>> 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) >> Hello and welcome to theCube's coverage of International Women's Day. I'm John Furrier, your host of "theCUBE." We've got great two remote guests coming into our Palo Alto Studios, some tech athletes, as we say, people that've been in the trenches, years of experience, Lena Smart, CISO at MongoDB, Cube alumni, and Tara Hernandez, VP of Developer Productivity at MongoDB as well. Thanks for coming in to this program and supporting our efforts today. Thanks so much. >> Thanks for having us. >> Yeah, everyone talk about the journey in tech, where it all started. Before we get there, talk about what you guys are doing at MongoDB specifically. MongoDB is kind of gone the next level as a platform. You have your own ecosystem, lot of developers, very technical crowd, but it's changing the business transformation. What do you guys do at Mongo? We'll start with you, Lena. >> So I'm the CISO, so all security goes through me. I like to say, well, I don't like to say, I'm described as the ones throat to choke. So anything to do with security basically starts and ends with me. We do have a fantastic Cloud engineering security team and a product security team, and they don't report directly to me, but obviously we have very close relationships. I like to keep that kind of church and state separate and I know I've spoken about that before. And we just recently set up a physical security team with an amazing gentleman who left the FBI and he came to join us after 26 years for the agency. So, really starting to look at the physical aspects of what we offer as well. >> I interviewed a CISO the other day and she said, "Every day is day zero for me." Kind of goofing on the Amazon Day one thing, but Tara, go ahead. Tara, go ahead. What's your role there, developer productivity? What are you focusing on? >> Sure. Developer productivity is kind of the latest description for things that we've described over the years as, you know, DevOps oriented engineering or platform engineering or build and release engineering development infrastructure. It's all part and parcel, which is how do we actually get our code from developer to customer, you know, and all the mechanics that go into that. It's been something I discovered from my first job way back in the early '90s at Borland. And the art has just evolved enormously ever since, so. >> Yeah, this is a very great conversation both of you guys, right in the middle of all the action and data infrastructures changing, exploding, and involving big time AI and data tsunami and security never stops. Well, let's get into, we'll talk about that later, but let's get into what motivated you guys to pursue a career in tech and what were some of the challenges that you faced along the way? >> I'll go first. The fact of the matter was I intended to be a double major in history and literature when I went off to university, but I was informed that I had to do a math or a science degree or else the university would not be paid for. At the time, UC Santa Cruz had a policy that called Open Access Computing. This is, you know, the late '80s, early '90s. And anybody at the university could get an email account and that was unusual at the time if you were, those of us who remember, you used to have to pay for that CompuServe or AOL or, there's another one, I forget what it was called, but if a student at Santa Cruz could have an email account. And because of that email account, I met people who were computer science majors and I'm like, "Okay, I'll try that." That seems good. And it was a little bit of a struggle for me, a lot I won't lie, but I can't complain with how it ended up. And certainly once I found my niche, which was development infrastructure, I found my true love and I've been doing it for almost 30 years now. >> Awesome. Great story. Can't wait to ask a few questions on that. We'll go back to that late '80s, early '90s. Lena, your journey, how you got into it. >> So slightly different start. I did not go to university. I had to leave school when I was 16, got a job, had to help support my family. Worked a bunch of various jobs till I was about 21 and then computers became more, I think, I wouldn't say they were ubiquitous, but they were certainly out there. And I'd also been saving up every penny I could earn to buy my own computer and bought an Amstrad 1640, 20 meg hard drive. It rocked. And kind of took that apart, put it back together again, and thought that could be money in this. And so basically just teaching myself about computers any job that I got. 