability for customers to really rely on Astro as the heart of the way they do data orchestration, and kind of think of it as the foundational layer that helps tie together all the different tools, practices and teams large companies have to do today. >> That foundational layer is absolutely critical. You've both mentioned open source software. Paola, I want to go back to you, and just give the audience an understanding of how open source really plays into Astronomer's mission as a company, and into the technologies like Astro. >> Mm-hmm. Yeah, absolutely. I mean, we, so we at Astronomers started using Airflow and actually building our products because Airflow is open source and we were our own customers at the beginning of our company journey. And, I think the open source community is at the core of everything we do. You know, without that open source community and culture, I think, you know, we have less of a business, and so, we're super invested in continuing to cultivate and grow that. And, I think there's a couple sort of concrete ways in which we do this that personally make me really excited to do my own job. You know, for one, we do things like we organize meetups and we sponsor the Airflow Summit and there's these sort of baseline community efforts that I think are really important and that reminds you, hey, there just humans trying to do their jobs and learn and use both our technology and things that are out there and contribute to it. So, making it easier to contribute to Airflow, for example, is another one of our efforts. As Viraj mentioned, we also employ, you know, engineers internally who are on our team whose full-time job is to make the open source project better. Again, regardless of whether or not you're a customer of ours or not, we want to make sure that we continue to cultivate the Airflow project in and of itself. And, we're also building developer tooling that might not be a part of the Apache Open Source project, but is still open source. So, we have repositories in our own sort of GitHub organization, for example, with tools that individual data practitioners, again customers are not, can use to make them be more productive in their day-to-day jobs with Airflow writing Dags for the most common use cases out there. The last thing I'll say is how important I think we've found it to build sort of educational resources and documentation and best practices. Airflow can be complex. It's been around for a long time. There's a lot of really, really rich feature sets. And so, how do we enable folks to actually use those? And that comes in, you know, things like webinars, and best practices, and courses and curriculum that are free and accessible and open to the community are just some of the ways in which I think we're continuing to invest in that open source community over the next year and beyond. >> That's awesome. It sounds like open source is really core, not only to the mission, but really to the heart of the organization. Viraj, I want to go back to you and really try to understand how does Astronomer fit into the wider modern data stack and ecosystem? Like what does that look like for customers? >> Yeah, yeah. So, both in the open source and with our commercial customers, right? Folks everywhere are trying to tie together a huge variety of tools in order to start making sense of their data. And you know, I kind of think of it almost like as like a pyramid, right? At the base level, you need things like data reliability, data, sorry, data freshness, data availability, and so on and so forth, right? You just need your data to be there. (coughs) I'm sorry. You just need your data to be there, and you need to make it predictable when it's going to be there. You need to make sure it's kind of correct at the highest level, some quality checks, and so on and so forth. And oftentimes, that kind of takes the case of ELT or ETL use cases, right? Taking data from somewhere and moving it somewhere else, usually into some sort of analytics destination. And, that's really what businesses can do to just power the core parts of getting insights into how their business is going, right? How much revenue did I had? What's in my pipeline, salesforce, and so on and so forth. Once that kind of base foundation is there and people can get the data they need, how they need it, it really opens up a lot for what customers can do. You know, I think one of the trendier things out there right now is MLOps, and how do companies actually put machine learning into production? Well, when you think about it you kind of have to squint at it, right? Like, machine learning pipelines are really just any other data pipeline. They just have a certain set of needs that might not not be applicable to ELT pipelines. And, when you kind of have a common layer to tie together all the ways data can move through your organization, that's really what we're trying to make it so companies can do. And, that happens in financial services where, you know, we have some customers who take app data coming from their mobile apps, and actually run it through their fraud detection services to make sure that all the activity is not fraudulent. We have customers that will run sports betting models on our platform where they'll take data from a bunch of public APIs around different sporting events that are happening, transform all of that in a way their data scientist can build models with it, and then actually bet on sports based on that output. You know, one of my favorite use cases I like to talk about that we saw in the open source is we had there was one company whose their business was to deliver blood transfusions via drone into remote parts of the world. And, it was really cool because they took all this data from all sorts of places, right? Kind of orchestrated all the aggregation and cleaning and analysis that happened had to happen via airflow and the end product would be a drone being shot out into a real remote part of the world to actually give somebody blood who needed it there. Because it turns out for certain parts of the world, the easiest way to deliver blood to them is via drone and not via some other, some other thing. So, these kind of, all the things people do with the modern data stack is absolutely incredible, right? Like you were saying, every company's trying to be a data-driven company. What really energizes me is knowing that like, for all those best, super great tools out there that power a business, we get to be the connective tissue, or the, almost like the electricity that kind of ropes them all together and makes so people can actually do what they need to do. >> Right. Phenomenal use cases that you just described, Raj. I mean, just the variety alone of what you guys are able to do and impact is so cool. So Paola, when you're with those data engineers, those data scientists, and customer conversations, what's your pitch? Why use Astro? >> Mm-hmm. Yeah, yeah, it's a good question. And honestly, to piggyback off of Viraj, there's so many. I think what keeps me so energized is how mission critical both our product and data orchestration is, and those use cases really are incredible and we work with customers of all shapes and sizes. But, to answer your question, right, so why use Astra? Why use our commercial products? There's so many people using open source, why pay for something more than that? So, you know, the baseline for our business really is that Airflow has grown exponentially over the last five years, and like we said has become an industry standard that we're confident there's a huge opportunity for us as a company and as a team. But, we also strongly believe that being great at running Airflow, you know, doesn't make you a successful company at what you do. What makes you a successful company at what you do is building great products and solving problems and solving pin points of your own customers, right? And, that differentiating value isn't being amazing at running Airflow. That should be our job. And so, we want to abstract those customers from meaning to do things like manage Kubernetes infrastructure that you need to run Airflow, and then hiring someone full-time to go do that. Which can be hard, but again doesn't add differentiating value to your team, or to your product, or to your customers. So, folks to get away from managing that infrastructure sort of a base, a base layer. Folks who are looking for differentiating features that make their team more productive and allows them to spend less time tweaking Airflow configurations and more time working with the data that they're getting from their business. For help, getting, staying up with Airflow releases. There's a ton of, we've actually been pretty quick to come out with new Airflow features and releases, and actually just keeping up with that feature set and working strategically with a partner to help you make the most out of those feature sets is a key part of it. And, really it's, especially if you're an organization who currently is committed to using Airflow, you likely have a lot of Airflow environments across your organization. And, being able to see those Airflow environments in a single place and being able to enable your data practitioners to create Airflow environments with a click of a button, and then use, for example, our command line to develop your Airflow Dags locally and push them up to our product, and use all of the sort of testing and monitoring and observability that we have on top of our product is such a key. It sounds so simple, especially if you use Airflow, but really those things are, you know, baseline value props that we have for the customers that continue to be excited to work with us. And of course, I think we can go beyond that and there's, we have ambitions to add whole, a whole bunch of features and expand into different types of personas. >> Right? >> But really our main value prop is for companies who are committed to Airflow and want to abstract themselves and make use of some of the differentiating features that we now have at Astronomer. >> Got it. Awesome. >> Thank you. One thing, one thing I'll add to that, Paola, and I think you did a good job of saying is because every company's trying to be a data company, companies are at different parts of their journey along that, right? And we want to meet customers where they are, and take them through it to where they want to go. So, on one end you have folks who are like, "Hey, we're just building a data team here. We have a new initiative. We heard about Airflow. How do you help us out?" On the farther end, you know, we have some customers that have been using Airflow for five plus years and they're like, "Hey, this is awesome. We have 10 more teams we want to bring on. How can you help with this? How can we do more stuff in the open source with you? How can we tell our story together?" And, it's all about kind of taking this vast community of data users everywhere, seeing where they're at, and saying like, "Hey, Astro and Airflow can take you to the next place that you want to go." >> Which is incredibly- >> Mm-hmm. >> and you bring up a great point, Viraj, that every company is somewhere in a different place on that journey. And it's, and it's complex. But it sounds to me like a lot of what you're doing is really stripping away a lot of the complexity, really enabling folks to use their data as quickly as possible, so that it's relevant and they can serve up, you know, the right products and services to whoever wants what. Really incredibly important. We're almost out of time, but I'd love to get both of your perspectives on what's next for Astronomer. You give us a a great overview of what the company's doing, the value in it for customers. Paola, from your lens as one of the co-founders, what's next? >> Yeah, I mean, I think we'll continue to, I think cultivate in that open source community. I think we'll continue to build products that are open sourced as part of our ecosystem. I also think that we'll continue to build products that actually make Airflow, and getting started with Airflow, more accessible. So, sort of lowering that barrier to entry to our products, whether that's price wise or infrastructure requirement wise. I think making it easier for folks to get started and get their hands on our product is super important for us this year. And really it's about, I think, you know, for us, it's really about focused execution this year and all of the sort of core principles that we've been talking about. And continuing to invest in all of the things around our product that again, enable teams to use Airflow more effectively and efficiently. >> And that efficiency piece is, everybody needs that. Last question, Viraj, for you. What do you see in terms of the next year for Astronomer and for your role? >> Yeah, you know, I think Paola did a really good job of laying it out. So it's, it's really hard to disagree with her on anything, right? I think executing is definitely the most important thing. My own personal bias on that is I think more than ever it's important to really galvanize the community around airflow. So, we're going to be focusing on that a lot. We want to make it easier for our users to get get our product into their hands, be that open source users or commercial users. And last, but certainly not least, is we're also really excited about Data Lineage and this other open source project in our umbrella called Open Lineage to make it so that there's a standard way for users to get lineage out of different systems that they use. When we think about what's in store for data lineage and needing to audit the way automated decisions are being made. You know, I think that's just such an important thing that companies are really just starting with, and I don't think there's a solution that's emerged that kind of ties it all together. So, we think that as we kind of grow the role of Airflow, right, we can also make it so that we're helping solve, we're helping customers solve their lineage problems all in Astro, which is our kind of the best of both worlds for us. >> Awesome. I can definitely feel and hear the enthusiasm and the passion that you both bring to Astronomer, to your customers, to your team. I love it. We could keep talking more and more, so you're going to have to come back. (laughing) Viraj, Paola, thank you so much for joining me today on this showcase conversation. We really appreciate your insights and all the context that you provided about Astronomer. >> Thank you so much for having us. >> My pleasure. For my guests, I'm Lisa Martin. You're watching this Cube conversation. (soft electronic music)

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

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

(uplifting music) >> Hello, welcome back to theCUBE's coverage of CloudNativeSecurityCon, the inaugural event, in Seattle. I'm John Furrier, host of theCUBE, here in the Palo Alto Studios. We're calling it theCUBE Center. It's kind of like our Sports Center for tech. It's kind of remote coverage. We've been doing this now for a few years. We're going to amp it up this year as more events are remote, and happening all around the world. So, we're going to continue the coverage with this segment focusing on the data stack, entrepreneurial opportunities around all things security, and as, obviously, data's involved. And our next guest is a friend of theCUBE, and CUBE alumni from 2013, entrepreneur himself, turned, now, venture capitalist angel investor, with his own firm, Opher Kahane, Managing Director, Sonoma Ventures. Formerly the founder of Origami, sold to Intuit a few years back. Focusing now on having a lot of fun, angel investing on boards, focusing on data-driven applications, and stacks around that, and all the stuff going on in, really, in the wheelhouse for what's going on around security data. Opher, great to see you. Thanks for coming on. >> My pleasure. Great to be back. It's been a while. >> So you're kind of on Easy Street now. You did the entrepreneurial venture, you've worked hard. We were on together in 2013 when theCUBE just started. XCEL Partners had an event in Stanford, XCEL, and they had all the features there. We interviewed Satya Nadella, who was just a manager at Microsoft at that time, he was there. He's now the CEO of Microsoft. >> Yeah, he was. >> A lot's changed in nine years. But congratulations on your venture you sold, and you got an exit there, and now you're doing a lot of investments. I'd love to get your take, because this is really the biggest change I've seen in the past 12 years, around an inflection point around a lot of converging forces. Data, which, big data, 10 years ago, was a big part of your career, but now it's accelerated, with cloud scale. You're seeing people building scale on top of other clouds, and becoming their own cloud. You're seeing data being a big part of it. Cybersecurity kind of has not really changed much, but it's the most important thing everyone's talking about. So, developers are involved, data's involved, a lot of entrepreneurial opportunities. So I'd love to get your take on how you see the current situation, as it relates to what's gone on in the past five years or so. What's the big story? >> So, a lot of big stories, but I think a lot of it has to do with a promise of making value from data, whether it's for cybersecurity, for Fintech, for DevOps, for RevTech startups and companies. There's a lot of challenges in actually driving and monetizing the value from data with velocity. Historically, the challenge has been more around, "How do I store data at massive scale?" And then you had the big data infrastructure company, like Cloudera, and MapR, and others, deal with it from a scale perspective, from a storage perspective. Then you had a whole layer of companies that evolved to deal with, "How do I index massive scales of data, for quick querying, and federated access, et cetera?" But now that a lot of those underlying problems, if you will, have been solved, to a certain extent, although they're always being stretched, given the scale of data, and its utility is becoming more and more massive, in particular with AI use cases being very prominent right now, the next level is how to actually make value from the data. How do I manage the full lifecycle of data in complex environments, with complex organizations, complex use cases? And having seen this from the inside, with Origami Logic, as we dealt with a lot of large corporations, and post-acquisition by Intuit, and a lot of the startups I'm involved with, it's clear that we're now onto that next step. And you have fundamental new paradigms, such as data mesh, that attempt to address that complexity, and responsibly scaling access, and democratizing access in the value monetization from data, across large organizations. You have a slew of startups that are evolving to help the entire lifecycle of data, from the data engineering side of it, to the data analytics side of it, to the AI use cases side of it. And it feels like the early days, to a certain extent, of the revolution that we've seen in transition from traditional databases, to data warehouses, to cloud-based data processing, and big data. It feels like we're at the genesis of that next wave. And it's super, super exciting, for me at least, as someone who's sitting more in the coach seat, rather than being on the pitch, and building startups, helping folks as they go through those motions. >> So that's awesome. I want to get into some of these data infrastructure dynamics you mentioned, but before that, talk to the audience around what you're working on now. You've been a successful entrepreneur, you're focused on angel investing, so, super-early seed stage. What kind of deals are you looking at? What's interesting to you? What is Sonoma Ventures looking for, and what are some of the entrepreneurial dynamics that you're seeing right now, from a startup standpoint? >> Cool, so, at a macro level, this is a little bit of background of my history, because it shapes very heavily what it is that I'm looking at. So, I've been very fortunate with entrepreneurial career. I founded three startups. All three of them are successful. Final two were sold, the first one merged and went public. And my third career has been about data, moving data, passing data, processing data, generating insights from it. And, at this phase, I wanted to really evolve from just going and building startup number four, from going through the same motions again. A 10 year adventure, I'm a little bit too old for that, I guess. But the next best thing is to sit from a point whereby I can be more elevated in where I'm dealing with, and broaden the variety of startups I'm focused on, rather than just do your own thing, and just go very, very deep into it. Now, what specifically am I focused on at Sonoma Ventures? So, basically, looking at what I refer to as a data-driven application stack. Anything from the low-level data infrastructure and cloud infrastructure, that helps any persona in the data universe maximize value for data, from their particular point of view, for their particular role, whether it's data analysts, data scientists, data engineers, cloud engineers, DevOps folks, et cetera. All the way up to the application layer, in applications that are very data-heavy. And what are very typical data-heavy applications? FinTech, cyber, Web3, revenue technologies, and product and DevOps. So these are the areas we're focused on. I have almost 23 or 24 startups in the portfolio that span all these different areas. And this is in terms of the aperture. Now, typically, focus on pre-seed, seed. Sometimes a little bit later stage, but this is the primary focus. And it's really about partnering with entrepreneurs, and helping them make, if you will, original mistakes, avoid the mistakes I made. >> Yeah. >> And take it to the next level, whatever the milestone they're driving with. So I'm very, very hands-on with many of those startups. Now, what is it that's happening right now, initially, and why is it so exciting? So, on one hand, you have this scaling of data and its complexity, yet lagging value creation from it, across those different personas we've touched on. So that's one fundamental opportunity which is secular. The other one, which is more a cyclic situation, is the fact that we're going through a down cycle in tech, as is very evident in the public markets, and everything we're hearing about funding going slower and lower, terms shifting more into the hands of typical VCs versus entrepreneur-friendly market, and so on and so forth. And a very significant amount of layoffs. Now, when you combine these two trends together, you're observing a very interesting thing, that a lot of folks, really bright folks, who have sold a startup to a company, or have been in the guts of the large startup, or a large corporation, have, hands-on, experienced all those challenges we've spoken about earlier, in turf, maximizing value from data, irrespective of their role, in a specific angle, or vantage point they have on those challenges. So, for many of them, it's an opportunity to, "Now, let me now start a startup. I've been laid off, maybe, or my company's stock isn't doing as well as it used to, as a large corporation. Now I have an opportunity to actually go and take my entrepreneurial passion, and apply it to a product and experience as part of this larger company." >> Yeah. >> And you see a slew of folks who are emerging with these great ideas. So it's a very, very exciting period of time to innovate. >> It's interesting, a lot of people look at, I mean, I look at Snowflake as an example of a company that refactored data warehouses. They just basically took data warehouse, and put it on the cloud, and called it a data cloud. That, to me, was compelling. They didn't pay any CapEx. They rode Amazon's wave there. So, a similar thing going on with data. You mentioned this, and I see it as an enabling opportunity. So whether it's cybersecurity, FinTech, whatever vertical, you have an enablement. Now, you mentioned data infrastructure. It's a super exciting area, as there's so many stacks emerging. We got an analytics stack, there's real-time stacks, there's data lakes, AI stack, foundational models. So, you're seeing an explosion of stacks, different tools probably will emerge. So, how do you look at that, as a seasoned entrepreneur, now investor? Is that a good thing? Is that just more of the market? 'Cause it just seems like more and more kind of decomposed stacks targeted at use cases seems to be a trend. >> Yeah. >> And how do you vet that, is it? >> So it's a great observation, and if you take a step back and look at the evolution of technology over the last 30 years, maybe longer, you always see these cycles of expansion, fragmentation, contraction, expansion, contraction. Go decentralize, go centralize, go decentralize, go centralize, as manifested in different types of technology paradigms. From client server, to storage, to microservices, to et cetera, et cetera. So I think we're going through another big bang, to a certain extent, whereby end up with more specialized data stacks for specific use cases, as you need performance, the data models, the tooling to best adapt to the particular task at hand, and the particular personas at hand. As the needs of the data analysts are quite different from the needs of an NL engineer, it's quite different from the needs of the data engineer. And what happens is, when you end up with these siloed stacks, you end up with new fragmentation, and new gaps that need to be filled with a new layer of innovation. And I suspect that, in part, that's what we're seeing right now, in terms of the next wave of data innovation. Whether it's in a service of FinTech use cases, or cyber use cases, or other, is a set of tools that end up having to try and stitch together those elements and bridge between them. So I see that as a fantastic gap to innovate around. I see, also, a fundamental need in creating a common data language, and common data management processes and governance across those different personas, because ultimately, the same underlying data these folks need, albeit in different mediums, different access models, different velocities, et cetera, the subject matter, if you will, the underlying raw data, and some of the taxonomies right on top of it, do need to be consistent. So, once again, a great opportunity to innovate, whether it's about semantic layers, whether it's about data mesh, whether it's about CICD tools for data engineers, and so on and so forth. >> I got to ask you, first of all, I see you have a friend you brought into the interview. You have a dog in the background who made a little cameo appearance. And that's awesome. Sitting right next to you, making sure everything's going well. On the AI thing, 'cause I think that's the hot trend here. >> Yeah. >> You're starting to see, that ChatGPT's got everyone excited, because it's kind of that first time you see kind of next-gen functionality, large-language models, where you can bring data in, and it integrates well. So, to me, I think, connecting the dots, this kind of speaks to the beginning of what will be a trend of really blending of data stacks together, or blending of models. And so, as more data modeling emerges, you start to have this AI stack kind of situation, where you have things out there that you can compose. It's almost very developer-friendly, conceptually. This is kind of new, but kind of the same concept's been working on with Google and others. How do you see this emerging, as an investor? What are some of the things that you're excited about, around the ChatGPT kind of things that's happening? 