'Cause most of my jobs were like clerical work and secretary at that point. But any job that had a computer in front of that, I would make it my business to go find the guy who did computing 'cause it was always a guy. And I would say, you know, I want to learn how these work. Let, you know, show me. And, you know, I would take my lunch hour and after work and anytime I could with these people and they were very kind with their time and I just kept learning, so yep. >> Yeah, those early days remind me of the inflection point we're going through now. This major C change coming. Back then, if you had a computer, you had to kind of be your own internal engineer to fix things. Remember back on the systems revolution, late '80s, Tara, when, you know, your career started, those were major inflection points. Now we're seeing a similar wave right now, security, infrastructure. It feels like it's going to a whole nother level. At Mongo, you guys certainly see this as well, with this AI surge coming in. A lot more action is coming in. And so there's a lot of parallels between these inflection points. How do you guys see this next wave of change? Obviously, the AI stuff's blowing everyone away. Oh, new user interface. It's been called the browser moment, the mobile iPhone moment, kind of for this generation. There's a lot of people out there who are watching that are young in their careers, what's your take on this? How would you talk to those folks around how important this wave is? >> It, you know, it's funny, I've been having this conversation quite a bit recently in part because, you know, to me AI in a lot of ways is very similar to, you know, back in the '90s when we were talking about bringing in the worldwide web to the forefront of the world, right. And we tended to think in terms of all the optimistic benefits that would come of it. You know, free passing of information, availability to anyone, anywhere. You just needed an internet connection, which back then of course meant a modem. >> John: Not everyone had though. >> Exactly. But what we found in the subsequent years is that human beings are what they are and we bring ourselves to whatever platforms that are there, right. And so, you know, as much as it was amazing to have this freely available HTML based internet experience, it also meant that the negatives came to the forefront quite quickly. And there were ramifications of that. And so to me, when I look at AI, we're already seeing the ramifications to that. Yes, are there these amazing, optimistic, wonderful things that can be done? Yes. >> Yeah. >> But we're also human and the bad stuff's going to come out too. And how do we- >> Yeah. >> How do we as an industry, as a community, you know, understand and mitigate those ramifications so that we can benefit more from the positive than the negative. So it is interesting that it comes kind of full circle in really interesting ways. >> Yeah. The underbelly takes place first, gets it in the early adopter mode. Normally industries with, you know, money involved arbitrage, no standards. But we've seen this movie before. Is there hope, Lena, that we can have a more secure environment? >> I would hope so. (Lena laughs) Although depressingly, we've been in this well for 30 years now and we're, at the end of the day, still telling people not to click links on emails. So yeah, that kind of still keeps me awake at night a wee bit. The whole thing about AI, I mean, it's, obviously I am not an expert by any stretch of the imagination in AI. I did read (indistinct) book recently about AI and that was kind of interesting. And I'm just trying to teach myself as much as I can about it to the extent of even buying the "Dummies Guide to AI." Just because, it's actually not a dummies guide. It's actually fairly interesting, but I'm always thinking about it from a security standpoint. So it's kind of my worst nightmare and the best thing that could ever happen in the same dream. You know, you've got this technology where I can ask it a question and you know, it spits out generally a reasonable answer. And my team are working on with Mark Porter our CTO and his team on almost like an incubation of AI link. What would it look like from MongoDB? What's the legal ramifications? 'Cause there will be legal ramifications even though it's the wild, wild west just now, I think. Regulation's going to catch up to us pretty quickly, I would think. >> John: Yeah, yeah. >> And so I think, you know, as long as companies have a seat at the table and governments perhaps don't become too dictatorial over this, then hopefully we'll be in a good place. But we'll see. I think it's a really interest, there's that curse, we're living in interesting times. I think that's where we are. >> It's interesting just to stay on this tech trend for a minute. The standards bodies are different now. Back in the old days there were, you know, IEEE standards, ITF standards. >> Tara: TPC. >> The developers are the new standard. I mean, now you're seeing open source completely different where it was in the '90s to here beginning, that was gen one, some say gen two, but I say gen one, now we're exploding with open source. You have kind of developers setting the standards. If developers like it in droves, it becomes defacto, which then kind of rolls into implementation. >> Yeah, I mean I think if you don't have developer input, and this is why I love working with Tara and her team so much is 'cause they get it. If we don't have input from developers, it's not going to get