'Cause it brings it mainstream. Again, a million downloads, fastest applications get a million downloads, even among all the successes. So it's obviously hit a nerve. People are talking about it. What's your take on that? >> Yeah, so, I think that's a great point, and clearly, it feels like an iPhone moment, right, to the industry, in this case, AI, and lots of applications. And I think there's, at a high level, probably three different layers of innovation. One is on top of those platforms. What use cases can one bring to the table that would drive on top of a ChatGPT-like service? Whereby, the startup, the company, can bring some unique datasets to infuse and add value on top of it, by custom-focusing it and purpose-building it for a particular use case or particular vertical. Whether it's applying it to customer service, in a particular vertical, applying it to, I don't know, marketing content creation, and so on and so forth. That's one category. And I do know that, as one of my startups is in Y Combinator, this season, winter '23, they're saying that a very large chunk of the YC companies in this cycle are about GPT use cases. So we'll see a flurry of that. The next layer, the one below that, is those who actually provide those platforms, whether it's ChatGPT, whatever will emerge from the partnership with Microsoft, and any competitive players that emerge from other startups, or from the big cloud providers, whether it's Facebook, if they ever get into this, and Google, which clearly will, as they need to, to survive around search. The third layer is the enabling layer. As you're going to have more and more of those different large-language models and use case running on top of it, the underlying layers, all the way down to cloud infrastructure, the data infrastructure, and the entire set of tools and systems, that take raw data, and massage it into useful, labeled, contextualized features and data to feed the models, the AI models, whether it's during training, or during inference stages, in production. Personally, my focus is more on the infrastructure than on the application use cases. And I believe that there's going to be a massive amount of innovation opportunity around that, to reach cost-effective, quality, fair models that are deployed easily and maintained easily, or at least with as little pain as possible, at scale. So there are startups that are dealing with it, in various areas. Some are about focusing on labeling automation, some about fairness, about, speaking about cyber, protecting models from threats through data and other issues with it, and so on and so forth. And I believe that this will be, too, a big driver for massive innovation, the infrastructure layer. >> Awesome, and I love how you mentioned the iPhone moment. I call it the browser moment, 'cause it felt that way for me, personally. >> Yep. >> But I think, from a business model standpoint, there is that iPhone shift. It's not the BlackBerry. It's a whole 'nother thing. And I like that. But I do have to ask you, because this is interesting. You mentioned iPhone. iPhone's mostly proprietary. So, in these machine learning foundational models, >> Yeah. >> you're starting to see proprietary hardware, bolt-on, acceleration, bundled together, for faster uptake. And now you got open source emerging, as two things. It's almost iPhone-Android situation happening. >> Yeah. >> So what's your view on that? Because there's pros and cons for either one. You're seeing a lot of these machine learning laws are very proprietary, but they work, and do you care, right? >> Yeah. >> And then you got open source, which is like, "Okay, let's get some upsource code, and let people verify it, and then build with that." Is it a balance? >> Yes, I think- >> Is it mutually exclusive? What's your view? >> I think it's going to be, markets will drive the proportion of both, and I think, for a certain use case, you'll end up with more proprietary offerings. With certain use cases, I guess the fundamental infrastructure for ChatGPT-like, let's say, large-language models and all the use cases running on top of it, that's likely going to be more platform-oriented and open source, and will allow innovation. Think of it as the equivalent of iPhone apps or Android apps running on top of those platforms, as in AI apps. So we'll have a lot of that. Now, when you start going a little bit more into the guts, the lower layers, then it's clear that, for performance reasons, in particular, for certain use cases, we'll end up with more proprietary offerings, whether it's advanced silicon, such as some of the silicon that emerged from entrepreneurs who have left Google, around TensorFlow, and all the silicon that powers that. You'll see a lot of innovation in that area as well. It hopefully intends to improve the cost efficiency of running large AI-oriented workloads, both in inference and in learning stages. >> I got to ask you, because this has come up a lot around Azure and Microsoft. Microsoft, pretty good move getting into the ChatGPT >> Yep. >> and the open AI, because I was talking to someone who's a hardcore Amazon developer, and they said, they swore they would never use Azure, right? One of those types. And they're spinning up Azure servers to get access to the API. So, the developers are flocking, as you mentioned. The YC class is all doing large data things, because you can now program with data, which is amazing, which is amazing. So, what's your take on, I know you got to be kind of neutral 'cause you're an investor, but you got, Amazon has to respond, Google, essentially, did all the work, so they have to have a solution. So, I'm expecting Google to have something very compelling, but Microsoft, right now, is going to just, might run the table on developers, this new wave of data developers. What's your take on the cloud responses to this? What's Amazon, what do you think AWS is going to do? What should Google be doing? What's your take? >> So, each of them is coming from a slightly different angle, of course. I'll say, Google, I think, has massive assets in the AI space, and their underlying cloud platform, I think, has been designed to support such complicated workloads, but they have yet to go as far as opening it up the same way ChatGPT is now in that Microsoft partnership, and Azure. Good question regarding Amazon. AWS has had a significant investment in AI-related infrastructure. Seeing it through my startups, through other lens as well. How will they respond to that higher layer, above and beyond the low level, if you will, AI-enabling apparatuses? How do they elevate to at least one or two layers above, and get to the same ChatGPT layer, good question. Is there an acquisition that will make sense for them to accelerate it, maybe. Is there an in-house development that they can reapply from a different domain towards that, possibly. But I do suspect we'll end up with acquisitions as the arms race around the next level of cloud wars emerges, and it's going to be no longer just about the basic tooling for basic cloud-based applications, and the infrastructure, and the cost management, but rather, faster time to deliver AI in data-heavy applications. Once again, each one of those cloud suppliers, their vendor is coming with different assets, and different pros and cons. All of them will need to just elevate the level of the fight, if you will, in this case, to the AI layer. >> It's going to be very interesting, the different stacks on the data infrastructure, like I mentioned, analytics, data lake, AI, all happening. It's going to be interesting to see how this turns into this AI cloud, like data clouds, data operating systems. So, super fascinating area. Opher, thank you for coming on and sharing your expertise with us. Great to see you, and congratulations on the work. I'll give you the final word here. Give a plugin for what you're looking for for startup seats, pre-seeds. What's the kind of profile that gets your attention, from a seed, pre-seed candidate or entrepreneur? >> Cool, first of all, it's my pleasure. Enjoy our chats, as always. Hopefully the next one's not going to be in nine years. As to what I'm looking for, ideally, smart data entrepreneurs, who have come from a particular domain problem, or problem domain, that they understand, they felt it in their own 10 fingers, or millions of neurons in their brains, and they figured out a way to solve it. Whether it's a data infrastructure play, a cloud infrastructure play, or a very, very smart application that takes advantage of data at scale. These are the things I'm looking for. >> One final, final question I have to ask you, because you're a seasoned entrepreneur, and now coach. What's different about the current entrepreneurial environment right now, vis-a-vis, the past decade? What's new? Is it different, highly accelerated? What advice do you give entrepreneurs out there who are putting together their plan? Obviously, a global resource pool now of engineering. It might not be yesterday's formula for success to putting a venture together to get to that product-market fit. What's new and different, and what's your advice to the folks out there about what's different about the current environment for being an entrepreneur? >> Fantastic, so I think it's a great question. So I think there's a few axes of difference, compared to, let's say, five years ago, 10 years ago, 15 years ago. First and foremost, given the amount of infrastructure out there, the amount of open-source technologies, amount of developer toolkits and frameworks, trying to develop an application, at least at the application layer, is much faster than ever. So, it's faster and cheaper, to the most part, unless you're building very fundamental, core, deep tech, where you still have a big technology challenge to deal with. And absent that, the challenge shifts more to how do you manage my resources, to product-market fit, how are you integrating the GTM lens, the go-to-market lens, as early as possible in the product-market fit cycle, such that you reach from pre-seed to seed, from seed to A, from A to B, with an optimal amount of velocity, and a minimal amount of resources. One big difference, specifically as of, let's say, beginning of this year, late last year, is that money is no longer free for entrepreneurs, which means that you need to operate and build startup in an environment with a lot more constraints. And in my mind, some of the best startups that have ever been built, and some of the big market-changing, generational-changing, if you will, technology startups, in their respective industry verticals, have actually emerged from these times. And these tend to be the smartest, best startups that emerge because they operate with a lot less money. Money is not as available for them, which means that they need to make tough decisions, and make verticals every day. What you don't need to do, you can kick the cow down the road. When you have plenty of money, and it cushions for a lot of mistakes, you don't have that cushion. And hopefully we'll end up with companies with a more agile, more, if you will, resilience, and better cultures in making those tough decisions that startups need to make every day. Which is why I'm super, super excited to see the next batch of amazing unicorns, true unicorns, not just valuation, market rising with the water type unicorns that emerged from this particular era, which we're in the beginning of. And very much enjoy working with entrepreneurs during this difficult time, the times we're in. >> The next 24 months will be the next wave, like you said, best time to do a company. Remember, Airbnb's pitch was, "We'll rent cots in apartments, and sell cereal." Boy, a lot of people passed on that deal, in that last down market, that turned out to be a game-changer. So the crazy ideas might not be that bad. So it's all about the entrepreneurs, and >> 100%. >> this is a big wave, and it's certainly happening. Opher, thank you for sharing. Obviously, data is going to change all the markets. Refactoring, security, FinTech, user experience, applications are going to be changed by data, data operating system. Thanks for coming on, and thanks for sharing. Appreciate it. >> My pleasure. Have a good one. >> Okay, more coverage for the CloudNativeSecurityCon inaugural event. Data will be the key for cybersecurity. theCUBE's coverage continues after this break. (uplifting music)

>>Hello everyone. Welcome back to the Cube's Live coverage. I'm John Fur, host of the Cube. We got two sets here, three sets total. Another one in the executive center. It's our 10th year covering AWS Reinvent. I remember 2013 like it was yesterday. You know, now it's a massive of people buying out restaurants. 35,000 people now it's 55,000, soon to be 70,000 back. Great event. Continuing to set the standard in the industry. We had an amazing guest here, Leah Bibo, vice President of Product Marketing. She's in charge of the messaging, the product, overseeing how these products gonna market. Leah, great to see you. Thanks for joining me on the Cube today. >>Absolutely. It's great to be here. It's also my 10 reinvent, so it's, it's been a wild ride. >>Absolutely. Yeah. You and I were talking before we came on camera, how much we love products and yes, this is a product-centric company, has been from day one and you know, over the years watching the announcements, the tsunami of announcements, just all the innovation that's come out from AWS over the years has been staggering to say the least. Everyone always jokes about, oh my God, 5,000 new announcements, over 200 services you're managing and you're marketing them. It's pretty crazy right now. And Adam, as he comes on, as I called them, the solutions CEO on my piece I wrote on Friday, we're in an era where solutions, the products are enabling more solutions. Unpack the messaging around this cuz this is really big moment for aws. >>Absolutely. Well, I'll say first of all that we are a customer focused company that happens to be really good at innovating incredible products and services for our customers. So today the, the energy in the room and what Adam talked about, I think is focused on a few great things for customers that are really important for transformation. So we talked a lot about best price performance for workloads and we talked about extreme workloads, but if you think about the work that we've been doing to innovate on the silicon side, we're really talking about with Graviton all your workloads and getting really great price performance for all of them. You know, we came out with graviton three 25% faster than graviton two, also 60% more energy efficient. We talked about something that is emerging that I think is gonna be really big, which is simulation and really the ability to model these complex worlds and all the little interactions, which I think, you know, in the future as we have more complex environments like 3D simulation is gonna be a bigger part of every, every business's >>Business. You know, just as an aside, we were talking on the analyst segment that speeds and feeds are back and the old days and the data center days was like, we don't wanna talk about speeds and feeds about solutions and you know, the outcomes when you get the cloud, it was like, okay, get the workloads over there, but people want faster and lower cost performance workloads gotta be running at at high performance. And, and there's a real discussion around those. Let's unpack security data performance. What, what does that mean for customers? Because again, I get the workloads run fast. That's great. What else is behind the curtain, so to speak from a customer standpoint? >>Absolutely. Well I think if you're gonna move all your workloads to the cloud, you know, security is a really big area that's important. It's important to every one of our enterprise companies customers. Actually it's important to all of our customers and we've been working, you know, since the beginning of AWS to really create and build the most secure global infrastructure. And you know, as our customers have moved mission critical workloads, we've built out a lot more capabilities and now we have a whole portfolio of security services. And what we announced today is kind of game changing. The service called Security Lake, which brings together, you know, an ecosystem of security data in a format that's open. So you can share data between all of these sources and it's gonna give folks the opportunity to really be able to analyze data, find threats faster, and just kind of know their security posture. And I think, you know, as we talked about today, you don't wanna think about the cloud as unfathomable, the unfathomable, you really need to know that security. And I think that like a lot of things we discussed, security is a data opportunity, right? And I think we, we had a section on on data, but really if you look at the keynote across security, across solutions, across the purpose built things we made, it's all, it all comes down to data and it's really the, the transformational element that our customers >>Are. I mean the data secured is very integral part good call out there. And I, I wanna just double down on that real quick because I remember in 2014 I interviewed Steven Schmidt when he was the CSOs and back then in 2014, if you remember the conversation was this, the clouds not secure, gotta be on premises. Now in today's keynote, Adam says, and he laid out the whole global security footprint. There's a lot going on that Amazon has now become more secure than on-prem. He actually made that statement. So, and then plus you got thousands of security partners, third party partners, you got the open cyber security framework which you guys co-found with all the other, so you got securities not as a team sport, this is what they, they said yes, yes. What does that mean for customers? Because now this is a big deal. >>Well I think for customers, I mean it means nothing but goodness, right? But all of these thousands of security partners have really innovated and created solutions that our customers are using. But they all have different types of data in different silos. And to really get a full picture bringing all that data together is really important. And it's not easy today. You know, log data from different sources, data from detection services and really what customers want is an easier way to get it all together. Which is why we have the open OCS F and really analyze using the tools of their choice. And whether that's AWS tools for analytics or it's tools from our partners, customers need to be able to make that choice so that they can feel like their applications and their workloads are the most secure on aws. >>You know, I've been very impressed with guard duty and I've been following Merit Bear's blogs on online. She's in the security team, she's amazing. Shout out to her. She's been pushing guard duty for a long time now there's big news around guard duty. So you got EKS protection, you know, at Coan this was the biggest cloud native issue, the runtime of Kubernetes and inside the container and outside the container detection of threats, right? As a real software supply chain concern. How are you guys marketing that? This is a huge announcement. EKS protection I know is very nuanced but it's pretty big deal. >>It is a big deal. It is a big deal. And guard duty has been kind of like a quiet service that maybe you don't hear a lot about, but has been really, really popular with our customers. Adam mentioned that 85% of, you know, our top 2000 customers are using guard duty today. And it was a big moment. We launched EKS protection, you know, a little bit earlier and the customer uptake on that has been really incredible. And it is because you can protect your Kubernetes cluster, which is really important because so many customers are, you know, part of their migration to the cloud is containers. Yeah. And so we're pretty excited that now we can answer that question of what's going on inside the container. And so you have both, yeah, right. You know that your Kubernetes pluses are good and you know what's going on inside the container and it's just more threats that you can detect and protect >>Yourself from. You know, as an aside, I'm sure you're watching this, but you know, we go to a lot of events, you know, the C I C D pipeline as developers are getting higher velocity coding, it has moved in because of DevOps on the cloud into the C I C D pipeline. So you're seeing that developer takes some of those IT roles in the coding workflow, hence the, the shift left and or container security, which you guys now, now and are driving towards. But the security and the data teams are emerging as a very key element inside the organizational structure. When I sat down with Adam, one of the things he was very adamant about in my conversation was not just digital transformation, business transformation, structural organizational moves are making where it's not a department anymore, it is the company, a technology is the company when you transform. Absolutely. So digital is the process, business is the outcome. This is a really huge message. What's your reaction to that? What's, what can you share extra cuz that's, this is a big part of the thing. He hit it right outta the gate on the front end of the keynote. >>Absolutely. Absolutely. I mean I think, you know, companies have been migrating to the cloud for a while, but I think that this time that we're going through has really accelerated that migration And as part of that, you know, digital transformation has become real for a lot of companies. And it is true what Adam said there is technology transformation involved, there's data transformation involved, but it, it is transforming businesses. And I think if you look at some of the things that Adam talked about, you know, aws, supply chain, security Lake, aws clean rooms, and Omic, aws, omic, you know, those are all examples of data and the ability to work with data transforming different lines of business within a company, transforming horizontal processes like contact centers and like supply chain and also, you know, going into vertical specific solutions. So what it means is that as technology becomes more pervasive, as data becomes more pervasive, businesses are transforming and that means that a lot more people are going to use the cloud and interact with the cloud and they might not want to or be able to kind of use our building blocks. And so what's really exciting that what we're able to do is make cloud more accessible to lines of business folks to analysts, to security folks. So >>It's, yeah, and that's, and that's why I was calling my this this new trend I see as Amazon Classic, my words, not your words, I call the, hey there was classic cloud and then you got the next gen clown, the new next generation. And I was talking with Adrian Cockcroft, former aws, so he's now retired, he's gonna come on later today. He and I were talking, he use this thing of you got a bag of Legos aka primitives or a toy that's been assembled for you glued together, ones out of the box, but they're not mutually exclusive. You can build a durable application and foundation with the building blocks more durable. You can manage it, refine it, but you got the solution that breaks. You don't have as much flexibility but you gotta replace it. That's okay too. So like this is now kind of a new portfolio approach to the cloud. It's very interesting and I think, I think, I think that's what I took away from the keynote is that you can have both. >>Yes, absolutely. You can do both. I mean, we're gonna go full throttle on releasing innovations and pushing the envelope on compute and storage and databases and our core services because they matter. And having, you know, the choice to choose from a wide range of options. I mean that's what, that's what customers need. You know, if you're gonna run hpc, you're gonna run machine learning and you're gonna run your SAP applications or your Windows applications, you need choice of what you know, specific type of instance and compute capabilities. You need to get the price performance. It's, it's definitely not a one size fits all. It's a 600 instance type. Size fits all maybe. >>Exactly. And you got a lot of instance and we'll get to that in a second. Yeah, I love the themes. I love this keynote themes you had like at first space, but I get the whole data, then you look at it, you can look at it differently. Really good metaphor, the ocean one I love with the security because he mentioned you can have the confidence to explore go deep snorkeling versus scuba and knowing how much oxygen you have. I mean, so really cool metaphor made me think very provocative. So again, this is kind of why people go to AWS because you now have these, these abilities to do things differently, depend on the context of what products you're working with. Yes. Explain why that was the core theme. Was there any rationale behind that? Was it just how you guys saw it? I mean that was pretty clever. >>Well, I think that, you know, we're, we're talking about environments and I think in this world, you know, there's uncertainty in a lot of places and we really feel like all of us need to be prepared for different types of environments. And so we wanted to explore what that could look like. And I think, you know, we're fascinated by space and the vastness and it is very much like the world of data. I don't know about you, but I actually scuba dive. So I love the depths of the ocean. I loved working on that part. There's extremes, extreme workloads like hpc, extreme workloads like machine learning with the growing models and there's an imagination, which is also one of my favorite areas to explore. >>Yeah. And you use the Antarctica one for about the whole environment and extreme conditions. That's good in the performance. And I love that piece of it. And I want to get into the, some of the things I love the speeds and fee. I think the, the big innovation with the silicon we've been covering as, you know, like a blanket. The, he's got the GRAVITON three 25% faster than GRAVITON two, the C seven GN network intense workloads. This is kind of a big deal. I mean this is one of those things where it might not get picked up in the major press, but the network use cases are significant. Nira has been successful. Share your thoughts on these kinds of innovations because they look kind of small, but they're not, they're >>Big, they're not small for sure, especially at the scale that our customers are, are, are running their applications. Like every little optimization that you can get really makes a huge difference. And I think it's exciting. I mean you hit on, you kind of hit on it when we've been working on silicon for a while now we know that, you know, if we're gonna keep pushing the element, the envelope in these areas, we had to, we had to go down to the silicon. And I think that Nitro has really been what's kind of been a breakthrough for us. You know, reinventing that virtualization layer, offloading security and storage and networking to special purpose chips. And I think that it's not just in the area of network optimization, right? You saw training optimized instances and inference optimized instances and HPC optimized instances. So yeah, we are kind of looking at all the extremes of, of what customers want to do. >>I know you can't talk about the future, but I can almost connect the dots as you're talking. It's like, hmm, specialized instances, specialized chips, maybe programmability of workload, smart intelligence, generative AI, weaving in there. A lot of kind of cool things I can see around the corner around generative AI automation. Hey, go to this instance with that go here. This is kind of what I see kind of coming around the corner. >>And we have some of that with our instance optimizers, our cost optimizer products where, you know, we wanna help customers find the best instance for their workload, get the best utilization they possibly can, you know, cut costs, but still have the great performance. So I don't, I don't know about your future, John, it sounds great, but we have, you know, we're taking steps in that direction today. >>Still look in this code that's gonna be on this code. Okay. Any, okay, I wanna give you one final question. Well, well two questions. One was a comment Adam made, I'd love to get your reaction if you want to tighten your bell, come to the cloud. I thought that was a very interesting nuance. A lot of economic pressure. Cloud is an opportunity to get agile, time to value faster. We had Zs carve cube analyst who's with us earlier said, the more you spend on the cloud, the more you save. That was his line, which I thought was very smart. Spending more doesn't mean you're gonna lose money, means you can save money too. So a lot of cost optimization discussions. Absolutely. Hey, your belt come to the cloud. What does he mean by that? >>Well I think that in, in times where, you know, there's uncertainty and economic conditions, it is, it's really, you know, you sometimes wanna pull back kind of, you know, batten down the hatches. But the cloud really, and we saw this with C you know, if you, if you move to the cloud, not only can you cut costs, but you put yourself in this position where you can continue to innovate and you can be agile and you can be prepared for whatever environment you're in so that you know when things go back or you have a customer needs that and innovation that goes off like you, you can accelerate back up really, really quickly. And I think we talked about Airbnb, that example of how, you know, in, in that really tough time of covid when travel industry wasn't happening so much, you know, they were able to scale back and save money. And then at the same time when, you know, Airbnb's kind of once again travel came back, they were in a position to really, really quickly change with the, the customer needs. >>You know, Lee, it's always great talking with you. You got a lot of energy, you're so smart and we both love products and you're leading the product marketing. We have an Instagram challenge here on the cube. I'm gonna put you on the spot here. Oh my gosh. It's called Instagram. We called a bumper sticker section. We used to call it what's the bumper sticker for reinvent. But we kind of modernized that. If you were gonna do an Instagram reel right now, what would be the Instagram reel for reinvent Keynote day one. As we look for, we got Verner, we'll probably talk about productivity with developers. What's the Instagram reel for reinvent? >>Wow. That means I have to get short with it, right? I am, I'm not always, that's still wrong answer. Yeah, well I think, you know, this is really big day one, so it's excitement, it's, we're glad to be here. We have a lot coming for you. We're super excited. And if you think about it, it's price, performance, it's data, it's security and it's solutions for purpose-built use cases. >>Great job. Congratulations. I love the message. I love how you guys had the theme. I thought it was great. And it's great to see Amazon continue to innovate with, with the, with the, with the innovation on the product side. But as we get into transformation, starting to see these solutions and the ecosystem is thriving and looking forward to hearing the, the new partner, chief Aruba tomorrow. Absolutely. See what she's got a new plan apparently unveiling. So exciting. Everyone's pretty excited. Thanks for coming >>On. Great. Great. Thanks for having >>Me. All right. Leah, here in the cube. You are the cube, the leader in tech coverage. I'm John Fur, your host. More live coverage after the short break. We'll be right back here. Day two of the cube, day one of reinvent. Lot of great action. Three, four days of wall to wall coverage. We'll be right back.