used. There's going to be ways of of working around it, especially when it comes to security. If they don't, you know, if you're a developer and you're sat at your screen and you don't want to do that particular thing, you're going to find a way around it. You're a smart person. >> Yeah. >> So. >> Developers on the front lines now versus, even back in the '90s, they're like, "Okay, consider the dev's, got a QA team." Everything was Waterfall, now it's Cloud, and developers are on the front lines of everything. Tara, I mean, this is where the standards are being met. What's your reaction to that? >> Well, I think it's outstanding. I mean, you know, like I was at Netscape and part of the crowd that released the browser as open source and we founded mozilla.org, right. And that was, you know, in many ways kind of the birth of the modern open source movement beyond what we used to have, what was basically free software foundation was sort of the only game in town. And I think it is so incredibly valuable. I want to emphasize, you know, and pile onto what Lena was saying, it's not just that the developers are having input on a sort of company by company basis. Open source to me is like a checks and balance, where it allows us as a broader community to be able to agree on and enforce certain standards in order to try and keep the technology platforms as accessible as possible. I think Kubernetes is a great example of that, right. If we didn't have Kubernetes, that would've really changed the nature of how we think about container orchestration. But even before that, Linux, right. Linux allowed us as an industry to end the Unix Wars and as someone who was on the front lines of that as well and having to support 42 different operating systems with our product, you know, that was a huge win. And it allowed us to stop arguing about operating systems and start arguing about software or not arguing, but developing it in positive ways. So with, you know, with Kubernetes, with container orchestration, we all agree, okay, that's just how we're going to orchestrate. Now we can build up this huge ecosystem, everybody gets taken along, right. And now it changes the game for what we're defining as business differentials, right. And so when we talk about crypto, that's a little bit harder, but certainly with AI, right, you know, what are the checks and balances that as an industry and as the developers around this, that we can in, you know, enforce to make sure that no one company or no one body is able to overly control how these things are managed, how it's defined. And I think that is only for the benefit in the industry as a whole, particularly when we think about the only other option is it gets regulated in ways that do not involve the people who actually know the details of what they're talking about. >> Regulated and or thrown away or bankrupt or- >> Driven underground. >> Yeah. >> Which would be even worse actually. >> Yeah, that's a really interesting, the checks and balances. I love that call out. And I was just talking with another interview part of the series around women being represented in the 51% ratio. Software is for everybody. So that we believe that open source movement around the collective intelligence of the participants in the industry and independent of gender, this is going to be the next wave. You're starting to see these videos really have impact because there are a lot more leaders now at the table in companies developing software systems and with AI, the aperture increases for applications. And this is the new dynamic. What's your guys view on this dynamic? How does this go forward in a positive way? Is there a certain trajectory you see? For women in the industry? >> I mean, I think some of the states are trying to, again, from the government angle, some of the states are trying to force women into the boardroom, for example, California, which can be no bad thing, but I don't know, sometimes I feel a bit iffy about all this kind of forced- >> John: Yeah. >> You know, making, I don't even know how to say it properly so you can cut this part of the interview. (John laughs) >> Tara: Well, and I think that they're >> I'll say it's not organic. >> No, and I think they're already pulling it out, right. It's already been challenged so they're in the process- >> Well, this is the open source angle, Tara, you are getting at it. The change agent is open, right? So to me, the history of the proven model is openness drives transparency drives progress. >> No, it's- >> If you believe that to be true, this could have another impact. >> Yeah, it's so interesting, right. Because if you look at McKinsey Consulting or Boston Consulting or some of the other, I'm blocking on all of the names. There has been a decade or more of research that shows that a non homogeneous employee base, be it gender or ethnicity or whatever, generates more revenue, right? There's dollar signs that can be attached to this, but it's not enough for all companies to want to invest in that way. And it's not enough for all, you know, venture firms or investment firms to grant that seed money or do those seed rounds. I think it's getting better very slowly, but socialization is a much harder thing to overcome over time. Particularly, when you're not just talking about one country like the United States in our case, but around the world. You know, tech centers now exist all over the world, including places that even 10 years ago we might not have expected like Nairobi, right. Which I think is amazing, but you have to factor in the cultural implications of that as well, right. So yes, the openness is important and we have, it's important that we have those voices, but I don't think it's a panacea solution, right. It's just one more piece. I think honestly that one of the most important opportunities has been with Cloud computing and Cloud's been around for a while. So why would I say that? It's because if you think about like everybody holds up the Steve Jobs, Steve Wozniak, back in the '70s, or Sergey and Larry for Google, you know, you had to have access to enough credit card limit to go to Fry's and buy your servers and then access to somebody like Susan Wojcicki to borrow the garage or whatever. But there was still a certain amount of upfrontness that you had to be able to commit to, whereas now, and we've, I think, seen a really good evidence of this being able to lease server resources by the second and have development platforms that you can do on your phone. I mean, for a while I think Africa, that the majority of development happened on mobile devices because there wasn't a sufficient supply chain of laptops yet. And that's no longer true now as far as I know. But like the power that that enables for people who would otherwise be underrepresented in our industry instantly opens it up, right? And so to me that's I think probably the biggest opportunity that we've seen from an industry on how to make more availability in underrepresented representation for entrepreneurship. >> Yeah. >> Something like AI, I think that's actually going to take us backwards if we're not careful. >> Yeah. >> Because of we're reinforcing that socialization. >> Well, also the bias. A lot of people commenting on the biases of the large language inherently built in are also problem. Lena, I want you to weigh on this too, because I think the skills question comes up here and I've been advocating that you don't need the pedigree, college pedigree, to get into a certain jobs, you mentioned Cloud computing. I mean, it's been around for you think a long time, but not really, really think about it. The ability to level up, okay, if you're going to join something new and half the jobs in cybersecurity are created in the past year, right? So, you have this what used to be a barrier, your degree, your pedigree, your certification would take years, would be a blocker. Now that's gone. >> Lena: Yeah, it's the opposite. >> That's, in fact, psychology. >> I think so, but the people who I, by and large, who I interview for jobs, they have, I think security people and also I work with our compliance folks and I can't forget them, but let's talk about security just now. I've always found a particular kind of mindset with security folks. We're very curious, not very good at following rules a lot of the time, and we'd love to teach others. I mean, that's one of the big things stem from the start of my career. People were always interested in teaching and I was interested in learning. So it was perfect. And I think also having, you know, strong women leaders at MongoDB allows other underrepresented groups to actually apply to the company 'cause they see that we're kind of talking the talk. And that's been important. I think it's really important. You know, you've got Tara and I on here today. There's obviously other senior women at MongoDB that you can talk to as well. There's a bunch of us. There's not a whole ton of us, but there's a bunch of us. And it's good. It's definitely growing. I've been there for four years now and I've seen a growth in women in senior leadership positions. And I think having that kind of track record of getting really good quality underrepresented candidates to not just interview, but come and join us, it's seen. And it's seen in the industry and people take notice and they're like, "Oh, okay, well if that person's working, you know, if Tara Hernandez is working there, I'm going to apply for that." And that in itself I think can really, you know, reap the rewards. But it's getting started. It's like how do you get your first strong female into that position or your first strong underrepresented person into that position? It's hard. I get it. If it was easy, we would've sold already. >> It's like anything. I want to see people like me, my friends in there. Am I going to be alone? Am I going to be of a group? It's a group psychology. Why wouldn't? So getting it out there is key. Is there skills that you think that people should pay attention to? One's come up as curiosity, learning. What are some of the best practices for folks trying to