>>Hey guys and girls, welcome back to Motor City, Lisa Martin here with John Furrier on the Cube's third day of coverage of Coon Cloud Native Con North America. John, we've had some great conversations over the last two and a half days. We've been talking about identity and security management as a critical need for enterprises within the cloud native space. We're gonna have another quick conversation >>On that. Yeah, we got a great segment coming up from someone who's been in the industry, a long time expert, running a great company. Now it's gonna be one of those pieces that fits into what we call super cloud. Others are calling cloud operating system. Some are calling just Cloud 2.0, 3.0. But there's definitely a major trend happening around how cloud is going Next generation. We've been covering it. So this segment should be >>Great. Let's unpack those trends. One of our alumni is back with us, O Rika Zi, co-founder and CEO of Aerio. Omri. Great to have you back on the >>Cube. Thank you. Great to be here. >>So identity move to the cloud, Access authorization did not talk to us about why you found it assertive, what you guys are doing and how you're flipping that script. >>Yeah, so back 15 years ago, I helped start Azure at Microsoft. You know, one of the first few folks that you know, really focused on enterprise services within the Azure family. And at the time I was working for the guy who ran all of Windows server and you know, active directory. He called it the linchpin workload for the Windows Server franchise, like big words. But what he meant was we had 95% market share and all of these new SAS applications like ServiceNow and you know, Workday and salesforce.com, they had to invent login and they had to invent access control. And so we were like, well, we're gonna lose it unless we figure out how to replace active directory. And that's how Azure Active Directory was born. And the first thing that we had to do as an industry was fix identity, right? Yeah. So, you know, we worked on things like oof Two and Open, Id Connect and SAML and Jot as an industry and now 15 years later, no one has to go build login if you don't want to, right? You have companies like Odd Zero and Okta and one login Ping ID that solve that problem solve single sign-on, on the web. But access Control hasn't really moved forward at all in the last 15 years. And so my co-founder and I who were both involved in the early beginnings of Azure Active directory, wanted to go back to that problem. And that problem is even bigger than identity and it's far from >>Solved. Yeah, this is huge. I think, you know, self-service has been a developer thing that's, everyone knows developer productivity, we've all experienced click sign in with your LinkedIn or Twitter or Google or Apple handle. So that's single sign on check. Now the security conversation kicks in. If you look at with this no perimeter and cloud, now you've got multi-cloud or super cloud on the horizon. You've got all kinds of opportunities to innovate on the security paradigm. I think this is kind of where I'm hearing the most conversation around access control as well as operationally eliminating a lot of potential problems. So there's one clean up the siloed or fragmented access and two streamlined for security. What's your reaction to that? Do you agree? And if not, where, where am I missing that? >>Yeah, absolutely. If you look at the life of an IT pro, you know, back in the two thousands they had, you know, l d or active directory, they add in one place to configure groups and they'd map users to groups. And groups typically corresponded to roles and business applications. And it was clunky, but life was pretty simple. And now they live in dozens or hundreds of different admin consoles. So misconfigurations are rampant and over provisioning is a real problem. If you look at zero trust and the principle of lease privilege, you know, all these applications have these course grained permissions. And so when you have a breach, and it's not a matter of if, it's a matter of when you wanna limit the blast radius of you know what happened, and you can't do that unless you have fine grained access control. So all those, you know, all those reasons together are forcing us as an industry to come to terms with the fact that we really need to revisit access control and bring it to the age of cloud. >>You guys recently, just this week I saw the blog on Topaz. Congratulations. Thank you. Talk to us about what that is and some of the gaps that's gonna help sarto to fill for what's out there in the marketplace. >>Yeah, so right now there really isn't a way to go build fine grains policy based real time access control based on open source, right? We have the open policy agent, which is a great decision engine, but really optimized for infrastructure scenarios like Kubernetes admission control. And then on the other hand, you have this new, you know, generation of access control ideas. This model called relationship based access control that was popularized by Google Zanzibar system. So Zanzibar is how they do access control for Google Docs and Google Drive. If you've ever kind of looked at a Google Doc and you know you're a viewer or an owner or a commenter, Zanzibar is the system behind it. And so what we've done is we've married these two things together. We have a policy based system, OPPA based system, and at the same time we've brought together a directory, an embedded directory in Topaz that allows you to answer questions like, does this user have this permission on this object? And bringing it all together, making it open sources a real game changer from our perspective, real >>Game changer. That's good to hear. What are some of the key use cases that it's gonna help your customers address? >>So a lot of our customers really like the idea of policy based access management, but they don't know how to bring data to that decision engine. And so we basically have a, you know, a, a very opinionated way of how to model that data. So you import data out of your identity providers. So you connect us to Okta or oze or Azure, Azure Active directory. And so now you have the user data, you can define groups and then you can define, you know, your object hierarchy, your domain model. So let's say you have an applicant tracking system, you have nouns like job, you know, know job descriptions or candidates. And so you wanna model these things and you want to be able to say who has access to, you know, the candidates for this job, for example. Those are the kinds of rules that people can express really easily in Topaz and in assertive. >>What are some of the challenges that are happening right now that dissolve? What, what are you looking at to solve? Is it complexity, sprawl, logic problems? What's the main problem set you guys >>See? Yeah, so as organizations grow and they have more and more microservices, each one of these microservices does authorization differently. And so it's impossible to reason about the full surface area of, you know, permissions in your application. And more and more of these organizations are saying, You know what, we need a standard layer for this. So it's not just Google with Zanzibar, it's Intuit with Oddy, it's Carta with their own oddy system, it's Netflix, you know, it's Airbnb with heed. All of them are now talking about how they solve access control extracted into its own service to basically manage complexity and regain agility. The other thing is all about, you know, time to market and, and tco. >>So, so how do you work with those services? Do you replace them, you unify them? What is the approach that you're taking? >>So basically these organizations are saying, you know what? We want one access control service. We want all of our microservices to call that thing instead of having to roll out our own. And so we, you know, give you the guts for that service, right? Topaz is basically the way that you're gonna go implement an access control service without having to go build it the same way that you know, large companies like Airbnb or Google or, or a car to >>Have. What's the competition look like for you guys? I'm not really seeing a lot of competition out there. Are there competitors? Are there different approaches? What makes you different? >>Yeah, so I would say that, you know, the biggest competitor is roll your own. So a lot of these companies that find us, they say, We're sick and tired of investing 2, 3, 4 engineers, five engineers on this thing. You know, it's the gift that keeps on giving. We have to maintain this thing and so we can, we can use your solution at a fraction of the cost a, a fifth, a 10th of what it would cost us to maintain it locally. There are others like Sty for example, you know, they are in the space, but more in on the infrastructure side. So they solve the problem of Kubernetes submission control or things like that. So >>Rolling your own, there's a couple problems there. One is do they get all the corner cases who built a they still, it's a company. Exactly. It's heavy lifting, it's undifferentiated, you just gotta check the box. So probably will be not optimized. >>That's right. As Bezo says, only focus on the things that make your beer taste better. And access control is one of those things. It's part of your security, you know, posture, it's a critical thing to get right, but you know, I wanna work on access control, said no developer ever, right? So it's kind of like this boring, you know, like back office thing that you need to do. And so we give you the mechanisms to be able to build it securely and robustly. >>Do you have a, a customer story example that is one of your go-tos that really highlights how you're improving developer productivity? >>Yeah, so we have a couple of them actually. So there's the largest third party B2B marketplace in the us. Free retail. Instead of building their own, they actually brought in aer. And what they wanted to do with AER was be the authorization layer for both their externally facing applications as well as their internal apps. So basically every one of their applications now hooks up to AER to do authorization. They define users and groups and roles and permissions in one place and then every application can actually plug into that instead of having to roll out their own. >>I'd like to switch gears if you don't mind. I get first of all, great update on the company and progress. I'd like to get your thoughts on the cloud computing market. Obviously you were your legendary position, Azure, I mean look at the, look at the progress over the past few years. Just been spectacular from Microsoft and you set the table there. Amazon web service is still, you know, thundering away even though earnings came out, the market's kind of soft still. You know, you see the cloud hyperscalers just continuing to differentiate from software to chips. Yep. Across the board. So the hyperscalers kicking ass taking names, doing great Microsoft right up there. What's the future? Cuz you now have the conversation where, okay, we're calling it super cloud, somebody calling multi-cloud, somebody calling it distributed computing, whatever you wanna call it. The old is now new again, it just looks different as cloud becomes now the next computer industry, >>You got an operating system, you got applications, you got hardware, I mean it's all kind of playing out just on a massive global scale, but you got regions, you got all kinds of connected systems edge. What's your vision on how this plays out? Because things are starting to fall into place. Web assembly to me just points to, you know, app servers are coming back, middleware, Kubernetes containers, VMs are gonna still be there. So you got the progression. What's your, what's your take on this? How would you share, share your thoughts to a friend or the industry, the audience? So what's going on? What's, what's happening right now? What's, what's going on? >>Yeah, it's funny because you know, I remember doing this quite a few years ago with you probably in, you know, 2015 and we were talking about, back then we called it hybrid cloud, right? And it was a vision, but it is actually what's going on. It just took longer for it to get here, right? So back then, you know, the big debate was public cloud or private cloud and you know, back when we were, you know, talking about these ideas, you know, we said, well you know, some applications will always stay on-prem and some applications will move to the cloud. I was just talking to a big bank and they basically said, look, our stated objective now is to move everything we can to the public cloud and we still have a large private cloud investment that will never go away. And so now we have essentially this big operating system that can, you know, abstract all of this stuff. So we have developer platforms that can, you know, sit on top of all these different pieces of infrastructure and you know, kind of based on policy decide where these applications are gonna be scheduled. So, you know, the >>Operating schedule shows like an operating system function. >>Exactly. I mean like we now, we used to have schedulers for one CPU or you know, one box, then we had schedulers for, you know, kind of like a whole cluster and now we have schedulers across the world. >>Yeah. My final question before we kind of get run outta time is what's your thoughts on web assembly? Cuz that's getting a lot of hype here again to kind of look at this next evolution again that's lighter weight kind of feels like an app server kind of direction. What's your, what's your, it's hyped up now, what's your take on that? >>Yeah, it's interesting. I mean back, you know, what's, what's old is new again, right? So, you know, I remember back in the late nineties we got really excited about, you know, JVMs and you know, this notion of right once run anywhere and yeah, you know, I would say that web assembly provides a pretty exciting, you know, window into that where you can take the, you know, sandboxing technology from the JavaScript world, from the browser essentially. And you can, you know, compile an application down to web assembly and have it real, really truly portable. So, you know, we see for example, policies in our world, you know, with opa, one of the hottest things is to take these policies and can compile them to web assemblies so you can actually execute them at the edge, you know, wherever it is that you have a web assembly runtime. >>And so, you know, I was just talking to Scott over at Docker and you know, they're excited about kind of bringing Docker packaging, OCI packaging to web assemblies. So we're gonna see a convergence of all these technologies right now. They're kind of each, each of our, each of them are in a silo, but you know, like we'll see a lot of the patterns, like for example, OCI is gonna become the packaging format for web assemblies as it is becoming the packaging format for policies. So we did the same thing. We basically said, you know what, we want these policies to be packaged as OCI assembly so that you can sign them with cosign and bring the entire ecosystem of tools to bear on OCI packages. So convergence is I think what >>We're, and love, I love your attitude too because it's the open source community and the developers who are actually voting on the quote defacto standard. Yes. You know, if it doesn't work, right, know people know about it. Exactly. It's actually a great new production system. >>So great momentum going on to the press released earlier this week, clearly filling the gaps there that, that you and your, your co-founder saw a long time ago. What's next for the assertive business? Are you hiring? What's going on there? >>Yeah, we are really excited about launching commercially at the end of this year. So one of the things that we were, we wanted to do that we had a promise around and we delivered on our promise was open sourcing our edge authorizer. That was a huge thing for us. And we've now completed, you know, pretty much all the big pieces for AER and now it's time to commercially launch launch. We already have customers in production, you know, design partners, and you know, next year is gonna be the year to really drive commercialization. >>All right. We will be watching this space ery. Thank you so much for joining John and me on the keep. Great to have you back on the program. >>Thank you so much. It was a pleasure. >>Our pleasure as well For our guest and John Furrier, I'm Lisa Martin, you're watching The Cube Live. Michelle floor of Con Cloud Native Con 22. This is day three of our coverage. We will be back with more coverage after a short break. See that.

>>Hey guys. Welcome back to Detroit, Michigan. Lisa Martin and John Furrier here live with the cube at Coan Cloud Native Con North America. John, it's been a great day. This is day one of our coverage of three days of coverage. Kubernetes is growing up. Yeah, it's maturing. >>Yeah. We got three days of wall to wall coverage, all about Kubernetes. We about security, large scale, cloud native at scale. That's the big focus. This next segment's gonna be really awesome. You have a fast growing private company and a practitioner, big name, blue chip practitioner, building out next NextGen Cloud first, transforming, then building out the next level. This is classic of what we call super cloud-like, like interview. It's gonna be great. I'm looking forward >>To this anytime we can talk about Super Cloud. All right, please welcome back. One of our alumni, Bani is here, the CEO of Rafe. Great to see you Santos. Ula also joins us, the global head of Cloud SRE at Mass Mutual. Ge. Great to have you on the program. Thanks >>For having us. Thank you for having me. >>So Steve, you've been on the queue many times. You were on just recently with the momentum that that's around us today with the maturation of Kubernetes, the collaboration of the community, the recognition of the community. What are some of the things that you're excited about with on, on day one of the show? >>Wow, so many new companies. I mean, there are companies that I don't know who are here. And I, I, I live in this industry and I'm seeing companies that I don't know, which is a good thing. I mean, it means that the, the community's growing. But at the same time, I'm also seeing another thing, which is I have met more enterprise representatives at this show than other coupons. Like when we hung out at, you know, in Valencia for example, or even, you know, other places. It hasn't been this many people, which means, and this is, this is a good thing that enterprises are now taking Kubernetes seriously. It's not a toy. It's not just for developers. It's enterprises who are now investing in Kubernetes as a foundational component, right. For their applications going forward. And that to me is very, very good. >>Definitely becoming foundational. >>Yep. Well, you guys got a great traction. We had many interviews at the Cube and you got a practitioner here with you. You guys are both pioneering kind of what I call the next gen cloud. First you gotta get through gen one, which you guys done at Mass Mutual, extremely well, take us through the story of your transformation. Cause you're on the, at the front end now of that next inflection point. But take us through how you got here. You had a lot of transformation success at Mass Mutual. >>So I was actually talking about this topic few, few minutes back, right? And, and the whole cloud journey in big companies, large financial institutions, healthcare industry or, or our insurance sector. It takes generations of leadership to get, to get to that perfection level. And, and ideally the, the, the cloud for strategy starts in, and then, and then how do you, how do you standardize and optimize cloud, right? You know, that that's, that's the second gen altogether. And then operationalization of the cloud. And especially if, you know, if you're talking about Kubernetes, you know, in the traditional world, you know, almost every company is running middleware and their applications in middleware. And then containerization is a topic that come, that came in. And docker is, is you know, basically the runtime containerization. So that came in first and from Docker, you know, eventually when companies started adopting Docker, Docker Swarm is one of the technologies that they adopted. And eventually when, when, when we were taking it to a more complicated application implementations or modernization efforts, that's when Kubernetes played a key role. And, and Hasi was pointing out, you know, like you never saw so many companies working on Kubernetes. So that should tell you one story, right? How fast Kubernetes is growing and how important it is for your cloud strategy. So, >>And your success now, and what are you thinking about now? What's on your agenda now as you look forward? What's on your plate? What are you guys doing right now? >>So we are, we are past the stage of, you know, proof of concepts, proof of technologies, pilot implementations. We are actually playing it, you know, the real game now. So in the past I used the quote, you know, like, hello world to real world. So we are actually playing in the real world, not, not in the hello world anymore. Now, now this is where the real time challenges will, will pop up, right? So if you're talking about standardizing it and then optimizing the cloud and how do you put your governance structure in place? How do you make sure your regulations are met? You know, the, the, the demands that come out of regulations are met and, and how, how are you going to scale it and, and, and while scaling, however you wanna to keep up with all the governance and regulations that come with it. So we are in that stage today. >>Has Steve talked about, you talked about the great evolution of what's going on at Mass Mutual has talked a little bit about who, you mentioned one of the things that's surprising you about this Coan and Detroit is that you're seeing a lot more enterprise folks here who, who's deciding in the organization and your customer conversations, Who are the deci decision makers in terms of adoption of Kubernetes these days? Is that elevating? >>Hmm. Well this guy, >>It's usually, you know, one of the things I'm seeing here, and John and I have talked about this in the past, this idea of a platform organization and enterprises. So consistently what I'm seeing is, you know, somebody, a cto, CIO level, you know, individual is making a determin decision. I have multiple internal buss who are now modernizing applications. They're individually investing in DevOps. And this is not a good investment for my business. I'm going to centralize some of this capability so that we can all benefit together. And that team is essentially a platform organization and they're making Kubernetes a shared services platform so that everybody else can come and, and, and sort of, you know, consume it. So what that means to us is our customer is a platform organization and their customer is a developer. So we have to make two constituencies successful. Our customer who's providing a multi-tenant platform, and then their customer who's a developer, both have to be happy. If you don't solve for both, you know, constituencies, you're not gonna be >>Successful. You're targeting the builder of the infrastructure and the consumer of that infrastructure. >>Yes sir. It has to be both. Exactly. Right. Right. So, so that look, honestly, that it, it, you know, it takes iterations to figure these things out, right? But this is a consistent theme that I am seeing. In fact, what I would argue now is that every enterprise should be really stepping back and thinking about what is my platform strategy. Cuz if you don't have a platform strategy, you're gonna have a bunch of different teams who are doing different things and some will be successful and look, some will not be. And that is not good for business. >>Yeah. And, and stage, I wanna get to you, you mentioned that your transformation was what you look forward and your title, global head of cloud sre. Okay, so sre, we all know came from Google, right? Everyone wants to be like Google, but no one wants to be like Google, right? And no one is Google, Google's a unique thing. It's only one Google. But they had the dynamic and the power dynamic of one person to large scale set of servers or infrastructure. But concept is, is, is can be portable, but, but the situation isn't. So board became Kubernetes, that's inside baseball. So you're doing essentially what Google did at their scale you're doing for Mass Mutual. That's kind of what's happening. Is that kind of how I see it? And you guys are playing in there partnering. >>So I I totally agree. Google introduce, sorry, Ty engineering. And, and if you take, you know, the traditional transformation of the roles, right? In the past it was called operations and then DevOps ops came in and then SRE is is the new buzzword. And the future could be something like product engineering, right? And, and, and in this journey, you know, here is what I tell, you know, folks on my side like what worked for Google might not work for a financial company, might not work for an insurance company. So, so, so it's, it's okay to use the word sre, but but the end of the day that SRE has to be tailored down to, to your requirements and and, and the customers that you serve and the technology that you serve. Yep. >>And this is, this is why I'm coming back, this platform engineering. At the end of the day, I think SRE just translates to, you're gonna have a platform engineering team cuz you gotta enable developers to be producing more code faster, better, cheaper guardrails policy. So this, it's kind of becoming the, you serve the business, which is now the developers it used to serve the business Yep. Back in the old days. Hey, the, it serves the business. Yep. Which is a terminal, >>Which is actually true >>Now it the new, it serves the developers, which is the business. Which is the business. Because if digital transformation goes to completion, the company is the app. Yep. >>And the, you know, the, the hard line between development and operations, right? So, so that's thining down over the time, you know, like that that line might disappear. And, and, and that's where asari is fitting in. >>Yeah. And they're building platforms to scale the enablement up that what is, so what is the key challenges you guys are, are both building out together this new transformational direction? What's new and what's the same, The same is probably the business results, but what's the new dynamic involved in rolling it out and making people successful? You got the two constituents, the builders of the infrastructures and the consumers of the services on the other side. What's the new thing? >>So the new thing if, if I may go fast these, so the faster market to, you know, value, right? That we are bringing to the table. That's, that's very important. You know, business has an idea. How do you get that idea implemented in terms of technology and, and take it into real time. So that journey we have cut down, right? Technology is like Kubernetes. It makes, it makes, you know, an IT person's life so easy that, that they can, they can speed up the process in, in, in a traditional way. What used to take like an year or six months can be done in a month today or or less than that, right? So, so there's definitely the losses, speed, velocity, agility in general, and then flexibility. And then the automation that we put in, especially if you have to maintain like thousands of clusters, you know, these, these are today like, you know, it is possible to, to make that happen with a click off a button. In the past it used to take like, you know, probably, you know, a hundred, a hundred percent team and operational team to do it. And a lot of time. But, but, but that automation is happening. You know, and we can get into the technology as much as possible. But, but, you know, blueprinting and all that stuff made >>It possible. Well say that for another interview, we'll do it take time. >>But the, the end user on the other end, the consumer doesn't have the patience that they once had. Right? Right. It's, I want this in my lab now. Now, how does the culture of Mass Mutual, how is it evolv to be able to deliver the velocity that your customers are demanding? >>So if once in a while, you know, it's important to step yourself into the customer's shoes and think it from their, from their, from their perspective, business does not care how you're running your IT shop. What they care about is your stability of the product and the efficiencies of the product and, and, and how, how, how easy it is to reach out to the customers and how well we are serving the customers, right? So whether I'm implementing Docker in the background, Dr. Swam or es you know, business doesn't even care about it. What they really care about it is if your environment goes down, it's a problem. And, and, and if you, if your environment or if your solution is not as efficient as the business needs, that's the problem, right? So, so at that point, the business will step in. So our job is to make sure, you know, from an, from a technology perspective, how fast you can make implement it and how efficiently you can implement it. And at the same time, how do you play within the guardrails of security and compliance. >>So I was gonna ask you if you have VMware in your environment, cause a lot of clients compare what vCenter does for Kubernetes is really needed. And I think that's what you guys got going on. I I can say that you're the v center of Kubernetes. I mean, as a, as an as an metaphor, a place to manage it all is all 1, 1 1 paint of glass, so to speak. Is that how you see success in your environment? >>So virtualization has gone a long way, you know where we started, what we call bare metal servers, and then we virtualized operating systems. Now we are virtualizing applications and, and we are virtualizing platforms as well, right? So that's where Kubernetes basically got. >>So you see the need for a vCenter like thing for Uber, >>Definitely a need in the market in the way you need to think is like, you know, let's say there is, there is an insurance company who actually mented it and, and they gain the market advantage. Right? Now the, the the competition wants to do it as well, right? So, so, so there's definitely a virtualization of application layer that, that, that's very critical and it's, it's a critical component of cloud strategy as >>A whole. See, you're too humble to say it. I'll say you like the V center of Kubernetes, Explain what that means and your turn. If I said that to you, what would you react? How would you react to that? Would say bs or would you say on point, >>Maybe we should think about what does vCenter do today? Right? It's, it's so in my opinion, by the way, well vCenter in my opinion is one of the best platforms ever built. Like ha it's the best platform in my opinion ever built. It's, VMware did an amazing job because they took an IT engineer and they made him now be able to do storage management, networking management, VMs, multitenancy, access management audit, everything that you need to run a data center, you can do from a single, essentially single >>Platform, from a utility standpoint home >>Run. It's amazing, right? Yeah, it is because you are now able to empower people to do way more. Well why are we not doing that for Kubernetes? So the, the premise man Rafa was, well, oh, bless, I should have IT engineers, same engineers now they should be able to run fleets of clusters. That's what people that mass major are able to do now, right? So to that end, now you need cluster management, you need access management, you need blueprinting, you need policy management, you need ac, you know, all of these things that have happened before chargebacks, they used to have it in, in V center. Now they need to happen in other platforms. But for es so should do we do many of the things that vCenter does? Yes. >>Kind >>Of. Yeah. Are we a vCenter for es? Yeah, that is a John Forer question. >>All right, well, I, I'll, the speculation really goes back down to the earlier speed question. If you can take away the, the complexity and not make it more steps or change a tool chain or do something, then the devs move faster and the service layer that serves the business, the new organization has to enable speed. So this, this is becoming a, a real discussion point in the industry is that, oh yeah, we've got new tool, look at the shiny new toy. But if it doesn't move the needle, does it help productivity for developers? And does it actually scale up the enablement? That's the question. So I'm sure you guys are thinking about this a lot, what's your reaction? >>Yeah, absolutely. And one thing that just, you know, hit my mind is think about, you know, the hoteling industry before Airbnb and after Airbnb, right? Or, or, or the taxi industry, you know, before Uber and after Uber, right? So if I'm providing a platform, a Kubernetes platform for my application folks or for my application partners, they have everything ready. All they need to do is like, you know, build their application and deployed and running, right? They, they, they don't have to worry about provisioning of the servers and then building the middleware on top of it and then, you know, do a bunch of testing to make sure, you know, they, they, they iron out all the, all the compatible issues and whatnot. Yeah. Now, now, today, all I, all I say is like, hey, you have, we have a platform built for you. You just build your application and then deploy it in a development environment. That's where you put all the pieces of puzzle together, make sure you see your application working, and then the next thing that, that you do is like, you know, you know, build >>Production, chip, build production, go and chip release it. Yeah, that's the nirvana. But then we're there. I mean, we're there now we're there. So we see the future. Because if you, if that's the case, then the developers are the business. They have to be coding more features, they have to react to customers. They might see new business opportunities from a revenue standpoint that could be creatively built, got low code, no code, headless systems. These things are happening where this I call the architectural list environment where it's like, you don't need architecture, it's already happening. >>Yeah. And, and on top of it, you know, if, if someone has an idea, they want to implement an idea real quick, right? So how do you do it? Right? And, and, and you don't have to struggle building an environment to implement your idea and testers in real time, right? So, so from an innovation perspective, you know, agility plays a key role. And, and that, that's where the Kubernetes platforms or platforms like Kubernetes >>Plays. You know, Lisa, when we talked to Andy Chasy, when he was the CEO of aws, either one on one or on the cube, he always said, and this is kind of happening, companies are gonna be builders where it's not just utility. You need that table stakes to enable that new business idea. And so he, this last keynote, he did this big thing like, you know, think like your developers are the next entrepreneurial revenue generators. And I think that, I think starting to see that, what do you think about that? You see that coming sooner than later? Or is that in, in sight or is that still ways away? >>I, I think it's already happening at a level, at a certain level now. Now the question comes back to, you know, taking it to the reality, right? Yeah. I mean, you can, you can do your proof of concept, proof of technologies, and then, and then prove it out. Like, Hey, I got a new idea. This idea is great. Yeah. And, and it's to the business advantage, right? But we really want to see it in production live where your customers are actually >>Using it and the board meetings, Hey, we got a new idea that came in, generating more revenue, where'd that come from? Agile developer. Again, this is real. Yeah, >>Yeah. >>Absolutely agree. Yeah. I think, think both of you gentlemen said a word in, in your, as you were talking, you used the word guardrails, right? I think, you know, we're talking about rigidity, but you know, the really important thing is, look, these are enterprises, right? They have certain expectations. Guardrails is key, right? So it's automation with the guardrails. Yeah. Guardrails are like children, you know, you know, shouldn't be hurt. You know, they're seen but not hurt. Developers don't care about guard rails. They just wanna go fast. They also bounce >>Around a little bit. Yeah. Off the guardrails. >>One thing we know that's not gonna slow down is, is the expectations, right? Of all the consumers of this, the Ds the business, the, the business top line, and of course the customers. So the ability to, to really, as your website says, let's see, make life easy for platform teams is not trivial. And clearly what you guys are talking about here is you're, you're really an enabler of those platform teams, it sounds like to me. Yep. So, great work, guys. Thank you so much for both coming on the program, talking about what you're doing together, how you're seeing the, the evolution of Kubernetes, why, and really what the focus should be on those platform games. We appreciate all your time and your insights. >>Thank you so much for having us. Thanks >>For our pleasure. For our guests and for John Furrier, I'm Lisa Martin. You're watching The Cube Live, Cobe Con, Cloud Native con from Detroit. We've out with our next guest in just a minute, so stick around.