get into the tech field or that's in the tech field and advancing through? What advice are you guys- >> I mean, yeah, definitely, what I say to my team is within my budget, we try and give every at least one training course a year. And there's so much free stuff out there as well. But, you know, keep learning. And even if it's not right in your wheelhouse, don't pick about it. Don't, you know, take a look at what else could be out there that could interest you and then go for it. You know, what does it take you few minutes each night to read a book on something that might change your entire career? You know, be enthusiastic about the opportunities out there. And there's so many opportunities in security. Just so many. >> Tara, what's your advice for folks out there? Tons of stuff to taste, taste test, try things. >> Absolutely. I mean, I always say, you know, my primary qualifications for people, I'm looking for them to be smart and motivated, right. Because the industry changes so quickly. What we're doing now versus what we did even last year versus five years ago, you know, is completely different though themes are certainly the same. You know, we still have to code and we still have to compile that code or package the code and ship the code so, you know, how well can we adapt to these new things instead of creating floppy disks, which was my first job. Five and a quarters, even. The big ones. >> That's old school, OG. There it is. Well done. >> And now it's, you know, containers, you know, (indistinct) image containers. And so, you know, I've gotten a lot of really great success hiring boot campers, you know, career transitioners. Because they bring a lot experience in addition to the technical skills. I think the most important thing is to experiment and figuring out what do you like, because, you know, maybe you are really into security or maybe you're really into like deep level coding and you want to go back, you know, try to go to school to get a degree where you would actually want that level of learning. Or maybe you're a front end engineer, you want to be full stacked. Like there's so many different things, data science, right. Maybe you want to go learn R right. You know, I think it's like figure out what you like because once you find that, that in turn is going to energize you 'cause you're going to feel motivated. I think the worst thing you could do is try to force yourself to learn something that you really could not care less about. That's just the worst. You're going in handicapped. >> Yeah and there's choices now versus when we were breaking into the business. It was like, okay, you software engineer. They call it software engineering, that's all it was. You were that or you were in sales. Like, you know, some sort of systems engineer or sales and now it's,- >> I had never heard of my job when I was in school, right. I didn't even know it was a possibility. But there's so many different types of technical roles, you know, absolutely. >> It's so exciting. I wish I was young again. >> One of the- >> Me too. (Lena laughs) >> I don't. I like the age I am. So one of the things that I did to kind of harness that curiosity is we've set up a security champions programs. About 120, I guess, volunteers globally. And these are people from all different backgrounds and all genders, diversity groups, underrepresented groups, we feel are now represented within this champions program. And people basically give up about an hour or two of their time each week, with their supervisors permission, and we basically teach them different things about security. And we've now had seven full-time people move from different areas within MongoDB into my team as a result of that program. So, you know, monetarily and time, yeah, saved us both. But also we're showing people that there is a path, you know, if you start off in Tara's team, for example, doing X, you join the champions program, you're like, "You know, I'd really like to get into red teaming. That would be so cool." If it fits, then we make that happen. And that has been really important for me, especially to give, you know, the women in the underrepresented groups within MongoDB just that window into something they might never have seen otherwise. >> That's a great common fit is fit matters. Also that getting access to what you fit is also access to either mentoring or sponsorship or some sort of, at least some navigation. Like what's out there and not being afraid to like, you know, just ask. >> Yeah, we just actually kicked off our big mentor program last week, so I'm the executive sponsor of that. I know Tara is part of it, which is fantastic. >> We'll put a plug in for it. Go ahead. >> Yeah, no, it's amazing. There's, gosh, I don't even know the numbers anymore, but there's a lot of people involved in this and so much so that we've had to set up mentoring groups rather than one-on-one. And I think it was 45% of the mentors are actually male, which is quite incredible for a program called Mentor Her. And then what we want to do in the future