(bright upbeat music) >> Hey, guys. Welcome back to Detroit, Michigan. Lisa Martin and John Furrier here live with "theCUBE" at KubeCon CloudNativeCon, North America. John, it's been a great day. This is day one of our coverage of three days of coverage. Kubernetes is growing up. It's maturing. >> Yeah, we got three days of wall-to-wall coverage, all about Kubernetes. We heard about Security, Large scale, Cloud native at scale. That's the big focus. This next segment's going to be really awesome. You have a fast growing private company and a practitioner, big name, blue chip practitioner, building out next-gen cloud. First transforming, then building out the next level. This is classic, what we call Super Cloud-Like interview. It's going to be great. I'm looking forward to this. >> Anytime we can talk about Super Cloud, right? Please welcome back, one of our alumni, Haseeb Budhani is here, the CEO of Rafay. Great to see you. Santhosh Pasula, also joins us, the global head of Cloud SRE at Mass Mutual. Guys, great to have you on the program. >> Thanks for having us. >> Thank you for having me. >> So, Haseeb, you've been on "theCUBE" many times. You were on just recently, with the momentum that's around us today with the maturation of Kubernetes, the collaboration of the community, the recognition of the community. What are some of the things that you're excited about with on day one of the show? >> Wow, so many new companies. I mean, there are companies that I don't know who are here. And I live in this industry, and I'm seeing companies that I don't know, which is a good thing. It means that the community's growing. But at the same time, I'm also seeing another thing, which is, I have met more enterprise representatives at this show than other KubeCons. Like when we hung out at in Valencia, for example, or even other places, it hasn't been this many people. Which means, and this is a good thing that enterprises are now taking Kubernetes seriously. It's not a toy. It's not just for developers. It's enterprises who are now investing in Kubernetes as a foundational component for their applications going forward. And that to me is very, very good. >> Definitely, becoming foundational. >> Haseeb: Yeah. >> Well, you guys got a great traction. We had many interviews at "theCUBE," and you got a practitioner here with you guys, are both pioneering, kind of what I call the next-gen cloud. First you got to get through Gen-One, which you guys done at Mass Mutual extremely well. Take us through the story of your transformation? 'Cause you're on at the front end now of that next inflection point. But take us through how you got here? You had a lot of transformation success at Mass Mutual? >> So, I was actually talking about this topic few minutes back. And the whole cloud journey in big companies, large financial institutions, healthcare industry or insurance sector, it takes generations of leadership to get to that perfection level. And ideally, the cloud for strategy starts in, and then how do you standardize and optimize cloud, right? That's the second-gen altogether, and then operationalization of the cloud. And especially if you're talking about Kubernetes, in the traditional world, almost every company is running middleware and their applications in middleware. And their containerization is a topic that came in. And Docker is basically the runtime containerization. So, that came in first, and from Docker, eventually when companies started adopting Docker, Docker Swarm is one of the technologies that they adopted. And eventually, when we were taking it to a more complicated application implementations or modernization efforts, that's when Kubernetes played a key role. And as Haseeb was pointing out, you never saw so many companies working on Kubernetes. So, that should tell you one story, right? How fast Kubernetes is growing, and how important it is for your cloud strategy. >> And your success now, and what are you thinking about now? What's on your agenda now? As you look forward, what's on your plate? What are you guys doing right now? >> So we are past the stage of proof of concepts, proof of technologies, pilot implementations. We are actually playing it, the real game now. In the past, I used the quote, like "Hello world to real world." So, we are actually playing in the real world, not in the hello world anymore. Now, this is where the real time challenges will pop up. So, if you're talking about standardizing it, and then optimizing the cloud, and how do you put your governance structure in place? How do you make sure your regulations are met? The demands that come out of regulations are met? And how are you going to scale it? And while scaling, how are you going to keep up with all the governance and regulations that come with it? So we are in that stage today. >> Haseeb talked about, you talked about the great evolution of what's going on at Mass Mutual. Haseeb talk a little bit about who? You mentioned one of the things that's surprising you about this KubeCon in Detroit, is that you're seeing a lot more enterprise folks here? Who's deciding in the organization and your customer conversations? Who are the decision makers in terms of adoption of Kubernetes these days? Is that elevating? >> Hmm. Well, this guy. (Lisa laughing) One of the things I'm seeing here, and John and I have talked about this in the past, this idea of a platform organization and enterprises. So, consistently what I'm seeing, is somebody, a CTO, CIO level, an individual is making a decision. I have multiple internal Bus who are now modernizing applications. They're individually investing in DevOps, and this is not a good investment for my business. I'm going to centralize some of this capability so that we can all benefit together. And that team is essentially a platform organization. And they're making Kubernetes a shared services platform so that everybody else can come and sort of consume it. So, what that means to us, is our customer is a platform organization, and their customer is a developer. So we have to make two constituencies successful. Our customer who's providing a multi-tenant platform, and then their customer, who's your developer, both have to be happy. If you don't solve for both, you know, constituencies, you're not going to be successful. >> So, you're targeting the builder of the infrastructure and the consumer of that infrastructure? >> Yes, sir. It has to be both. >> On the other side? >> Exactly, right. So that look, honestly, it takes iteration to figure these things out. But this is a consistent theme that I am seeing. In fact, what I would argue now, is that every enterprise should be really stepping back and thinking about what is my platform strategy? Because if you don't have a platform strategy, you're going to have a bunch of different teams who are doing different things, and some will be successful, and look, some will not be. And that is not good for business. >> Yeah, and Santhosh, I want to get to you. You mentioned your transformations, what you look forward, and your title, Global Head of Cloud, SRE. Okay, so SRE, we all know came from Google, right? Everyone wants to be like Google, but no one wants to be like Google, right? And no one is Google. Google's a unique thing. >> Haseeb: Only one Google. >> But they had the dynamic and the power dynamic of one person to large scale set of servers or infrastructure. But concept can be portable, but the situation isn't. So, Borg became Kubernetes, that's inside baseball. So, you're doing essentially what Google did at their scale, you're doing for Mass Mutual. That's kind of what's happening, is that kind of how I see it? And you guys are playing in there partnering? >> So, I totally agree. Google introduce SRE, Site Reliability Engineering. And if you take the traditional transformation of the roles, in the past, it was called operations, and then DevOps ops came in, and then SRE is the new buzzword. And the future could be something like Product Engineering. And in this journey, here is what I tell folks on my side, like what worked for Google might not work for a financial company. It might not work for an insurance company. It's okay to use the word, SRE, but end of the day, that SRE has to be tailored down to your requirements. And the customers that you serve, and the technology that you serve. >> This is why I'm coming back, this platform engineering. At the end of the day, I think SRE just translates to, you're going to have a platform engineering team? 'Cause you got to enable developers to be producing more code faster, better, cheaper, guardrails, policies. It's kind of becoming the, these serve the business, which is now the developers. IT used to serve the business back in the old days, "Hey, the IT serves the business." >> Yup. >> Which is a term now. >> Which is actually true now. >> The new IT serves the developers, which is the business. >> Which is the business. >> Because if digital transformation goes to completion, the company is the app. >> The hard line between development and operations, so that's thinning down. Over the time, that line might disappear. And that's where SRE is fitting in. >> Yeah, and then building platform to scale the enablement up. So, what is the key challenges? You guys are both building out together this new transformational direction. What's new and what's the same? The same is probably the business results, but what's the new dynamic involved in rolling it out and making people successful? You got the two constituents, the builders of the infrastructures and the consumers of the services on the other side. What's the new thing? >> So, the new thing, if I may go first. The faster market to value that we are bringing to the table, that's very important. Business has an idea. How do you get that idea implemented in terms of technology and take it into real time? So, that journey we have cut down. Technology is like Kubernetes. It makes an IT person's life so easy that they can speed up the process. In a traditional way, what used to take like an year, or six months, can be done in a month today, or less than that. So, there's definitely speed velocity, agility in general, and then flexibility. And then the automation that we put in, especially if you have to maintain like thousands of clusters. These are today, it is possible to make that happen with a click off a button. In the past, it used to take, probably, 100-person team, and operational team to do it, and a lot of time. But that automation is happening. And we can get into the technology as much as possible, but blueprinting and all that stuff made it possible. >> We'll save that for another interview. We'll do it deep time. (panel laughing) >> But the end user on the other end, the consumer doesn't have the patience that they once had, right? It's, "I want this in my lab now." How does the culture of Mass Mutual? How is it evolve to be able to deliver the velocity that your customers are demanding? >> Once in a while, it's important to step yourself into the customer's shoes and think it from their perspective. Business does not care how you're running your IT shop. What they care about is your stability of the product and the efficiencies of the product, and how easy it is to reach out to the customers. And how well we are serving the customers, right? So, whether I'm implementing Docker in the background, Docker Swam or Kubernetes, business doesn't even care about it. What they really care about, it is, if your environment goes down, it's a problem. And if your environment or if your solution is not as efficient as the business needs, that's the problem, right? So, at that point, the business will step in. So, our job is to make sure, from a technology perspective, how fast you can make implement it? And how efficiently you can implement it? And at the same time, how do you play within the guardrails of security and compliance? >> So, I was going to ask you, if you have VMware in your environment? 'Cause a lot of clients compare what vCenter does for Kubernetes is really needed. And I think that's what you guys got going on. I can say that, you're the vCenter of Kubernetes. I mean, as as metaphor, a place to manage it all, is all one paint of glass, so to speak. Is that how you see success in your environment? >> So, virtualization has gone a long way. Where we started, what we call bare metal servers, and then we virtualized operating systems. Now, we are virtualizing applications, and we are virtualizing platforms as well, right? So that's where Kubernetes plays a role. >> So, you see the need for a vCenter like thing for Kubernetes? >> There's definitely a need in the market. The way you need to think is like, let's say there is an insurance company who actually implement it today, and they gain the market advantage. Now, the the competition wants to do it as well, right? So, there's definitely a virtualization of application layer that's very critical, and it's a critical component of cloud strategy as a whole. >> See, you're too humble to say it. I'll say, you're like the vCenter of Kubernetes. Explain what that means in your term? If I said that to you, what would you react? How would you react to that? Would you say, BS, or would you say on point? >> Maybe we should think about what does vCenter do today? So, in my opinion, by the way, vCenter in my opinion, is one of the best platforms ever built. Like it's the best platform in my opinion ever built. VMware did an amazing job, because they took an IT engineer, and they made him now be able to do storage management, networking management, VM's multitenancy, access management, audit. Everything that you need to run a data center, you can do from essentially single platform. >> John: From a utility standpoint, home-run? >> It's amazing. >> Yeah. >> Because you are now able to empower people to do way more. Well, why are we not doing that for Kubernetes? So, the premise man Rafay was, well, I should have IT engineers, same engineers. Now, they should be able to run fleets of clusters. That's what people that Mass Mutual are able to do now. So, to that end, now you need cluster management, you need access management, you need blueprinting, you need policy management. All of these things that have happened before, chargebacks, they used to have it in vCenter, now they need to happen in other platforms but for Kubernetes. So, should we do many of the things that vCenter does? Yes. >> John: Kind of, yeah. >> Are we a vCenter for Kubernetes? >> No. >> That is a John Furrier question. >> All right, well, the speculation really goes back down to the earlier speed question. If you can take away the complexity and not make it more steps, or change a tool chain, or do something, then the Devs move faster. And the service layer that serves the business, the new organization, has to enable speed. This is becoming a real discussion point in the industry, is that, "Yeah, we got new tool. Look at the shiny new toy." But if it move the needle, does it help productivity for developers? And does it actually scale up the enablement? That's the question. So, I'm sure you guys are thinking about this a lot. What's your reaction? >> Yeah, absolutely. And one thing that just hit my mind, is think about the hoteling industry before Airbnb and after Airbnb. Or the taxi industry before Uber and after Uber. So, if I'm providing a platform, a Kubernetes platform for my application folks, or for my application partners, they have everything ready. All they need to do is build their application and deploy it, and run it. They don't have to worry about provisioning of the servers, and then building the Middleware on top of it, and then, do a bunch of testing to make sure they iron out all the compatible issues and whatnot. Now, today, all I say is like, "Hey, we have a platform built for you. You just build your application, and then deploy it in a development environment, that's where you put all the pieces of puzzle together. Make sure you see your application working, and then the next thing that you do is like, do the correction. >> John: Shipping. >> Shipping. You build the production. >> John: Press. Go. Release it. (laughs) That when you move on, but they were there. I mean, we're there now. We're there. So, we need to see the future, because that's the case, then the developers are the business. They have to be coding more features, they have to react to customers. They might see new business opportunities from a revenue standpoint that could be creatively built, got low code, no code, headless systems. These things are happening where there's, I call the Architectural List Environment where it's like, you don't need architecture, it's already happening. >> Yeah, and on top of it, if someone has an idea, they want to implement an idea real quick. So, how do you do it? And you don't have to struggle building an environment to implement your idea and test it in real time. So, from an innovation perspective, agility plays a key role. And that's where the Kubernetes platforms, or platforms like Kubernetes plays. >> You know, Lisa, when we talked to Andy Jassy, when he was the CEO of AWS, either one-on-one or on "theCUBE," he always said, and this is kind of happening, "Companies are going to be builders, where it's not just utility, you need that table stakes to enable that new business idea." And so, in this last keynote, he did this big thing like, "Think like your developers are the next entrepreneurial revenue generators." I think I'm starting to see that. What do you think about that? You see that coming sooner than later? Or is that an insight, or is that still ways away? >> I think it's already happening at a level, at a certain level. Now ,the question comes back to, you know, taking it to the reality. I mean, you can do your proof of concept, proof of technologies, and then prove it out like, "Hey, I got a new idea. This idea is great." And it's to the business advantage. But we really want to see it in production live where your customers are actually using it. >> In the board meetings, "Hey, we got a new idea that came in, generating more revenue, where'd that come from?" Agile Developer. Again, this is real. >> Yeah. >> Yeah. Absolutely agree. Yeah, I think both of you gentlemen said a word as you were talking, you used the word, Guardrails. We're talking about agility, but the really important thing is, look, these are enterprises, right? They have certain expectations. Guardrails is key, right? So, it's automation with the guardrails. Guardrails are like children, you know, shouldn't be heard. They're seen but not heard. Developers don't care about guardrails, they just want to go fast. >> They also bounce around a little bit, (laughs) off the guardrails. >> Haseeb: Yeah. >> One thing we know that's not going to slow down, is the expectations, right? Of all the consumers of this, the Devs, the business, the business top line, and, of course, the customers. So, the ability to really, as your website says, let's say, "Make Life Easy for Platform Teams" is not trivial. And clearly what you guys are talking about here, is you're really an enabler of those platform teams, it sounds like to me. >> Yup. >> So, great work, guys. Thank you so much for both coming on the program, talking about what you're doing together, how you're seeing the evolution of Kubernetes, why? And really, what the focus should be on those platform teams. We appreciate all your time and your insights. >> Thank you so much for having us. >> Thanks for having us. >> Our pleasure. For our guests and for John Furrier, I'm Lisa Martin. You're watching "theCUBE" Live, KubeCon CloudNativeCon from Detroit. We'll be back with our next guest in just a minute, so stick around. (bright upbeat music)

>>Okay, welcome back to everyone. Cube's live coverage of VMware Explorer, 2022. I'm John fur, host of the cube. Two sets three days of live coverage. Dave Ante's here. Lisa Martin, Dave Nicholson, all host of the cube 12 interviews today, just we're with Rocklin and rolling, getting down to the end of the show. As we wind down and look back and look at the future. We've got Steven Jones. Here's the general manager of the VMware cloud on AWS. He's with Amazon web service. Steven Jones. Welcome to the cube. >>Thanks John. >>Welcome back cube alumni. I've been on many times going back to 2015. Yeah. >>Pleasure to be here. Great >>To see you again. Thanks for coming on. Obviously 10 years at AWS, what a ride is that's been, come on. That's fantastic. Tell me it's been crazy. >>Wow. Learned a lot of stuff along the way, right? I mean, we, we, we knew that there was a lot of opportunity, right? Customers wanting the agility and flexibility of, of the cloud and, and we, we still think it's early days, right? I mean, you'll hear Andy say that animals say that, but it really is. Right. If you look at even just the amount of spend that's being spent on, on clouds, it's in the billions, right. And the amount of, of spend in it is still in the trillion. So there's, there's a long way to go and customers are pushing us hard. Obviously >>It's been interesting a lot going on with VM. We're obviously around with them, obviously changing the strategy with their, their third generation and their narrative. Obviously the Broadcom thing is going on around them. And 10 years at abs, we've been, we've been, this'll be our ninth year, no 10th year at reinvent coming up for us. So, but it's 10 years of everything at Amazon, 10 years of S three, 10 years of C two. So if you look at the, the marks of time, now, the history books are starting to be written about Amazon web services. You know, it's about 10 years of full throttle cube hyperscaler in action. I mean, I'm talking about real growth, like >>Hardcore, for sure. I'll give you just one anecdote. So when I first joined, I think we had maybe two EC two instances back in the day and the maximum amount of memory you could conversion into one of these machines was I think 128 gig of Ram fast forward to today. You literally can get a machine with 24 terabytes of Ram just in insane amounts. Right? My, my son who's a gamer tells me he's got 16 gig in his, in his PC. You need to, he thinks that's a lot. >>Yeah. >>That's >>Excited about that. That's not even on his graphics card. I mean, he's, I know it's coming next. The GPU, I mean, just all >>The it's like, right? >>I mean, all the hardware innovation that you guys have done, I mean, look at every it's changed. Everyone's changed their strategy to copy AWS nitro, Dave ante. And I talk about this all the time, especially with James Hamilton and the team over there, Peter DeSantos, these guys have, are constantly going at the atoms and innovating at the, at the level. I mean that, that's how hardcore it is over there right now. I mean, and the advances on the Silicon graviton performance wise is crazy. I mean, so what does that enabling? So given that's continuing, you guys are continuing to do great work there on the CapEx side, we think that's enabling another set of new net new applications because we're starting to see new things emerge. We saw snowflake come on, customer of AWS refactor, the data warehouse, they call it a data cloud. You're starting to see Goldman Sachs. You see capital one, you see enterprise customers building on top of AWS and building a cloud business without spending the CapEx >>Is exactly right. And Ziggy mentioned graviton. So graviton is one of our fastest growing compute families now. And you know, you mentioned a couple of ISVs and partners of ours who are leaning in heavily on porting their own software. Every event Adam announced that we're working with SAP to, to help them port their HANA cloud, which is a, a database of service offering HANA flagship to graviton as well. So it's, it's definitely changing. >>And I think, you know, one of the, and we're gonna circle back to VMware is kind of a point to this. This conversation is that, is that if you look at the trends, right, okay. VMware really tried hard to do cloud and they had a good shot at it V cloud air, but it just, they didn't have the momentum that you guys had at AWS. We saw a lot, lot of other stragglers try to do cloud. They fell off the road, OpenStack, HP, and the list goes on and on. I don't wanna get into that, but the point is, as you guys become more powerful and you're open, right? So you have open ecosystem, you have people now coming back, taking advantage and refactoring and picking up where they left off. VMware was the one of the first companies that actually said, you know what pat Gelsinger said? And I was there, let's clear up the positioning. Let's go all in with AWS. That's >>Right >>At that time, 2016. >>Yeah. This was new for us, for >>Sure. And then now that's set the standard. Now everybody else is kind of doing it. Where is the VMware cloud relationship right now? How is that going out? State's worked. >>It's working well very well. It's I mean, we're celebrating, I think we made the announcement what, five years ago at this conference. Yeah. 2016. So, I mean, it's, it's been a tremendous ride. The best part are the customers who were coming and adopting and proving to us that our vision back then was the right vision. And, and, and what's been different. I think about this relationship. And it was new for us was that we, we purposely went after a jointly engineered solution. This wasn't a, we've got a, a customer or a partner that's just going to run and build something on us. This is something where we both bring muscle and we actually build a, a joint offering together. Talk about, about the main difference. >>Yeah. And that, and that's been working, but now here at this show, if you look at, if you squint through the multi-cloud thing, which is like just, I think positioning for, you know, what could happen in, in a post broad Broadcom world, the cloud native has traction they're Tansu where, where customers were leaning in. So their enterprise customer is what I call the classic. It, you know, mainstream enterprise, which you guys have been doing a lot of business with. They're now thinking, okay, I'm gonna go on continu, accelerate on, in the public cloud, but I'm gonna have hybrid on premise as well. You guys have that solution. Now they're gonna need cloud native. And we were speculating that VMware is probably not gonna be able to get 'em all of it. And, and that there's a lot more cloud native options as customers want more cloud native. How do you see that piece on Amazon side? Because there's a lot of benefits between the VMware cloud on AWS and the services that you guys have natively in your cloud. So we see customers really taking advantage of the AWS goodness, as well as expanding the cloud side at VMware cloud on AWS. >>Yeah. There's probably two ways I would look at this. Right? So, so one is the combination of VMware cloud on AWS. And then both native services just generally brings more options to customers. And so typically what we're seeing now is customers are just able to move much faster, especially as it comes to data center, evacuations, migrating all their assets, right? So it used to be that, and still some customers they're like, I I've gotta think through my entire portfolio of applications and decide what to refactor. And the only way I can move it to cloud is to actually refactor it into some net new application, more and more. We're actually seeing customers. They've got their assets. A lot of them are still on premises in a VMware state, right. They can move those super quick and then modernize those. And so I think where you'll see VMware and AWS very aligned is on this, this idea of migrate. Now you need to get the benefits of TCO and, and the agility that comes with being in the cloud and then modernize. We took a step further, which is, and I think VMware would agree here too, but all of the, the myriad of services, I think it's 200 plus now AWS native services are for use right alongside any that a customer wants to run in VMware. And so we have examples of customers that are doing just, >>And that's, that's how you guys see the native and, and VMware cloud integrating in. Yeah, that's, that's important because this, I mean, if I always joke about, you know, we've been here 12 years listening in the hallways and stuff, you know, on the bus to the event last night, walking the parties and whatnot, listening in the streets, there's kind of two conversations that rise right to the top. And I wanna get your reaction to this Steven, because this seems to be representative of this demographic here at VMware conference, there's conversations around ransomware and storage and D dub and recovery. It's all, a lot of those happen. Yeah. Clearly a big crowd here that care about, you know, Veeam and NetApp and storage and like making sure stuff's secure and air gapped. And a lot of that kind of, I call nerdy conversations and then the other one is, okay, I gotta get the cloud story. >>Right. So there's kind of the operational security. And then there's like, okay, what's my path to true cloud. I need to get this moving. I need to have better applications. My company is the application now not it serves some sort of back office function. Yeah. It's like, my company is completely using technology as its business. So the app is the business. So that means everything's technology driven, not departmental siloed. So there's a, that's what I call the true cloud conversation. How do you, how do you see that evolving because VMware customers are now going there. And I won't say, I won't say they're behind, but they're certainly going there faster than ever before. >>I think, I think, I mean, it's an interesting con it's an interesting way to put it and I, I would completely agree. I think it's, it's very clear that I think a lot of customer companies are actually being disrupted. Right. And they have to move fast and reinvent themselves. You said the app is now becoming the company. Right. I mean, if, if you look at where not too many years back, there were, you know, big companies like Netflix that were born in the cloud. Right. Airbnb they're disruptors. >>There's, that's the >>App, right? That's the app. Yeah. So I, I would exactly agree. And, and that's who other companies are competing with. And so they have to move quickly. You talked about some, some technology that allows them to do that, right? So this week we announced the general availability of a NetApp on tap solution. It's been available on AWS for some time as a fully managed FSX storage solution. But now customers can actually leverage it with, with VMC. Now, why is that important? Well, there's tens of thousands of customers running VMware. On-premises still, there's thousands of them that are actually using NetApp filers, right? NetApp, NetApp filers, and the same enterprise features like replication. D do you were talking about and Snapp and clone. Those types of things can be done. Now within the V VMware state on AWS, what's even better is they can actually move faster. So consider replicating all this, you know, petabytes and petabytes of data that are in these S from on-premises into AWS, this, this NetApp service, and then connected connecting that up to the BMC option. So it just allows customers much, much. >>You guys, you guys have always been customer focus. Every time I sat down with the Andy jazzy and then last year with Adam, same thing we worked back from, I know it's kind of a canned answer on some of the questions from media, but, but they do really care. I've had those conversations. You guys do work backwards from the customer, actually have documents called working backwards. But one of the things that I observed, we talked about here yesterday on the cube was the observations of reinvent versus say, VM world. Now explore is VM world's ecosystem was very partner-centric in the sense of the partners needed to rely on VMware. And the customers came here for both more of the partners, not so much VMware in the sense there wasn't as much, many, many announcements can compare that to the past, say eight years of reinvent, where there's so much Amazon action going on the partners, I won't say take as a second, has a backseat to Amazon, but the, the attendees go there generally for what's going on with AWS, because there's always new stuff coming out. >>And it's, it's amazing. But this year it starts to see that there's an overlap or, or change between like the VMware ecosystem. And now Amazon there's, a lot of our interviews are like, they're on both ecosystems. They're at Amazon's show they're here. So you start to see what I call the naturalization of partners. You guys are continuing to grow, and you'll probably still have thousands of announcements at the event this year, as you always do, but the partners are much more part of the AWS equation, not just we're leasing all these new services and, and oh, for sure. Look at us, look at Amazon. We're growing. Cause you guys were building out and look, the growth has been great. But now as you guys get to this next level, the partners are integral to the ecosystem. How do you look at that? How has Amazon thinking about that? I know there's been some, some, a lot of active reorgs around AWS around solving this problem or no solve the problem, addressing the need and this next level of growth. What's your reaction to >>That? Well, I mean, it's, it's a, it's a good point. So I have to be honest with you, John. I, I, I spent eight of my 10 years so far at AWS within the partner organization. So partners are very near and dear to my heart. We've got tens of thousands of partners and you are you're right. You're starting to see some overlap now between the VMware partner ecosystem and what we've built now in AWS and partners are big >>By the way, you sell out every reinvent. So it's, you have a lot of partners. I'm not suggesting that you, that there's no partner network there, but >>Partners are critical. I mean, absolutely naturally we want a relationship with a customer, but in order to scale the way we need to do to meet the, the needs of customers, we need partners. Right. We, we can't, we can't interact with every single customer as much as we would like to. Right. And so partners have long built teams and expertise that, that caters to even niche workloads or opportunity areas. And, and we love partners >>For that. Yeah. I know you guys do. And also we'll point out just to kind of give props to you guys on the partner side, you don't, you keep that top of the stack open on Amazon. You've done some stuff for end to end where customers want all Amazon, but for the most part, you let competition come in, even on, so you guys are definitely partner friendly. I'm just observing more the maturization of partners within the reinvent ecosystem, cuz we're there every year. I mean, it's, I mean, first of all, they're all buzzing. I mean, it's not like there's no action. There's a lot of customers there it's sold out as big numbers, but it just seems that the partners are much more integrated into the value proposition of at a AWS because of the, the rising tide and, and now their enablement, cuz now they're part of the, of the value proposition. Even more than ever before >>They, they really are. And they, and they're building a lot of capabilities and services on us. And so their customers are our customers. And like you say, it's rising tide, right. We, we all do better together. >>Okay. So let's talk about the VMware cloud here. What's the update here in terms of the show, what's your, what's your main focus cuz a lot of people here are doing, doing sessions. What's been some of the con content that you guys are producing here. >>Yeah. So the best part obviously is a always the customer conversations to partner conversations. So a, a lot of, a lot of sessions there, we did keynote yesterday in Ryan and I, where we talked about a number of announcements that are, I think pretty material now to the offering a joint announcement with NetApp yesterday as well around the storage solution I was talking about. And then some, some really good technical deep dives on how the offering works. Customers are still interested in like how, how do I take what I've got on premises and easily move into AWS and technology like HSX H CX solution with VMware makes it really easy without having to re IP applications. I mean, you know, it is super difficult sometimes to, to move an application. If you've got figure out where all the firewall rules are and re iPing those, those things source. But yeah, it's, it's been fantastic. >>A lot of migrations to the cloud too. A lot of cloud action, new cloud action. You guys have probably seen an uptake on services right on the native side. >>Yes. Yes. For sure. So maybe I just outlined some of the, some of the assets we made this week. So absolutely >>Go ahead. >>We, we announced a new instance family as a, a major workhorse underneath the VMware cloud offering called I, I, you mentioned nitro earlier, this is on, based on our latest generation of nitro, which allows us to offer as you know, bare metal instances, which is, which is what VMware actually VMware was our first partnership and customer that I would say actually drove us to really get Nira done and out the door. And we've continued to iterate on that. And so this I four, I instance, it's based on the, the latest Intel isolate processor with more than double the Ram double the compute, a whopping 75 gigabytes per second network. So it's a real powerhouse. The cool thing is that with the, with the NetApp storage solution that we, we discussed, we're now disaggregating the need to provision, compute and storage at the same time. It used to be, if you wanted to add more storage to your VSAN array, that was on a V VMware cloud. Yeah. You'd add another note. You might not need more compute for memory. You'd have to add another note. And so now customers can simply start adding chunks of storage. And so this opens up customers. I had a customer come to me yesterday and said, there's no reason for us not to move. Now. We were waiting for something that like this, that allowed us to move our data heavy workloads yeah. Into VMware cloud. It's >>Like, it's like the, the alignment. You mentioned alignment earlier. You know, I would say that VMware customers are lined up now almost perfectly with the hybrid story that's that's seamless or somewhat seems it's never truly seamless. But if you look at like what Deepak's doing with Kubernetes and open source, you, you guys have that there talking that big here, you got vs a eight vSphere, eight out it's all cloud native. So that's lined up with what you guys are doing on your services and the horsepower. They have their stuff, you have yours that works better together. So it seems like it's more lined up than ever before. What's your take on that? Do you agree? And, and if so, what folks watching here that are VMware customers, what's, what's the motivation now to go faster? >>Look, it is, it is absolutely lined up. We are, as, as I mentioned earlier, we are jointly engineering and developing this thing together. And so that includes not just the nuts and bolts underneath, but kind of the vision of where it's going. And so we're, we're collectively bringing in customer feedback. >>What is that vision real quick? >>So that vision has to actually help an under help meet even the most demanding customer workloads. Okay. So you've got customer workloads that are still locked in on premises. And why is that? Well, it used to be, there was big for data and migration, right? And the speed. And so we continue to iterate this and that again is a joint thing. Instead of say, VMware, just building on AWS, it really is a, a tight partnership. >>Yeah. The lift and shift is a, an easy thing to do. And, and, and by the way, that could be a hassle too. But I hear most people say the reason holding us back on the workloads is it's just a lot of work, a hassle making it easier is what they want. And you guys are doing that. >>We are doing that. Absolutely. And by the way, we've got not just engineering teams, but we've got customer support teams on both sides working together. We also have flexible commercial options, right? If a customer wants to buy from AWS because they've negotiated some kind of deal with us, they can do that. They wanna buy from VMware for a similar reason. They could buy from VMware. So are >>They in the marketplace? >>They are in the market. There, there are some things in the marketplace. So you talked about Tansu, there's a Tansu offering in the marketplace. So yes. Customers can >>Contract. Yeah. Marketplaces. I'm telling you that's very disruptive. I'm Billy bullish on the market AIOS marketplace. I think that's gonna be a transformative way. People have what they procure and fully agree, deploy and how, and channel relationships are gonna shift. I think that's gonna be a disruptive enabler to the partner equation and, and we haven't even seen it yet. We're gonna be up there in September for their inaugural event. I think it's a small group, but we're gonna be documenting that. So even final question for you, what's next for you? What's on the agenda. You got reinvent right around the corner. Your P ones are done. Right? I know. Assuming all that, I turn that general joke. That's an internal Amazon joke. FYI. You've got your plan. What's next for the world. Obviously they're gonna go this, take this, explore global. No matter what happens with Broadcom, this is gonna be a growth wave with hybrid. What's next for you and your team with AWS and VMware's relationship? >>Yeah. So both of us are hyper focused on adding additional options, both from a, an instance compute perspective. You know, VMware announced some, some, some additional offerings that we've got. We've got a fully complete, like, so they're, they announce things like VMware flex compute V VMware flex storage. You mentioned earlier, there was a conversation around ransomware. There's a new ransomware based offering. So we're hyper focused on rounding out, continuing to round out the offering and giving customers even more choice >>Real quick. Jonathan made me think about the ransomware we were at reinforce Steven Schmidtz now the CSO. Now you got a CSO. AJ's the CSO. You got a whole focus, huge emphasis on security right now. I know you always have, but now it's much more public. It's PO more positive, I think, than some of the other events I've been to. It's been more Lum and doom. What's the security tie in here with VMware. Can you share a little bit real quick on the security piece update around this relationship? >>Yeah, you bet. So as you know, security for us is job zero. Like you don't have anything of security. And so what are the things that, that we're excited about specifically with VMware is, is the latest offering that, that we put together and it's called this, this ransomware offering. And it's, it's a little bit different than other ransomware. I mean, a lot of people have ransomware offerings today, just >>Air gap. >>Right, right, right. Exactly. No, that's easy. No, this one is different. So on the back end, so within VMC, there's this, this option where CU we can be to be taking iterative snapshots of a customer environment. Now, if an event were to occur, right. And a customer is like, I have to know if I'm compromised, we can actually spin up super easy. This is cloud. Remember? Yeah. We can spin up a, a copy of this environment, throw a switch, pick a snapshot with NSX. So VMware NSX firewall it off and then use some custom tooling from VMware to actually see if it's been compromised or not. And then iterate through that until you actually know you're clean. And that's different than just tools that do maybe a >>Little bit of scam. We had Tom gills on yesterday and, and one of the things Dave ante had to leave is taking the sun to college is last one in the house and B nester now, but Tom Gill was on. We were talking about how good their security story is ware. And they really weren't showboating it as much as they could have here. I thought they could have done a better job, but this is an example of kind of them really leaning in with you guys. That's the key part of the relationship. >>Yeah, it really is. And I think this is something is materially different than what you can get elsewhere. And it's exciting for, >>Okay. Now the, the real question I want to know is what's your plans for AWS reinvent the blockbuster end of the year, Amazon surf show that gets bigger and bigger. I know it's still hybrid now, but it's looking be hybrid, but people are back in person last year. You guys were the first event really come back and still had massive numbers. AWS summit, New York at 19,000. I heard last week in Chicago, big numbers. So we're expecting reinvent to be pretty large this year. What are you, what are you gonna do there? What's your role there? >>We are expecting, well, I'll be there. I cover multiple businesses. Obviously. We're, we're planning on some additional announcements, obviously in the VMware space as well. And one of the other businesses I run is around SAP. And you should look for some things there as well. Yeah. Really looking forward to reinvent, except for the fact that it's right after Thanksgiving. But I think it >>Always ruins my, I always get an article out. I like, why are you we're having, we're having Thanksgiving dinner. I gotta write this article. It's gotta get Adam, Adam. Leski exclusive. We, every year we do a, a CEO sit down with Andy was the CEO and then now Adam. But yeah, it's a great event to me. I think it sets the tone. And it's gonna be very interesting to see the big clouds are coming to the big cloud. You guys, and you guys are now called hyperscalers. Now, multiple words. It's interesting. You guys are providing the CapEx goodness for everybody else now. And that relationship seems to be the new, the new industry standard of you guys provide the enablement and then everyone you get paid, cuz it's a service. A whole nother level of cloud is emerging in the partner network, GSI other companies. Yeah. >>Yeah. I mean we're really scaling. I mean we continue to iterate and release regions at a fast clip. We just announced support for VMware in Hong Kong. Yeah. So now we're up to 21 regions for this service, >>The sovereign clouds right around the corner. Let's we'll talk about that soon. Steven. Thanks for coming. I know you gotta go. Thank you for your valuable time. Coming in. Put Steven Jones. Who's the general manager of the VMware cloud on AWS business. Four AWS here inside the cube day. Three of cube coverage. I'm John furrier. Thanks for watching. We'll be right back.