is actually create a program called Mentor Them so that it's not, you know, not just on the female and so that we can live other groups represented and, you know, kind of break down those groups a wee bit more and have some more granularity in the offering. >> Tara, talk about mentoring and sponsorship. Open source has been there for a long time. People help each other. It's community-oriented. What's your view of how to work with mentors and sponsors if someone's moving through ranks? >> You know, one of the things that was really interesting, unfortunately, in some of the earliest open source communities is there was a lot of pervasive misogyny to be perfectly honest. >> Yeah. >> And one of the important adaptations that we made as an open source community was the idea, an introduction of code of conducts. And so when I'm talking to women who are thinking about expanding their skills, I encourage them to join open source communities to have opportunity, even if they're not getting paid for it, you know, to develop their skills to work with people to get those code reviews, right. I'm like, "Whatever you join, make sure they have a code of conduct and a good leadership team. It's very important." And there are plenty, right. And then that idea has come into, you know, conferences now. So now conferences have codes of contact, if there are any good, and maybe not all of them, but most of them, right. And the ideas of expanding that idea of intentional healthy culture. >> John: Yeah. >> As a business goal and business differentiator. I mean, I won't lie, when I was recruited to come to MongoDB, the culture that I was able to discern through talking to people, in addition to seeing that there was actually women in senior leadership roles like Lena, like Kayla Nelson, that was a huge win. And so it just builds on momentum. And so now, you know, those of us who are in that are now representing. And so that kind of reinforces, but it's all ties together, right. As the open source world goes, particularly for a company like MongoDB, which has an open source product, you know, and our community builds. You know, it's a good thing to be mindful of for us, how we interact with the community and you know, because that could also become an opportunity for recruiting. >> John: Yeah. >> Right. So we, in addition to people who might become advocates on Mongo's behalf in their own company as a solution for themselves, so. >> You guys had great successful company and great leadership there. I mean, I can't tell you how many times someone's told me "MongoDB doesn't scale. It's going to be dead next year." I mean, I was going back 10 years. It's like, just keeps getting better and better. You guys do a great job. So it's so fun to see the success of developers. Really appreciate you guys coming on the program. Final question, what are you guys excited about to end the segment? We'll give you guys the last word. Lena will start with you and Tara, you can wrap us up. What are you excited about? >> I'm excited to see what this year brings. I think with ChatGPT and its copycats, I think it'll be a very interesting year when it comes to AI and always in the lookout for the authentic deep fakes that we see coming out. So just trying to make people aware that this is a real thing. It's not just pretend. And then of course, our old friend ransomware, let's see where that's going to go. >> John: Yeah. >> And let's see where we get to and just genuine hygiene and housekeeping when it comes to security. >> Excellent. Tara. >> Ah, well for us, you know, we're always constantly trying to up our game from a security perspective in the software development life cycle. But also, you know, what can we do? You know, one interesting application of AI that maybe Google doesn't like to talk about is it is really cool as an addendum to search and you know, how we might incorporate that as far as our learning environment and developer productivity, and how can we enable our developers to be more efficient, productive in their day-to-day work. So, I don't know, there's all kinds of opportunities that we're looking at for how we might improve that process here at MongoDB and then maybe be able to share it with the world. One of the things I love about working at MongoDB is we get to use our own products, right. And so being able to have this interesting document database in order to put information and then maybe apply some sort of AI to get it out again, is something that we may well be looking at, if not this year, then certainly in the coming year. >> Awesome. Lena Smart, the chief information security officer. Tara Hernandez, vice president developer of productivity from MongoDB. Thank you so much for sharing here on International Women's Day. We're going to do this quarterly every year. We're going to do it and then we're going to do quarterly updates. Thank you so much for being part of this program. >> Thank you. >> Thanks for having us. >> Okay, this is theCube's coverage of International Women's Day. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (light airy music) >> Hi, everybody, welcome back to the Fira in Barcelona. My name is Dave Vellante. I'm here with my co-host Dave Nicholson. Lisa Martin is in the house. John Furrier is pounding the news from our Palo Alto studio. We are super excited to be talking about cloud at the edge, what that means. Charles Tsai is here. He's the Senior Director of product management at Dell Technologies and Peter Fetterolf is the Chief Technology Officer at ACG Business Analytics, a firm that goes deep into the TCO and the telco space, among other things. Gents, welcome to theCUBE. Thanks for coming on. Thank you. >> Good to be here. >> Yeah, good to be here. >> So I've been in search all week of the elusive next wave of monetization for the telcos. We know they make great money on connectivity, they're really good at that. But they're all talking about how they can't let this happen again. Meaning we can't let the over the top vendors yet again, basically steal our cookies. So we're going to not mess it up this time. We're going to win in the monetization. Charles, where are those monetization opportunities? Obviously at the edge, the telco cloud at the edge. What is that all about and where's the money? >> Well, Dave, I think from a Dell's perspective, what we want to be able to enable operators is a solution that enable them to roll out services much quicker, right? We know there's a lot of innovation around IoT, MEG and so on and so forth, but they continue to rely on traditional technology and way of operations is going to take them years to enable new services. So what Dell is doing is now, creating the entire vertical stack from the hardware through CAST and automation that enable them, not only to push out services very quickly, but operating them using cloud principles. >> So it's when you say the entire vertical stack, it's the integrated hardware components with like, for example, Red Hat on top- >> Right. >> Or a Wind River? >> That's correct. >> Okay, and then open API, so the developers can create workloads, I presume data companies. We just had a data conversation 'cause that was part of the original stack- >> That's correct. >> So through an open ecosystem, you can actually sort of recreate that value, correct? >> That's correct. >> Okay. >> So one thing Dell is doing, is we are offering an infrastructure block where we are taking over the overhead of certifying every release coming from the Red Hat or the Wind River of the world, right? We want telcos to spend their resources on what is going to generate them revenue. Not the overhead of creating this cloud stack. >> Dave, I remember when we went through this in the enterprise and you had companies like, you know, IBM with the AS400 and the mainframe saying it's easier to manage, which it was, but it's still, you know, it was subsumed by the open systems trend. >> Yeah, yeah. And I think that's an important thing to probe on, is this idea of what is, what exactly does it mean to be cloud at the edge in the telecom space? Because it's a much used term. >> Yeah. >> When we talk about cloud and edge, in sort of generalized IT, but what specifically does it mean? >> Yeah, so when we talk about telco cloud, first of all it's kind of different from what you're thinking about public cloud today. And there's a couple differences. One, if you look at the big hyperscaler public cloud today, they tend to be centralized in huge data centers. Okay, telco cloud, there are big data centers, but then there's also regional data centers. There are edge data centers, which are your typical like access central offices that have turned data centers, and then now even cell sites are becoming mini data centers. So it's distributed. I mean like you could have like, even in a country like say Germany, you'd have 30,000 soul sites, each one of them being a data center. So it's a very different model. Now the other thing I want to go back to the question of monetization, okay? So how do you do monetization? The only way to do that, is to be able to offer new services, like Charles said. How do you offer new services? You have to have an open ecosystem that's going to be very, very flexible. And if we look at where telcos are coming from today, they tend to be very inflexible 'cause they're all kind of single vendor solutions. And even as we've moved to virtualization, you know, if you look at packet core for instance, a lot of them are these vertical stacks of say a Nokia or Ericson or Huawei where you know, you can't really put any other vendors or any other solutions into that. So basically the idea is this kind of horizontal architecture, right? Where now across, not just my central data centers, but across my edge data centers, which would be traditionally my access COs, as well as my cell sites. I have an open environment. And we're kind of starting with, you know, packet core obviously with, and UPFs being distributed, but now open ran or virtual ran, where I can have CUs and DUs and I can split CUs, they could be at the soul site, they