>>Good morning from New York city, Lisa Martin and John furrier with the cube. We are at AWS summit NYC. This is a series of summits this year, about 15 summit globally. And we're excited to be here, John, with about 10,000 folks. >>It's crowded. New York is packed big showing here at 80 of us summit. So it's super exciting, >>Super exciting. Just a little bit before the keynote. And we have our first guest, Kevin Farley joins us the director of strategic alliances at Maria DB. Kevin, welcome to >>The program. Thank you very much. Appreciate you guys having us. >>So all of us out from California to NYC. Yeah, lots of eyes. We got keynote with Warner Vogels coming up. We should be some good news, hopefully. Yep. But talk to us about Maria DB Skys cloud native version released a couple years ago. What's going on? >>Yeah, well, it's, you know, Skys SQL for us is really a be on the future. I think when we think about like the company's real mission is it's just creating a database for everyone. It's it's any cloud, any scale, um, any size of performance and really making sure that we're able to deliver on something that really kind of takes advantage of everything we've done in the market to date. If you think about it, there's not very many startups that have a billion downloads and 75% of the fortune 500 already using our service. So what we're really thinking about is how do we bridge that gap? How do we create a natural path for all of these customers? And if you think about not just Maria DB, but anyone else using the sequel query language, all the, my people, what I think most Andy jazzy TK, anyone says, you know, it's about 10% of the market currently is in the clouds. That's 90% of a total addressable market that hasn't done it yet. So creating cloud modernization for us, I think is just a huge opportunity. Do >>You guys have a great history with AWS? I want to just step back, you mentioned some stats on, on success. Can you scope the size and track record of Maria DB for us real quick and set the table? Because I think there's a bigger picture going on that we've been tracking for the past 13 years we address is the role of the database has always been one of those things where they didn't believe a one database fits all things, right. You guys have been part of that track record scope, the size and scale of Maria DB, the usage, the use cases and some of the successes. >>Yeah. I mean, like I said, some of the stats are already threw out there. So, you know, it is pervasive, I think is the best way to put it. I think what you look at what the database market really became is very siloed. Right? I think there was a lot of unique solutions that were built and delivered that had promise, but they also had compromise. And I think once you look at the landscape of a lot of fortune 500 companies, they have probably 10 to 15 different database solutions, right? And they're all doing unique things. They're difficult to manage. They're very costly. So what Marie DB is always kind of focused on is how do we continue to build more and more functionality into the database itself and allow that to be a single source of truth where application developers can seamlessly integrate applications. >>So then the theme of this event in New York city, which is scale dot, dot, dot, anything must align quite well with Maria and your >>Objectives. I mean, honestly, I think when I think of the problems that most database, um, companies, um, face customers, I should say it, it really comes down to performance and scale. Most of them like Maria DB, like you said, they it's like the car, you know, and love you've been driving it for years. You're an expert at it. It works great, but it doesn't have enough range. It doesn't go fast enough. It's hitting walls. That modern data requirements are just breaking. So scale for me is the favorite thing to talk about because what we launched as MariaDB expand, which is a plugable storage engine that is integrated into Skye, and it really gives you dynamic scale. So you can scale in, you can scale out, it's not costly compute to try to get for seasonality. So you can make your black Friday numbers. It's really about the dexterity to be able to come in and out as you need in a share, nothing architecture with full failover sale healing, high availability, married to the cloud for full cloud scale. And that's really the beauty of the AWS partnership. >>Can you elaborate a bit more on the partnership? How long have you guys been partners? Where is it now anything exciting coming out? >>Yeah, it it's, it's actually been a wonderful ride. They've really invested from the very beginning we went for the satisfactory. So they really brought a lot of resources to bear. And I think if you're looking at why it works, um, it's probably two things. I think the number one thing is that we share one of the core tenants and it's customer obsession in a, in a, in an environment where there is co-opetition right. You have to find paths for how do you get the best thing for the customer? And the second is pretty obvious, but if you look at any major cloud, their number one priority is getting large mission critical workloads into their cloud because the revenue is exponential on the backside. So what do we own? Large mission critical workloads. So if you marry that objective with AWS, the partnership is absolutely perfect for driving true revenue, growth scale, and, and revenue across, across both entities in the partner ecosystem. >>So Kevin talk about the, um, the hybrid strategy, cuz you're seeing cloud operations. Yep. Go hybrid. Amazon announced AWS announced outpost like four years ago. Right now edge is super hot. Yeah. So you're seeing like most of the enterprise is saying mm-hmm <affirmative> okay. Love cloud love the cloud database, but I got the on-prem hybrid cloud operations. Right. So it's not just proprietary operations. It's cloud ops. Yeah. How do you guys fit into that? What's the story. >>We, we actually it's. I mean, there's, there's all these new deliverables outposts, you know, come out with a promise. What we have is a reality right now, um, one of the largest, um, networking companies, which I can't mention yet publicly, um, we want a really big sky SQL deal, but what they had manufacturing plants, they needed to have on-prem deployments. So Maria DB naturally syncs with sky SQL. It's the same technology. It works in perfect harmony. So we really already deliver on the promise of hybrid, but of course there's a lot more we can grow in that area. And certainly thinking about app posts and other solutions, um, is definitely on the, the longer term roadmap of what could make sense for in our customer. What, >>What are some of the latest things that, that you guys are doing now that you weren't doing a few years ago that customers should know about the audience should know about? >>I mean, I think the game changer, we're always innovating. I mean, when you're the company that writes the code owns the code, you know, we can do hot fixes, we can do security patches, we can always do the things that give you real time access to what you need. But I think the game changer is what I mentioned a little bit earlier. And I think it's really the, the holy grail of the cloud. It's like, how can we take the, the SQL query language, which is well over 50% of the open source market. Right. And how do we convert that seamlessly into the cloud? How do we help you modernize on that journey? And expand gives you the ability to say, I can be the small, I can be a small startup. I got my C round. I don't wanna manage databases. I can use the exact same service as the largest fortune 100 company that has massive global scale and needs to be able to drive that across globe. Yeah. So I think that's the beauty is that it's really a democratization of the database, >>At least that, you know, we've been covering the big data space for 10 years. Remember all those different conversations had do those days and oh, they have big data and right. But then it's like too hard to set up. Then you had that kind of period where you saw a spark and data lakes emerge. Yeah. Then you, now it almost seems, seems like now more than ever, there's a data revolutions back. Right. It was almost like a lull in the, in, in the, in the market a little bit. Yeah. I'm gonna democratize data science right now. You got data. So now it just seems to be an explosion at that level. What's your analysis on that? Because you you've been in, in, in the weeds and in the, in the, in this market for 10 years. Yeah. And nothing really changed. It's just now it's more ready. Yeah. I think what's your observation. Why >>Is that? I think that's a really good question. And I love it cuz I mean, what the promise of things like could do and net new technologies sort of, it was always out there, but it required this whole net new lift and how do I do it? How do I manage it? How do I optimize it? The beauty of what we can do with Maria DB is that sky SQLs, which you already know and love. Right? And now we can Del you can deliver a data lake on S3, right? You can pull that data. And we also have the ability to do both analytical data and transactional data from the same database. So you can write applications that can pull column, store data up into, um, your application, but you can also have all of your asset transactions, which are absolutely required for all of your mission critical business. So I think that we're seeing more and more adoption. You've seen other companies start to talk about bringing the different elements in, but we're the only ones that really >>Do it and SQL standardizing that front end. Yeah. Even better than ever before. All the stuff under the covers is all being connected. >>That's the awesome part is right. Is you're literally doing what you already know how to do, but you blow it out on the back end, married to the cloud. And that I think is the real revolution of what makes usability real in the data space. And I think that's what was always the problem before >>When you're in partner conversations, you mentioned co-opetition. Yeah. <laugh> so I think when you're in partner conversations and customer conversations, there is a lot of the, the there's a lot of competition out there. Absolutely. Everyone's got their own key messages. What are the key differentiators that you're saying AWS Marie to be together better? And here's why, >>Yeah. I, I think that certainly you, you start with the global footprint of AWS, right? So what we rely on the most is having the ability to truly deal with global customers in availability zones, they're gonna optimize performance from them. But then when we look at what we do that really changes the game, it comes down to scale and performance. We actually just ran, um, a suspense test against cockroach that also does distributed sequel. Absolutely. You know, the results were off the chart. So we went public and said, we have an open challenge. Anyone that wants to try to beat, um, expand and Skye will we'll if you can, we'll put $25,000 towards charity. So we really are putting our money where our mouth is on that challenge. So we believe the performance cuz we've seen it and we know it's real, but then it's really always about data scale. Modern data requirements are breaking the mold of charting. They're breaking the mold of all these bandaids that people have put in these traditional services. And we give them future. We, we feature proof their investments, so they can say, Hey, I can start here. But if I end up being a startup that becomes Airbnb, I'm already built to blow it out on the back end. I can already use what I have. >>Speaking of startups, being the next Airbnb. If you look at behind us here, you can see, this is a really packed event in New York city events are back, but the ecosystem here is even flourishing. So Dave and I and Lisa were observing that we're still kind of in a growth mode, big time. So yeah, there's some market forces headwinds for the big unicorns, overfunded, you know, public companies, maybe the valuations are a little bit off, but there's still a surge of new innovations, new companies coming out of this. Um, and it's all around data and scale. It's all around new names. We've never heard of. Absolutely. What's your take on >>Reaction? Well, actually another awesome segues cuz in addition to the public clouds, I manage the ecosystem. And one of the things that we've really been focused on with Skys SQL is making it accessible API accessible. So if you're a company that has a huge Marine DB footprint change data capture might be the most important thing for you to say, we wanna do this, but we want you to stay in sync with our environments. Um, things like monitoring, things like BI, all of these are ecosystem plays and current partners that we have, um, that we really think about how do you holistically look at not only the database and what it can do, but how does it deliver value to different segments of your customer base or just your employee base that are using that stuff? So I think that's huge for us. >>Well, you know, one of the things that we talk often about is that every company, these days, regardless of industry, has to be a data company. Yep. You've gotta be able to access the data glean insights from an act on it quickly, whether it's manufacturing, retail, healthcare, are there any verticals in where Maria DB really excels? >>Um, so certainly we Excel in areas like financial services is huge DBS bank. Um, in APAC, one of our biggest customers, also one of the largest Oracle migrations, probably the, that we've ever done. A lot of people trying to get off Oracle, we make it seamless to get into Maria DB. Um, you can think about Samsung cloud and another, their entire consumer cloud is built on Maria DB, why it's integrated with expand right seasonality. So there's customers like that that really bring it home for us as far as ServiceNow tech sector. Right? So these are all different ones, but I think we're really strong in those >>Areas. So this brings up a good point. Dave and I a coined a term called super cloud at reinvent and Lisa and Dave were at multiple events we're together at events. And so a lot of people are getting behind this cuz it's multi-cloud sounds like something's broken. Yes. But so we call it super cloud because customers are building on top of ecosystems like Maria DB and others. Yeah. Not just AWS SOS does all the CapEx absolutely provide the value. So now people are having this new super cloud moment. We' saying we can get all the benefits of cloud scale mm-hmm <affirmative> without actually being a cloud. Right. So this is where the next gen layer comes. What's your reaction to, to super cloud. Do you think it's a thing? >>Well, I think it's a thing in the sense, from our perspective as an ISV, we're, we're laser focused on making sure that we support any cloud and we have a truly multicloud cloud platform. But the beauty of that as well is from a single UI, you're able to deploy databases in different clouds underneath that you're not looking at so you can have performance proximity, but you're still driving it through the same Skys UI. So for us it's, it's unequivocally true. Got it. And I think it's only ISVs like Maria DB that can deliver on that value because >>You're enabling, >>We're enabling it. Right. We partner, we build on top of everything. Right. So we can access everything underneath >>And they can then build on top of you. >>Sure, exactly. And that's exactly where it goes. Right? Yeah. So that, I think in that sense, the super cloud is actually already somewhat real. >>It's interesting. You look at the old, it spend, you take a big company. I won't say a name, but a leader in a, a vertical, they have such a big spend. Now they can leverage that spend in with the super cloud model. They then could become a service provider in the vertical. Absolutely capital one S doing it. Yeah. You're seeing, um, Goldman Sachs doing it. They have the power on the spend that they're leveraging in for their business and servicing their vertical and the smaller players. Do you see that trend? >>Well, I think that's the reality is that everyone is getting this place where if you're talking about sort of this broader super concept, you're talking about global scale, right? That's if in order to deliver a backbone that can service that model, you have to have the right data structure and the right database footprint to be able to scale. And I think that's what they all need to be able to do. And that's what we're really well positioned with Skys >>To enable companies, as we talked about a minute ago to truly become data companies. Yeah. And to be competitive and to scale on their own, where are your customer conversations? Are they at the C-suite level? Has that changed in the last couple of years? >>Uh, that's actually a really great way to state that question because I think you would've traditionally probably talked more to, um, the DBAs, right? They're the people that are having headaches. They're having problems. They're, they're trying to solve. We see a lot of developers now tons, right? They're thinking about, I have this, I have this new thing that I need to do to deliver this new application. And here's the requirements and the current model's broken. It doesn't optimize that it's a lot of work and it's hard to manage. So I think that we're in a great position to be able to take that to that next phase and deliver. And then of course, as you get deeper in with AWS, you're talking about, you know, CIO level, CISO level, they're they need to understand how do you fit into our larger paradigm. And many of these guys have, you know, hundreds of million dollar commits with AWS. So they think of their investment in the sense of the cloud stack. And we're part of that cloud stack, just like AWS services. So those conversations continue to happen certainly with our larger customers, cuz it truly is married. >>It is. And they continue to evolve. Kevin, thank you so much >>For joining. You're welcome. Great, >>John and me talking about what's going on with Maria >>D. Thank you, John. Thank you, Lisa. On behalf of Maria B, it was wonderful. Really >>Appreciate it. Fantastic as well for John furrier. I'm Lisa Martin. You're watching the cube live from New York city at AWS summit NYC, John and I we're back with our next guest in a minute.

(bright upbeat music) >> Hello, welcome to theCUBE, "Unstoppable Domains Showcase." I'm John Furrier, your host of theCUBE. We got a great discussion here called the influencers around what's going on Web 3.0. And also this new sea change, cultural change around this next generation, internet, web, cloud, all happening, Jeremiah Owyang, Industry Analyst and Founding Part of Kaleido Insights. Jeremiah, great to see you thanks for coming on I appreciate it. Ren Besnard, Vice President of Marketing and Unstoppable Domains in the middle of all the action. Gentlemen, thanks for coming on on theCUBE for this showcase. >> Wow, my pleasure. >> Thanks for having us, John. >> Jeremiah, I want to start with you. You've seen many ways refer in all of your work for over a decade now. You've seen the Web 2.0 wave now the Web 3.0 is here. And it's not, I wouldn't say hyped up it's really just ramping up. And you're seeing real practical examples. You're in the middle of all the action. What is this Web 3.0, can you frame for us? I mean, you've seen many webs. What is Web 3.0 mean, what is it all about? >> Well John, you and I worked in the Web 2.0 space and essentially that enabled peer-to-peer media where people could upload their thoughts and ideas and videos without having to rely on centralized media. Unfortunately, that distributed and decentralized movement actually became centralized on the platform which are the big social networks and big tech companies. And this has caused an uproar because the people who are creating the content did not have control, could not control their identities, and could not really monetize or make decisions. So Web 3.0 which is a moniker of a lot of different trends, including crypto, blockchain and sometimes the metaverse. Is to undo the controlling that has become centralized. And the power is now shifting back into the hands of the participants again. And in this movement, they want to have more control over their identities, their governance, the content that they're creating, how they're actually building it, and then how they're monetizing it. So in many ways it's changing the power and it's a new economic model. So that's Web 3.0. Without really even mentioning the technologies. Is that helpful? >> Yeah, it's great. And Ren, we're talking about on theCUBE many times and one notable stat I don't think it's been reported, but it's been more kind of a rumor. I hear that 30% of the Berkeley computer science students are dropping out and going into to crypto or blockchain or decentralized startups. Which means that there's a big wave coming in of talent. You're seeing startups, you're seeing a lot more formation, you're seeing a lot more, I would say it's kind of ramping up of real people, not just people with dream is actual builders out here doing stuff. What's your take on the Web 3.0 movement with all this kind of change happening from people and also the new ideas being refactored? >> I think that the competition for talent is extremely real. And we start looking at the stats, we see that there is an enormous draft of people that are moving into this space. People that are fascinated by technology and are embracing the ethos of Web 3.0. And at this stage I think it's not only engineers and developers, but we have moved into a second phase where we see that a lot of supporting functions, you know, marketing being one of them, sales, business development are being built up quite rapidly. It's not without actually reminding me of the mid 2000s, you know. When I started working with Google, at that point in time the walled gardens rightly absorbing vast, vast cohorts of young graduates and more experienced professionals that were passionate and moving into the web environment. And I think we are seeing a movement right now, which is not entirely similar except faster. >> Yeah, Jeremiah, you've seen the conversations of the cloud, I call the cloud kind of revolution. You had mobile in 2007. But you got Amazon Web Services changed the application space on how people developed in the cloud. And again, that created a lot of value. Now you're seeing the role of data as a huge part of how people are scaling and the decentralized movements. So you've got cloud which is kind of classic today, state of the art enterprise and or app developers. And you've got now decentralized wave coming, okay. You're seeing apps being developed on that architecture. Data is central in all this, right. So how, how do you view this as someone who's watching the landscape, you know, these walled gardens are hoarding all the data I mean, LinkedIn, Facebook. They're not sharing that data with anyone they're using it for themselves. So as- >> That's right. >> They can control back comes to the forefront. How do you see this market with the applications and what comes out of that? >> So the thing that we seen out of the five things that I had mentioned that are decentralizing. (Jeremiah coughing) Are the ones that have been easier to move across. Have been the ability to monetize and to build. But the data aspect has actually stayed pretty much central, frankly. What has decentralized is that the contracts, the blockchain ledgers, those have decentralized. But the funny thing is often a big portion of these blockchain networks are on Amazon 63 to 70%, same thing with (indistinct). So they're still using the Web 2.0 architectures. However, we're also seeing other forms like IPFS where the data could be spread across a wider range of folks. But right now we're still dependent on what Web 2.0. So the vision and the promise Web 3.0 when it to full decentralization is not here by any means. I'd say we're at a Web 2.25. >> Pre-Web 3.0 no, but actions there. How do you guys see the dangers, 'cause there's a lot of negative press but also there's a lot of positive press. You're seeing a lot of fraud, we've seen a lot of the crypto fraud over the past years. You've seen a lot of now positive. It's almost a self-governance thing and environment, the way the culture is. But what are the dangers, how do you guys educate people, what should people pay attention to, what should people look for to understand, you know, where to position themselves? >> Yes, so we've learned a lot from Web 1.0, Web 2.0, the sharing economy. And we are walking into Web 3.0 with eyes wide open. So people have rightfully put forth a number of challenges, the sustainability issues with excess using of computing and mining the excessive amount of scams that are happening in part due to unknown identities. Also the architecture breaks DAOn in some periods and there's a lack of regulation. This is something different though. In the last periods that we've gone through, we didn't really know what was going to happen. And we walked and think this is going to be great. The sharing economy, the gig economy, the social media's going to change the world around. It's very different now. People are a little bit jaded. So I think that's a change. And so I think we're going to see that sorted out in suss out just like we've seen with other trends. It's still very much in the early years. >> Ren, I got to get your take on this whole should influencers and should people be anonymous or should they be docs out there? You saw the board, eight guys that did that were kind of docs a little bit there. And that went viral. This is an issue, right? Because we just had a problem of fake news, fake people, fake information. And now you have a much more secure environment imutability is a wonderful thing. It's a feature, not a bug, right? So how is this all coming down? And I know you guys are in the middle of it with NFTs as authentication. Take us, what's your take on this because this is a big issue. >> Look, I think first I am extremely optimistic about technology in general. So I'm super, super bullish about this. And yet, you know, I think that while crypto has so many upsides, it's important to be super conscious and aware of the downsides that come with it to, you know. If you think about every Fortune 500 company there is always training required by all employees on internet safety, reporting of potential attacks and so on. In Web 3.0, we don't have that kind of standard reporting mechanisms yet for bad actors in that space. And so when you think about influencers in particular, they do have a responsibility to educate people about the potential, but also the dangers of the technology of Web 3.0 of crypto basically. Whether you're talking about hacks or online safety, the need for hardware, wallet, impersonators on discord, you know, security storing your seed phrase. So every actor influencer or else has got a role to play. I think that in that context to your point, it's very hard to tell whether influencers should be anonymous, oxydemous or fully docked. The decentralized nature of Web 3.0 will probably lead us to see a combination of those anonymity levels so to speak. And the movements that we've seen around some influencers identities become public are particularly interesting. I think there's probably a convergence of Web 2.O and Web 3.0 at play here, you know. Maybe occurring on the notion of 2.5. But for now I think in Web 2.0, all business founders and employees are known and they held accountable for their public comments and their actions. If Web 3.0 enables us to be anonymous, if DAOs have voting control, you know. What happens if people make comments and there is no way to know who they are, basically. What if the DAO doesn't take appropriate action? I think eventually there will be an element of community self-regulation where influencers will be acting in the best interest of their reputation. And I believe that the communities will self-regulate themselves and will create natural boundaries around what can be said or not said. >> I think that's a really good point about influencers and reputation because. Jeremiah, does it matter that you're anonymous have an icon that could be a NFT or a picture. But if I have an ongoing reputation I have trust, to this trust there. It's not like just a bot that was created just to spam someone. You know I'm starting to getting into this new way. >> You're right, and that word you said trust, that's what really this is about. But we've seen that public docs, people with their full identities have made mistakes. They have pulled the hood over people's faces and really scammed them out of a lot of money. We've seen that in the, that doesn't change anything in human behavior. So I think over time that we will see a new form of a reputation system emerge even for pseudonym and perhaps for people that are just anonymous that only show their potential wallet, address a series of numbers and letters. That form might take a new form of a Web 3.0 FICO Score. And you could look at their behaviors. Did they transact, you know, how did they behave? Were they involved in projects that were not healthy? And because all of that information is public on the chain and you can go back in time and see that. We might see a new form of a scoring emerge, of course. Who controls that scoring? That's a whole nother topic gone on controling and trust. So right now, John we do see that there's a number of projects, new NFT projects, where the founders will claim and use this as a point of differentiation that they are fully docs. So you know who they are and in their names. Secondly, we're seeing a number of products or platforms that require KYC, you know, your customers. So that's self-identification often with a government ID or credit card in order to bridge out your coins and turn that into fiat. In some cases that's required in some of these marketplaces. So we're seeing a collision here between our full names and pseudonyms and being anonymous. >> That's awesome. And I think this is the new, again, a whole new form of governance. Ren, you mentioned some comments about DAO. I want to get your thoughts again. You know, Jeremiah we've become historians over the years. We're getting old I'm a little bit older than you. (Jeremiah laughs) But we've seen the- >> You're young men. You know, I remember breaking in the business when the computer standards bodies were built to be more organic and then they became much more of a, kind of an anti-innovation environment where people, the companies would get involved, the standards organization just to slow things DAO and mark things up a little bit. So, you know, you look at DAOs like, hmm, is DAO a good thing or a bad thing. The answer is from people I talk to is, it depends. So I'd love to get your thoughts on getting momentum and becoming defacto with value, a value proposition, vis-a-vis just a DAO for the sake of having a DAO. This has been a conversation that's been kind of in the inside the baseball here, inside the ropes of the industry, but there's trade offs. Can you guys share your thoughts on when to do a DAO and when not to do a DAO and the benefits and trade offs of that? >> Sure, maybe I'll start off with a definition and then we'll go to, Ren. So a DAO, a decentralized autonomous organization, the best way to think about this It's a digital cooperative. and we've heard of worker cooperatives before. The difference is that they're using blockchain technologies in order to do three things, identity, governance, and rewards and mechanisms. They're relying on Web 2.0 tools and technologies like discord and Telegram and social networks to communicate. And as a cooperative they're trying to come up with a common goal. Ren, what's your take, that's the setup. >> So, you know for me when I started my journey into crypto and Web 3.0, I had no idea about what DAO actually meant. And an easy way for me to think of it and to grasp the nature of it was about the comparison between a DAO and perhaps a more traditional company structure, you know. In the traditional company structure, you have (indistinct), the company's led by a CEO and other executives. The DAO is a flat structure, and it's very much led by a group of core contributors. So to Jeremiah's point, you know, you get that notion of a cooperative type of structure. The decision making is very different, you know. We're talking about a super high level of transparency proposals getting submitted and voting systems using (indistinct) as opposed to, you know, management, making decisions behind closed doors. I think that speaks to a totally new form of governance. And I think we have hardly, hardly scratched the surface. We have seen recently very interesting moments in Web 3.0 culture. And we have seen how DAO suddenly have to make certain decisions and come to moments of claiming responsibility in order to police behavior of some of the members. I think that's important. I think it's going to redefine how we're thinking about that particularly new governance models. And I think it's going to pave the way for a lot of super interesting structure in the near future. >> Yeah and that's a great point. >> Go ahead, Jeremiah. >> That's a great point, Ren. Around the transparency for governance. So, John you post the question, does this make things faster or slower? And right now in the most doubts are actually pretty slow because they're set up as a flat organization. So as a response to that they're actually shifting to become representative democracies. Does that sound familiar? Or you can appoint delegates and use tokens to vote for them and they have a decision power. Almost like a committee and they can function. And so we've seen actually there sometimes are hierarchy except the person at the top is voted by those that have the tokens. In some cases, the people at the top had the most tokens. But that's a whole nother topic. So we're seeing a wide variety of governance structures. >> You know, Ren I was talking with Matt G, the Founder of Unstoppable. And I was telling him about the Domain Name System. And one little trivia note that many people don't know about is that the US government 'cause the internet was started by the US. The Department of Commerce kept that on tight leash because the international telecommunications wanted to get their hands on it because of ccTLDs and other things. So at that time, 'cause the innovation yet was isn't yet baked out. It was organically growing the governance, the rules of the road, keeping it very stable versus melding with it. So there's certain technologies that require, Jeremiah that let's keep an eye on as a community let's not formalize anything. Like the government did with the Domain Name System. Let's keep it tight and then finally released it. I think multiple years after 2004, I think it went over to the ITU. But this is a big point. I mean, if you get too structured, organic innovation can't go. What's you guys reaction to that? >> So I think, you know to take the stab at it. We have as a business, you know, thinking of Unstoppable Domains, a strong incentive to innovate. And this is what is going to be determining long-term value growth for the organization, for partners, for users, for customers. So you know the degree of formalization actually gives us a sense of purpose and a sense of action. And if you compare that to DAO, for instance, you can see how some of the upsides and downsides can pan out either way. It's not to say that there is a perfect solution. I think one of the advantages of the DAO is that you can let more people contribute. You can probably remove buyers quite effectively and you can have a high level of participation and involvement in decisions and own the upside in many ways. You know as a company, it's a slightly different setup. We have the opportunity to coordinate a very diverse and part-time workforce in a very you a different way. And we do not have to deal with the inefficiencies that might be inherent to some form of extreme decentralization. So there is a balance from an organizational structure that comes either side. >> Awesome. Jeremiah, I want to get your thoughts on a trend that you've been involved in, we've both been involved in. And you're seeing it now with the kind of social media world, the world of the role of an influencer. It's kind of moved from what was open source and influencer was a connect to someone who shared, created content enabled things to much more of a vanity. You update the photo on Instagram and having a large audience. So is there a new influencer model with Web 3.0 or is it, I control the audience I'm making money that way. Is there a shift in the influencer role or ideas that you see that should be in place for what is the role of an influencer? 'Cause as Web 3.0 comes you're going to see that role become instrumental. We've seen it in open source projects. Influencers, you know, the people who write code or ship code. So what's your take on that? Because this has been a conversation. People have been having the word influencer and redefining and reframing it. >> Sure, the influence model really hasn't changed that much, but the way that they're behaving has when it comes to Web 3.0. In this market, I mean there's a couple of things. Some of the influencers are investors. And so when you see their name on a project or a new startup, that's an indicator there's a higher level of success. You might want to pay more attention to it or not. Secondly, influencers themselves are launching their own NFT projects. So, Gary Vaynerchuk, a number of celebrities, Paris Hilton is involved. They are also doing theirs as well. Steve Aok, famous DJ launched his as well. So they're going head first and participating in building in this model. And their communities are coming around them and they're building economy. Now the difference is it's not I speak as an influencer to the fans. The difference is that the fans are now part of the community and they literally hold and own some of the economic value, whether it's tokens or the NFTs. So it's a collaborative economy, if you will, where they're all benefiting together. And that's a big difference as well. >> Can you see- >> Lastly, there's one little tactic we're seeing where marketers are air dropping NFTs, branded NFTs influencers wallet. So you can see it in there. So there's new tactics that are forming as well. Back to you. >> That's super exciting. Ren, what's your reaction to that? Because he just hit on a whole new way of how engagement's happening, how people are closed looping their votes, their votes of confidence or votes with their wallet. And the brands which are artists now influencers. I mean, this is a whole game changing instrumentation level. >> I think that what we are seeing right now is super reinvigorating as a marketeer who's been around for a few years, basically. I think that the shift in the way brands are going to communicate and engage with their audiences is profound. It's probably as revolutionary and even more revolutionary than the movement for brands in getting into digital. And you have that sentiment of a gold rush right now with a lot of brands that are trying to understand NFTs and how to actually engage with those communities and those audiences. There are many levels in which brands and influencers are going to engage. There are many influencers that actually advance the message and the mission because the explosion of content on Web 3.0 has been crazy. Part of that is due to the network effect nature of crypto. Because as Jaremiah mentioned, people are incentivized to promote projects. Holders of an NFT are also incentivized to promote it. So you end up with a fly wheel which is pretty unique of people that are hyping their project and that are educating other people about it and commenting on the ecosystem with IP right being given to NFT holders. You're going to see people promote brands instead of the brands actually having to. And so the notion of brands are gaining and delivering elements of the value to their fans is something that's super attractive, extremely interesting. And I think again, we have hardly scratched the surface of all that is possible in that particular space. >> That's interesting. You guys are bringing some great insight here. Jeremiah, the old days the word authentic was a kind of a cliche and brands like tried to be authentic. And they didn't really know what to do they called it organic, right? And now you have the trust concept with authenticity and environment like Web 3.0 where you can actually measure it and monetize it and capture it if you're actually authentic and trustworthy. >> That's right, and be because it's on blockchain, you can see how somebody's behaved with their economic behavior in the past. Of course, big corporations aren't going to have that type of trail on blockchain just yet. But individuals and executives who participate in this market might be. And we'll also see new types of affinity. Do executives do they participate in these NFT communities, do they purchase them or numerous brands like Adidas to acquire, you know, different NFT projects to participate. And of course the big brands are grabbing their domains. Of course you could talk to, Ren about that because it's owning your own name is a part of this trust and being found. >> That's awesome. Great insight guys. Closing comments, takeaways for the audience here. Each of you take a minute to share your thoughts on what you think is happening now where it goes, all right, where's it going to go? Jeremiah, we'll start with you. >> Sure, I think the vision of Web 3.0 where full decentralization happens, where the power is completely shifted to the edges. I don't think it's going to happen. I think we will reach Web 2.5. And I've been through so many tech trends where we said that the power's going to shift completely to of the end, it just doesn't. In part there's two reasons. One is the venture capital are the ones who tend to own the programs in the first place. And secondly, the startups themselves end up becoming the one-percenter. We see Airbnb and Uber are one-percenter now. So that trend happens over and over and over. Now with that said, the world will be in a better place. We will have more transparency. We will see economic power shifted to the people, the participants. And so they will have more control over the internet that they are building. >> Awesome, Ren final comments. >> I'm fully aligned with Jeremiah on the notion of control being returned to users, the notion of ownership and the notion of redistribution of the economic value that is created across all the different chains that we are going to see and all those ecosystems. I believe that we are going to witness two parallel movements of expansion. One that is going to be very lateral. When you think of crypto and Web 3.0 essentially you think of a few 100 tribes. And I think that more projects are going to be a more coalitions of individuals and entities, and those are going to exist around those projects. So you're going to see, you know, an increase in the number of tribes that one might join. And I also think that we're going to progress rapidly from the low 100 millions of crypto and NFT holders into the big hands basically. And that's going to be extreme interesting. I think that the next waves of crypto users, NFT fans are going to look very different from the early adopters that we had witnessed in the very early days. So it's not going to be your traditional model of technology adoption curves. I think the demographics are going to shift and the motivations are going to be different as well, which is going to be a wonderful time to educate and engage with new community members. >> All right, Ren and Jeremiah, thank you both for that great insight great segment breaking down Web 3.0 or Web 2.5 as Jeremiah says but we're in a better place. This is a segment with the influencers. As part of theCUBE and the Unstoppable Domain Showcase. I'm John Furrie, your host. Thanks for watching. (bright upbeat music)

>>Hello, and welcome to the cube unstoppable Doneen showcase. I'm John furrier, host of the cube. We got a great discussion here called the influencers around what's going on in web three and also this new sea change cultural change around this next generation, internet web cloud, all happening, Jeremiah yang industry analyst, and founding part of the cleaner insights. Share my great to see you. Thanks for coming on. Appreciate it. Uh, registered vice-president of marketing at unstoppable domains in the middle of all the actions. Gentlemen, thanks for coming on on the cube for this showcase. >>My pleasure. So I think it was done >>At Jeremy. I want to start with you. You've seen many ways, but fallen all of your work for over a decade now. Um, you've seen the web 2.0 wave. Now the web three's here. Um, and it's not, I wouldn't say hyped up. It's really just ramping up and you're seeing real practical examples. Uh, you're in the middle of all the action. What is this web three? Can you frame for us that mean you've seen many waves? What is web three mean? What is it? What is it all about? >>Well, John, you and I worked in the web to space and essentially that enabled peer to peer media where people could, could upload their thoughts and ideas and videos, um, without having to rely on centralized media. And unfortunately that distributed and decentralized movement actually became centralized on the platforms or the big social networks and big tech companies. And this has caused an uproar because the people who are creating the content did not have control, could not control their identities and could not really monetize or make decisions. So web three is what is, which is a moniker of a lot of different trends, including crypto blockchain. And sometimes the metaverse is to undo the controlling that has become centralized. And the power is now shifting back into the hands of the participants again, and then this movement, they want to have more control over their identities, their governance, the content that they're creating, how they're actually building it and then how they're monetizing it. So in many ways, it's, it's changing the power and it's a new economic model. So that's web three without really even mentioning the technologies. Is that helpful? >>Yeah, that's great. And ran. We were talking about, on the cute many times and one notable stat, I don't think it's been reported, but it's been more kind of a rumor. I hear that 30% of the, um, Berkeley computer science students are dropping out and going into crypto or blockchain or decentralized startups, which means that this there's a big wave coming in of talent. You seeing startups, you're seeing a lot more formation. You're seeing a lot more, I would say, kind of ramping up of real people, not just, you know, people with a dream it's actual builders out here doing stuff. What's your take on the web three, moving with all this kind of change happening, uh, from people and also the new ideas being refactored. >>I think that the competition for talent is extremely real. And we start looking at the stats. We see that there is an draft of people that are moving into this space. People that are fascinated by technology and are embracing the ethos of web three. And at this stage, I think it's not only engineers and developers, but we have moved into a second phase where we see that a lot of supporting functions know marketing, being one of them, sales, business development, uh, are being built up quite rapidly. It's not without actually reminding me of the mid two thousands. You know, when I started, uh, working with Google at that point in time, the walled gardens rightly absorbing vast, vast cohorts of young graduates and more experienced professionals that are passionate and moving into the web environment. And I think we are seeing a movement right now, which is not entirely dissimilar, except >>Yeah, Jeremiah. You've seen the conversations over the cloud. I call the cloud kind of revolution. You had mobile in 2007, but then you got Amazon web services changed the application space on how people developed in the cloud. And again, that created a lot of value. Now you're seeing the role of data as a huge part of how people are scaling and the decentralized movement. So you've got cloud, which is kind of classic today. State-of-the-art, you know, enterprise and or app developers and you've got now decentralized wave coming. Okay. You're seeing apps being developed on that, that architecture data is central in all of this, right. So how do you view this? As, as someone who's watching the landscape, you know, these walled gardens are hoarding all the data. I mean, LinkedIn Facebook, they're not sharing that data with anyone they're using it for themselves. So as they can control back, comes to the forefront, how do you see this market with the applications and what comes out of that? >>So the thing that we've seen and out of the five things that I had mentioned that are decentralizing, the ones that have been easier to move across have been the ability to monetize and to build. But the data aspect has actually stayed pretty much central. Frankly. What has decentralized is that the contracts to block blockchain ledgers to those of decentralized. But the funny thing is often a big portion of these blockchain networks are on Amazon 63 to 70%, same thing with Stelara. So they're still using the web 2.0 architectures. However, we're also seeing other farms like IPFS, where the data could be to spread it across a wider range of folks. But right now we're still dependent on what we're to point out. So the vision and the problem with 3.0, when it comes to full de-centralization is not here by any means. I'd say we're at a web 2.2, five, >>Pre-web 3m, no actions there. What do you guys, how do you guys see the, um, the dangers? Cause there's a lot of negative press, but also is a lot of positive press. You seeing, you know, a lot of fraud, we've seen a lot of the crypto fraud over the past years. You've seen a lot of now positives, it's almost a self-governance thing and environment, the way the culture is, but what are the dangers? How do you guys educate people? What should people pay attention to? What should people look for to understand, you know, where to position themselves? >>Yes. So we've learned a lot from web one, we to the sharing economy and we are walking into two and three with eyes wide open. So people have rightfully put forth a number of challenges, the sustainability issues with excess using of computing and mining, the, um, the excessive amount of scams that are happening in part due to unknown identities. Um, also the architecture breaks down in certain periods and there's a lack of regulation. Um, this, this is something different though in the last, uh, uh, periods that we've gone through, we didn't really know what was gonna happen. And we walked in big, this is going to be great. The sharing economy, the gig economy, the social media is going to change the world. Hurrah is very different. Now people are a little bit jaded. So I think that's the big change. And so I think we're going to see that, uh, you know, soar it out and suss out just like we've seen with other prints. It's still very much in the early years, >>Right. I got to get your take on this whole, uh, should influencers and should people be anonymous or should they be doxed out there? You saw the board eight guys that did, that were kind of docs a little bit there and that went, went viral. Um, this is an issue, right? Because we, we just had a problem of fake news, uh, fake people, fake information, and now you have a much more secure environment. Immutability is a wonderful thing. It's, it's a feature, not a bug, right. So how is this all coming down? And I know you guys are in the middle of it with, uh, NFTs as, as authentication tickets. What's your take on this because this is a big issue. >>Look, I think first I am extremely optimistic about technology in general. Uh, so I'm super, super bullish about this. And yet, you know, I think that while crypto has so many upsides, it's important to be super conscious and aware of the downsides that come with it too. You know, if you think about every fortune 500 company, there is always training required by all employees on internet safety reporting of potential attacks. And so on in web three, we don't have that kind of standard reporting mechanisms yet, uh, for bad actors in that space. And so when you think about influencers in particular, they do have a responsibility to educate people about, uh, the potential, but also the dangers of the technology of web three, uh, of crypto basically, uh, whether you're talking about hacks online safety, the need for hardware impersonators on discord, uh, security, uh, storing your, your seed phrase. >>So every actor in France or ELs has got a role to play. I think that, uh, in that context, to your point, it's very hard to tell whether influencers should be, uh, anonymous, opposite inverse or footy dogs. The decentralized nature of web three will probably lead us to see a combination of those anonymity levels, um, so to speak, um, and the, uh, movements that we've seen around some influencers, identities becoming public are particularly interesting. I think there's probably a convergence of web two and web three at play here. You know, maybe a on the notion of 2.5 for, I think in way to all business founders and employees are known and they're held accountable for their public comments and actions. Um, if web three enables us to be anonymous, if dials have 14 control, you know, what happens if people make comments and there is no way to know who they are basically, uh, what if the dowel doesn't take appropriate action? I think eventually there will be an element of community self-regulation where influencers will be, uh, acting in the best interest of their reputation. And I believe that the communities will self regulate themselves and we'll create natural boundaries around what can be said or not. >>I think that's a really good point about, um, influencers and reputation because Jeremiah doesn't matter that you're anonymous. I have an icon that could be a NFT or a picture, but if I have an ongoing reputation, I have trust there's trust there. It's not like a, you know, just a bot that was created just to spam someone. It was just, you know what I'm saying? They getting into you getting into this new way. >>You're right. And that, that word you said, trust, that's what really, this is about. But we've seen that public docks people with their full identities have made mistakes. They have pulled the hood over people's faces in and really scammed them out of a lot of money. We've seen that in it that doesn't change anything in human behavior. So I think over time that we will see a new form of a reputation system emerged even for pseudonyms and perhaps for people that are just anonymous that only show their a potential, a wallet address, a series of numbers and letters. Um, that form might take a new form of a web 3.0 FICO score, and you can look at their behaviors. Did they transact? You know, how do they behave? Do they, were they involved in projects that were not healthy? And because all of that information is public on the chain and you can go back in time and see that we might see a new form of, of, of a scoring emerge. >>Of course, who controls that scoring that's a whole nother topic, gong on control and trust. So right now, John, we do see that there's a number of projects, new NFG projects, where the founders will claim and use this as a point of differentiation that they are fully docs. So you know who they are and their names. Secondly, we're seeing a number of, um, uh, products or platforms that require KYC, know your customer so that self-identification often with a government ID or a credit card in order to bridge out your, your coins and turn that into a Fiat. In some cases that's required in some of these marketplaces. So we're seeing a coalition here between, uh, full names and pseudonyms and being anonymous. >>That's awesome. And that, and I think this is the new, again, a whole new form of governance ran. You mentioned some comments about Dow. So I want to get your thoughts again, you know, Jeremiah, we become historians over the years. We're getting old, I'm a little bit older than you, but we've seen the movie war. You know, I remember breaking in the business when the computer standards bodies were built to be more organic, and then they became much more of a kind of an anti-innovation environment where people, the companies would get involved the standards organization just to slow things down and muck things up a little bit. Um, so you know, you look at Dallas like, Hmm, is a Dal, a good thing, or a bad thing that the answer is from people I talked to, is it depends. So I'd love to get your thoughts on getting momentum and becoming defacto with value, a value proposition. Vis-a-vis just adapt for the sake of having a doubt. This has been a conversation that's been kind of in the inside the baseball here, inside the ropes of the industry, but there's trade-offs, can you guys share your thoughts on when to do a Dow and when not to do a Dow and the benefits and trade-offs of that? >>Sure. Maybe I'll start off with a definition and then we'll go to rent. So a Dao, a decentralized autonomous organization, the best way to think about this. It's a digital cooperative and we've heard of worker cooperatives before the differences that they're using blockchain technologies in order to do the three things, identity governance, and rewards and mechanisms. They're relying on web 2.0 tools and technologies like discord and telegram and social networks to communicate. And there's a cooperative they're trying to come up with a common goal, um, Ren, but what's your take, that's the setup? >>So, you know, for me, when I started my journey into crypto and web tree, I had no idea about, you know, what that actually meant and, uh, an easy way for me to think of it and to grasp the nature of it was about the comparison between a dowel and perhaps a more traditional company structure. Um, you know, in a traditional company structure, you have a Yorkie, the company is led by a CEO and other executives, uh, that that was a flat structure. And it's very much led by a group of core contributors. So, uh, to Jeremiah's point, you know, you get that notion of a co-operative, uh, type of structure. The decision-making is very different. You know, we're talking about a hot, super high level of transparency proposals getting submitted and, and voting systems, using applications, as opposed to, you know, management, making decisions behind closed doors. >>I think that speaks to a totally new form of governance. And I think we have hardly, hardly scratched the surface. We have seen recently, uh, very interesting moments in web tree culture. And we have seen how that was suddenly have to make certain decisions and then come to moments of claiming responsibility, uh, in order to, uh, put his behavior, uh, of some of the members. I think that's important. I think it's going to redefine how we're thinking about that, particularly new governance models. And I think he's going to pave the way for a lot of super interesting structure in the near future. >>That's a great point, ran around the transparency for governance. So John, you posed the question, does this make things faster or slower? And right now most dowels are actually pretty slow because they're set up as a flat organization. So as a response to that, they're actually shifting to become representative democracies. Does that sound familiar where you can appoint a delegates and use tokens to vote for them? And they have a decision power, almost like a committee and they can function. And so we've seen actually there are some times our hierarchies, except the person at the top is voted by those that have the tokens. In some cases, the people at the top had the most tokens, but that's a whole nother topic. So we're seeing a wide variety of governance structures, >>You know, rent. I was talking with Matt G the founder of, and I was telling him about the domain name system. And one little trivia note that many people don't know about is that the U S government cause unit it was started by the U S the department of commerce kept that on tight leash because the international telecommunications union wanted to get their hands on it because of ccTLDs and other things. So at that time, because the innovation yet wasn't yet baked out. It was organically growing the governance, the rules of the road, keeping it very stable versus meddling with it. So there's certain technologies that require Jeremiah that let's keep an eye on as a community. Let's not formalize anything like the government did with the domain name system. Let's keep it tight. And then finally released it, I think multiple years after 2004, I think it went over to the, to the ITU, but this is a big point. I mean, if you get too structured, organic innovation, can't go, what you guys' reaction to that. >>So I think to take a stab at it, um, we have as a business, you know, thinking of unstoppable domains, a strong incentive to innovate, uh, and this is what is going to be determining longterm value growth for the organization for, uh, partners, for users, for customers. So, you know, that degree of formalization actually gives us a sense of purpose and a sense of action. And if you compare that to Dows, for instance, you can see how some of the upsides and downsides can pan out either way. It's not to say that there is a perfect solution. I think one of the advantages of the Dow is that you can let more people contribute. You can probably remove bias quite effectively, and you can have a high level of participation and involvement in decisions and all the upside in many ways. Um, you know, as a company, it's a slightly different setup. We have the opportunity to coordinate a very, uh, diverse and part-time workforce in a very, uh, you know, different way. Um, and we do not have to deal with the inefficiencies that might be, you never run to some form of extreme decentralization so that those are balanced from an organizational structure, uh, that comes, uh, either side >>Sharon. I want to get your thoughts on, on, on a trend that you've been involved in. We both been involved in, and you're seeing it now with the kind of social media world, the world of a role of an influencer it's kind of moved from what was open source and influencer was a connect to someone who shared graded content, um, enabled things to much more of a vanity that the photo on Instagram and having a large audience. Um, so is there a new influencer model with web three or is it, is it the, I control the audience I'm making money that way. Is there a shift in the influencer role or, or ideas that you see that should be in place for what is the role of an influencer? Because as web three comes, you're going to see that role become instrumental. We've seen it in open source projects, influences, you know, the people who write code or ship code. So what's your take on that because there's been a conversation with people who have been having the word influencer and redefining and reframing it. >>Sure. The influence model really hasn't changed that much, but the way that they're behaving has when it comes to at three, this market, I mean, there's a couple of things. Some of the influencers are in investors. And so when you see their name on a project or a new startup, that's an indicator, there's a higher level of success. You might want to pay more attention to it or not. Secondly, influencers themselves are launching their own NFC projects. Gary Vaynerchuk, a number of celebrities, Paris Hilton is involved and they are also doing this as well. Steve Aoki, a famous DJ launched his as well. So they're going head first and participating in building in this model. And there are communities are coming around them and they're building economies. Now the difference is it's not, I speak as an influencer to the fans. The difference is that the fans are now part of the community and they hold, they literally holding own some of the economic value, whether it's tokens or the NFTs. So it's a collaborative economy, if you will, where they're all benefiting together. And that's a, that's a big difference as well. Lastly, there's, there's one little tactic we're seeing where marketers are airdropping in FTS, branded NFTs influencers with wallet. So you can see it in there. So there's new tactics that are forming as well. Yes. >>Super exciting. Ren, what's your reaction to that? Because he just hit on a whole new way of, of how engagement's happening, how people are closed, looping their, their votes, their, their votes of confidence or votes with their wallet. Um, and some brands which are artists now, influencers. I mean, this is a whole game-changing instrumentation level. >>I think that's what we are seeing right now is super re invigorating as a marketeer who has been around for a few years, basically. Um, I think that the shift in the web brands are going to communicate and engage with our audiences is profound. It's probably as revolutionary and even more revolutionary than the movement for, uh, brands in getting into digital. And you have that sentiment of a gold rush right now with a lot of brands that are trying to understand NFTs and, and how to actually engage with those communities and those audiences, um, dominate levels in which brands and influencers are going to engage. There are many influencers that actually advanced the message and the mission because the explosion of content on web tree has been crazy. Part of that is due to the network effect nature of crypto, because as Jeremiah mentioned, people are incentivized to promote projects, holders of an NFTA, also incentivized to promote it. So you end up with a flywheel, which is pretty unique of people that are hyping the project, and that are educating other people about it and commenting on the ecosystem, uh, with IP rights, being given to NFT holders, you're going to see people pull a brand since then of the brands actually having to. And so the notion of brands, again, judging and delivering, you know, elements of the value to their fans is something that's super attractive, extremely interesting. And I think, again, we've hardly scratched the surface of all that is possible in that. >>It's interesting. You guys are bringing some great insight here, Jeremiah, the old days, the word authentic was a kind of a cliche and brands like tried to be authentic and they didn't really know what to do. They called it organic, right? And now you have the trust concept with aura authenticity and environment like web three, where you can actually measure it and monetize it and capture it if you're actually authentic and trustworthy. >>That's right. And because it's on blockchain, you can see how somebody is behave with their economic behavior. In the past, of course, big corporations. Aren't going to have that type of trail on blockchain just yet. But the individuals and executives who participate in this market might be, and we'll also see a new types of affinity. Do you executives, do they participate in these NFT communities? Do they purchase them? We're seeing numerous brands like Adidas to acquire, uh, you know, different MTV projects to participate. And of course the big brands are grabbing their domains. Of course, you can talk to rant about that because it's owning your own name as a part of this trust and being >>That's awesome. Great insight guys. Closing comments, takeaways for the audience here. Each of you take a minute to give, share your thoughts on what you think is happening now, where it goes. All right, where's it going to go, Jeremy, we'll start with you. >>Sure. Um, I think the vision of web three, where full decentralization happens, where the power is completely shifted to the edges. I don't think it's going to happen. I think we will reach web 2.5 and I've been through so many tech trends where we said that the power is going to shift completely to the end. It just doesn't, there's two reasons. One is the venture capital are the ones who tend to own the pro programs in the first place. And secondly, the, the startups themselves end up becoming the one percenters. We see Airbnb and Uber are one-percenters now. So that trend happens over and over and over. Now with that said, the world will be in a better place. We will have more transparency. We will see economic power shifted to the people, the participants. And so they will have more control over the internet that they are building. >>Right. And final, final comments, >>Um, fully aligned with Jeremiah on the notions of control, being returned to users, the notion of ownership and the notion of redistribution of the economic value that is created across all the different chains, uh, uh, that we are going to see. And, and all those ecosystems. I believe that we are going to witness to palliate movements of expansion, one that is going to be very lateral. When you think of crypto and web three, essentially you think of a few hundred tribes. Uh, and I think that more projects are going to appear more, uh, coalitions of individuals and entities, and those are going to exist around those projects. So you're going to see an increase in the number of tribes that one might join. And I also think that we're going to progress rapidly from the low hundred millions of people and an FTE holders into the billions perfectly. Uh, and that's going to be extremely interesting. I think that the next wave of crypto users and Ft fans are going to look very different from the early adopters that we had witnessed in the very early days. So it's not going to be your traditional model of technology, adoption curves. I think the demographics going to shift and the motivations are going to be different as well, which is going to be a wonderful time to educate and engage with new community members. >>All right, Ron, Jeremy, thank you both for that great insight, great segment, uh, breaking down web three or web 2.5 as Jeremiah says, but we're in a better place. This is a segment with the influencers as part of the cubes and the unstoppable domain showcase. Um, John for your hosts. Thanks for watching.

>> From theCUBE studios in Palo Alto, in Boston, bringing you data driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vollante. >> The rise of Kubernetes came about through a combination of forces that were, in hindsight, quite a long shot. Amazon's dominance created momentum for Cloud native application development, and the need for newer and simpler experiences, beyond just easily spinning up computer as a service. This wave crashed into innovations from a startup named Docker, and a reluctant competitor in Google, that needed a way to change the game on Amazon and the Cloud. Now, add in the effort of Red Hat, which needed a new path beyond Enterprise Linux, and oh, by the way, it was just about to commit to a path of a Kubernetes alternative for OpenShift and figure out a governance structure to hurt all the cats and the ecosystem and you get the remarkable ascendancy of Kubernetes. Hello and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis, we tapped the back stories of a new documentary that explains the improbable events that led to the creation of Kubernetes. We'll share some new survey data from ETR and commentary from the many early the innovators who came on theCUBE during the exciting period since the founding of Docker in 2013, which marked a new era in computing, because we're talking about Kubernetes and developers today, the hoodie is on. And there's a new two part documentary that I just referenced, it's out and it was produced by Honeypot on Kubernetes, part one and part two, tells a story of how Kubernetes came to prominence and many of the players that made it happen. Now, a lot of these players, including Tim Hawkin Kelsey Hightower, Craig McLuckie, Joe Beda, Brian Grant Solomon Hykes, Jerry Chen and others came on theCUBE during formative years of containers going mainstream and the rise of Kubernetes. John Furrier and Stu Miniman were at the many shows we covered back then and they unpacked what was happening at the time. We'll share the commentary from the guests that they interviewed and try to add some context. Now let's start with the concept of developer defined structure, DDI. Jerry Chen was at VMware and he could see the trends that were evolving. He left VMware to become a venture capitalist at Greylock. Docker was his first investment. And he saw the future this way. >> What happens is when you define infrastructure software you can program it. You make it portable. And that the beauty of this cloud wave what I call DDI's. Now, to your point is every piece of infrastructure from storage, networking, to compute has an API, right? And, and AWS there was an early trend where S3, EBS, EC2 had API. >> As building blocks too. >> As building blocks, exactly. >> Not monolithic. >> Monolithic building blocks every little building bone block has it own API and just like Docker really is the API for this unit of the cloud enables developers to define how they want to build their applications, how to network them know as Wills talked about, and how you want to secure them and how you want to store them. And so the beauty of this generation is now developers are determining how apps are built, not just at the, you know, end user, you know, iPhone app layer the data layer, the storage layer, the networking layer. So every single level is being disrupted by this concept of a DDI and where, how you build use and actually purchase IT has changed. And you're seeing the incumbent vendors like Oracle, VMware Microsoft try to react but you're seeing a whole new generation startup. >> Now what Jerry was explaining is that this new abstraction layer that was being built here's some ETR data that quantifies that and shows where we are today. The chart shows net score or spending momentum on the vertical axis and market share which represents the pervasiveness in the survey set. So as Jerry and the innovators who created Docker saw the cloud was becoming prominent and you can see it still has spending velocity that's elevated above that 40% red line which is kind of a magic mark of momentum. And of course, it's very prominent on the X axis as well. And you see the low level infrastructure virtualization and that even floats above servers and storage and networking right. Back in 2013 the conversation with VMware. And by the way, I remember having this conversation deeply at the time with Chad Sakac was we're going to make this low level infrastructure invisible, and we intend to make virtualization invisible, IE simplified. And so, you see above the two arrows there related to containers, container orchestration and container platforms, which are abstraction layers and services above the underlying VMs and hardware. And you can see the momentum that they have right there with the cloud and AI and RPA. So you had these forces that Jerry described that were taking shape, and this picture kind of summarizes how they came together to form Kubernetes. And the upper left, Of course you see AWS and we inserted a picture from a post we did, right after the first reinvent in 2012, it was obvious to us at the time that the cloud gorilla was AWS and had all this momentum. Now, Solomon Hykes, the founder of Docker, you see there in the upper right. He saw the need to simplify the packaging of applications for cloud developers. Here's how he described it. Back in 2014 in theCUBE with John Furrier >> Container is a unit of deployment, right? It's the format in which you package your application all the files, all the executables libraries all the dependencies in one thing that you can move to any server and deploy in a repeatable way. So it's similar to how you would run an iOS app on an iPhone, for example. >> A Docker at the time was a 30% company and it just changed its name from .cloud. And back to the diagram you have Google with a red question mark. So why would you need more than what Docker had created. Craig McLuckie, who was a product manager at Google back then explains the need for yet another abstraction. >> We created the strong separation between infrastructure operations and application operations. And so, Docker has created a portable framework to take it, basically a binary and run it anywhere which is an amazing capability, but that's not enough. You also need to be able to manage that with a framework that can run anywhere. And so, the union of Docker and Kubernetes provides this framework where you're completely abstracted from the underlying infrastructure. You could use VMware, you could use Red Hat open stack deployment. You could run on another major cloud provider like rec. >> Now Google had this huge cloud infrastructure but no commercial cloud business compete with AWS. At least not one that was taken seriously at the time. So it needed a way to change the game. And it had this thing called Google Borg, which is a container management system and scheduler and Google looked at what was happening with virtualization and said, you know, we obviously could do better Joe Beda, who was with Google at the time explains their mindset going back to the beginning. >> Craig and I started up Google compute engine VM as a service. And the odd thing to recognize is that, nobody who had been in Google for a long time thought that there was anything to this VM stuff, right? Cause Google had been on containers for so long. That was their mindset board was the way that stuff was actually deployed. So, you know, my boss at the time, who's now at Cloudera booted up a VM for the first time, and anybody in the outside world be like, Hey, that's really cool. And his response was like, well now what? Right. You're sitting at a prompt. Like that's not super interesting. How do I run my app? Right. Which is, that's what everybody's been struggling with, with cloud is not how do I get a VM up? How do I actually run my code? >> Okay. So Google never really did virtualization. They were looking at the market and said, okay what can we do to make Google relevant in cloud. Here's Eric Brewer from Google. Talking on theCUBE about Google's thought process at the time. >> One interest things about Google is it essentially makes no use of virtual machines internally. And that's because Google started in 1998 which is the same year that VMware started was kind of brought the modern virtual machine to bear. And so Google infrastructure tends to be built really on kind of classic Unix processes and communication. And so scaling that up, you get a system that works a lot with just processes and containers. So kind of when I saw containers come along with Docker, we said, well, that's a good model for us. And we can take what we know internally which was called Borg a big scheduler. And we can turn that into Kubernetes and we'll open source it. And suddenly we have kind of a cloud version of Google that works the way we would like it to work. >> Now, Eric Brewer gave us the bumper sticker version of the story there. What he reveals in the documentary that I referenced earlier is that initially Google was like, why would we open source our secret sauce to help competitors? So folks like Tim Hockin and Brian Grant who were on the original Kubernetes team, went to management and pressed hard to convince them to bless open sourcing Kubernetes. Here's Hockin's explanation. >> When Docker landed, we saw the community building and building and building. I mean, that was a snowball of its own, right? And as it caught on we realized we know what this is going to we know once you embrace the Docker mindset that you very quickly need something to manage all of your Docker nodes, once you get beyond two or three of them, and we know how to build that, right? We got a ton of experience here. Like we went to our leadership and said, you know, please this is going to happen with us or without us. And I think it, the world would be better if we helped. >> So the open source strategy became more compelling as they studied the problem because it gave Google a way to neutralize AWS's advantage because with containers you could develop on AWS for example, and then run the application anywhere like Google's cloud. So it not only gave developers a path off of AWS. If Google could develop a strong service on GCP they could monetize that play. Now, focus your attention back to the diagram which shows this smiling, Alex Polvi from Core OS which was acquired by Red Hat in 2018. And he saw the need to bring Linux into the cloud. I mean, after all Linux was powering the internet it was the OS for enterprise apps. And he saw the need to extend its path into the cloud. Now here's how he described it at an OpenStack event in 2015. >> Similar to what happened with Linux. Like yes, there is still need for Linux and Windows and other OSs out there. But by and large on production, web infrastructure it's all Linux now. And you were able to get onto one stack. And how were you able to do that? It was, it was by having a truly open consistent API and a commitment into not breaking APIs and, so on. That allowed Linux to really become ubiquitous in the data center. Yes, there are other OSs, but Linux buy in large for production infrastructure, what is being used. And I think you'll see a similar phenomenon happen for this next level up cause we're treating the whole data center as a computer instead of trading one in visual instance is just the computer. And that's the stuff that Kubernetes to me and someone is doing. And I think there will be one that shakes out over time and we believe that'll be Kubernetes. >> So Alex saw the need for a dominant container orchestration platform. And you heard him, they made the right bet. It would be Kubernetes. Now Red Hat, Red Hat is been around since 1993. So it has a lot of on-prem. So it needed a future path to the cloud. So they rang up Google and said, hey. What do you guys have going on in this space? So Google, was kind of non-committal, but it did expose that they were thinking about doing something that was you know, pre Kubernetes. It was before it was called Kubernetes. But hey, we have this thing and we're thinking about open sourcing it, but Google's internal debates, and you know, some of the arm twisting from the engine engineers, it was taking too long. So Red Hat said, well, screw it. We got to move forward with OpenShift. So we'll do what Apple and Airbnb and Heroku are doing and we'll build on an alternative. And so they were ready to go with Mesos which was very much more sophisticated than Kubernetes at the time and much more mature, but then Google the last minute said, hey, let's do this. So Clayton Coleman with Red Hat, he was an architect. And he leaned in right away. He was one of the first outside committers outside of Google. But you still led these competing forces in the market. And internally there were debates. Do we go with simplicity or do we go with system scale? And Hen Goldberg from Google explains why they focus first on simplicity in getting that right. >> We had to defend of why we are only supporting 100 nodes in the first release of Kubernetes. And they explained that they know how to build for scale. They've done that. They know how to do it, but realistically most of users don't need large clusters. So why create this complexity? >> So Goldberg explains that rather than competing right away with say Mesos or Docker swarm, which were far more baked they made the bet to keep it simple and go for adoption and ubiquity, which obviously turned out to be the right choice. But the last piece of the puzzle was governance. Now Google promised to open source Kubernetes but when it started to open up to contributors outside of Google, the code was still controlled by Google and developers had to sign Google paper that said Google could still do whatever it wanted. It could sub license, et cetera. So Google had to pass the Baton to an independent entity and that's how CNCF was started. Kubernetes was its first project. And let's listen to Chris Aniszczyk of the CNCF explain >> CNCF is all about providing a neutral home for cloud native technology. And, you know, it's been about almost two years since our first board meeting. And the idea was, you know there's a certain set of technology out there, you know that are essentially microservice based that like live in containers that are essentially orchestrated by some process, right? That's essentially what we mean when we say cloud native right. And CNCF was seated with Kubernetes as its first project. And you know, as, as we've seen over the last couple years Kubernetes has grown, you know, quite well they have a large community a diverse con you know, contributor base and have done, you know, kind of extremely well. They're one of actually the fastest, you know highest velocity, open source projects out there, maybe. >> Okay. So this is how we got to where we are today. This ETR data shows container orchestration offerings. It's the same X Y graph that we showed earlier. And you can see where Kubernetes lands not we're standing that Kubernetes not a company but respondents, you know, they doing Kubernetes. They maybe don't know, you know, whose platform and it's hard with the ETR taxon economy as a fuzzy and survey data because Kubernetes is increasingly becoming embedded into cloud platforms. And IT pros, they may not even know which one specifically. And so the reason we've linked these two platforms Kubernetes and Red Hat OpenShift is because OpenShift right now is a dominant revenue player in the space and is increasingly popular PaaS layer. Yeah. You could download Kubernetes and do what you want with it. But if you're really building enterprise apps you're going to need support. And that's where OpenShift comes in. And there's not much data on this but we did find this chart from AMDA which show was the container software market, whatever that really is. And Red Hat has got 50% of it. This is revenue. And, you know, we know the muscle of IBM is behind OpenShift. So there's really not hard to believe. Now we've got some other data points that show how Kubernetes is becoming less visible and more embedded under of the hood. If you will, as this chart shows this is data from CNCF's annual survey they had 1800 respondents here, and the data showed that 79% of respondents use certified Kubernetes hosted platforms. Amazon elastic container service for Kubernetes was the most prominent 39% followed by Azure Kubernetes service at 23% in Azure AKS engine at 17%. With Google's GKE, Google Kubernetes engine behind those three. Now. You have to ask, okay, Google. Google's management Initially they had concerns. You know, why are we open sourcing such a key technology? And the premise was, it would level the playing field. And for sure it has, but you have to ask has it driven the monetization Google was after? And I would've to say no, it probably didn't. But think about where Google would've been. If it hadn't open source Kubernetes how relevant would it be in the cloud discussion. Despite its distant third position behind AWS and Microsoft or even fourth, if you include Alibaba without Kubernetes Google probably would be much less prominent or possibly even irrelevant in cloud, enterprise cloud. Okay. Let's wrap up with some comments on the state of Kubernetes and maybe a thought or two about, you know, where we're headed. So look, no shocker Kubernetes for all its improbable beginning has gone mainstream in the past year or so. We're seeing much more maturity and support for state full workloads and big ecosystem support with respect to better security and continued simplification. But you know, it's still pretty complex. It's getting better, but it's not VMware level of maturity. For example, of course. Now adoption has always been strong for Kubernetes, for cloud native companies who start with containers on day one, but we're seeing many more. IT organizations adopting Kubernetes as it matures. It's interesting, you know, Docker set out to be the system of the cloud and Kubernetes has really kind of become that. Docker desktop is where Docker's action really is. That's where Docker is thriving. It sold off Docker swarm to Mirantis has made some tweaks. Docker has made some tweaks to its licensing model to be able to continue to evolve its its business. To hear more about that at DockerCon. And as we said, years ago we expected Kubernetes to become less visible Stu Miniman and I talked about this in one of our predictions post and really become more embedded into other platforms. And that's exactly what's happening here but it's still complicated. Remember, remember the... Go back to the early and mid cycle of VMware understanding things like application performance you needed folks in lab coats to really remediate problems and dig in and peel the onion and scale the system you know, and in some ways you're seeing that dynamic repeated with Kubernetes, security performance scale recovery, when something goes wrong all are made more difficult by the rapid pace at which the ecosystem is evolving Kubernetes. But it's definitely headed in the right direction. So what's next for Kubernetes we would expect further simplification and you're going to see more abstractions. We live in this world of almost perpetual abstractions. Now, as Kubernetes improves support from multi cluster it will be begin to treat those clusters as a unified group. So kind of abstracting multiple clusters and treating them as, as one to be managed together. And this is going to create a lot of ecosystem focus on scaling globally. Okay, once you do that, you're going to have to worry about latency and then you're going to have to keep pace with security as you expand the, the threat area. And then of course recovery what happens when something goes wrong, more complexity, the harder it is to recover and that's going to require new services to share resources across clusters. So look for that. You also should expect more automation. It's going to be driven by the host cloud providers as Kubernetes supports more state full applications and begins to extend its cluster management. Cloud providers will inject as much automation as possible into the system. Now and finally, as these capabilities mature we would expect to see better support for data intensive workloads like, AI and Machine learning and inference. Schedule with these workloads becomes harder because they're so resource intensive and performance management becomes more complex. So that's going to have to evolve. I mean, frankly, many of the things that Kubernetes team way back when, you know they back burn it early on, for example, you saw in Docker swarm or Mesos they're going to start to enter the scene now with Kubernetes as they start to sort of prioritize some of those more complex functions. Now, the last thing I'll ask you to think about is what's next beyond Kubernetes, you know this isn't it right with serverless and IOT in the edge and new data, heavy workloads there's something that's going to disrupt Kubernetes. So in that, by the way, in that CNCF survey nearly 40% of respondents were using serverless and that's going to keep growing. So how is that going to change the development model? You know, Andy Jassy once famously said that if they had to start over with Amazon retail, they'd start with serverless. So let's keep an eye on the horizon to see what's coming next. All right, that's it for now. I want to thank my colleagues, Stephanie Chan who helped research this week's topics and Alex Myerson on the production team, who also manages the breaking analysis podcast, Kristin Martin and Cheryl Knight help get the word out on socials, so thanks to all of you. Remember these episodes, they're all available as podcasts wherever you listen, just search breaking analysis podcast. Don't forget to check out ETR website @etr.ai. We'll also publish. We publish a full report every week on wikibon.com and Silicon angle.com. You can get in touch with me, email me directly david.villane@Siliconangle.com or DM me at D Vollante. You can comment on our LinkedIn post. This is Dave Vollante for theCUBE insights powered by ETR. Have a great week, everybody. Thanks for watching. Stay safe, be well. And we'll see you next time. (upbeat music)