could be in edge data centers. But then moving forward, we're going to have like MEG, which are, you know, which are new kinds of services, you know, could be, you know, remote cars it could be gaming, it could be the Metaverse. And these are going to be a multi-vendor environment. So one of the things you need to do is you need to have you know, this cloud layer, and that's what Charles was talking about with the infrastructure blocks is helping the service providers do that, but they still own their infrastructure. >> Yeah, so it's still not clear to me how the service providers win that game but we can maybe come back to that because I want to dig into TCO a little bit. >> Sure. >> Because I have a lot of friends at Dell. I don't have a lot of friends at HPE. I've always been critical when they take an X86 server put a name on it that implies edge and they throw it over the fence to the edge, that's not going to work, okay? We're now seeing, you know we were just at the Dell booth yesterday, you did the booth crawl, which was awesome. Purpose-built servers for this environment. >> Charles: That's right. >> So there's two factors here that I want to explore in TCO. One is, how those next gen servers compare to the previous gen, especially in terms of power consumption but other factors and then how these sort of open ran, open ecosystem stacks compared to proprietary stacks. Peter, can you help us understand those? >> Yeah, sure. And Charles can comment on this as well. But I mean there, there's a couple areas. One is just moving the next generation. So especially on the Intel side, moving from Ice Lake to the Sapphire Rapids is a big deal, especially when it comes to the DU. And you know, with the radios, right? There's the radio unit, the RU, and then there's the DU the distributed unit, and the CU. The DU is really like part of the radio, but it's virtualized. When we moved from Ice lake to Sapphire Rapids, which is third generation intel to fourth generation intel, we're literally almost doubling the performance in the DU. And that's really important 'cause it means like almost half the number of servers and we're talking like 30, 40, 50,000 servers in some cases. So, you know, being able to divide that by two, that's really big, right? In terms of not only the the cost but all the TCO and the OpEx. Now another area that's really important, when I was talking moving from these vertical silos to the horizontal, the issue with the vertical silos is, you can't place any other workloads into those silos. So it's kind of inefficient, right? Whereas when we have the horizontal architecture, now you can place workloads wherever you want, which basically also means less servers but also more flexibility, more service agility. And then, you know, I think Charles can comment more, specifically on the XR8000, some things Dell's doing, 'cause it's really exciting relative to- >> Sure. >> What's happening in there. >> So, you know, when we start looking at putting compute at the edge, right? We recognize the first thing we have to do is understand the environment we are going into. So we spend with a lot of time with telcos going to the south side, going to the edge data center, looking at operation, how do the engineer today deal with maintenance replacement at those locations? Then based on understanding the operation constraints at those sites, we create innovation and take a traditional server, remodel it to make sure that we minimize the disruption to the operations, right? Just because we are helping them going from appliances to open compute, we do not want to disrupt what is have been a very efficient operation on the remote sites. So we created a lot of new ideas and develop them on general compute, where we believe we can save a lot of headache and disruptions and still provide the same level of availability, resiliency, and redundancy on an open compute platform. >> So when we talk about open, we don't mean generic? Fair? See what I mean? >> Open is more from the software workload perspective, right? A Dell server can run any type of workload that customer intend. >> But it's engineered for this? >> Environment. >> Environment. >> That's correct. >> And so what are some of the environmental issues that are dealt with in the telecom space that are different than the average data center? >> The most basic one, is in most of the traditional cell tower, they are deployed within cabinets instead of racks. So they are depth constraints that you just have no access to the rear of the chassis. So that means on a server, is everything you need to access, need to be in the front, nothing should be in the back. Then you need to consider how labor union come into play, right? There's a lot of constraint on who can go to a cell tower and touch power, who can go there and touch compute, right? So we minimize all that disruption through a modular design and make it very efficient. >> So when we took a look at XR8000, literally right here, sitting on the desk. >> Uh-huh. >> Took it apart, don't panic, just pulled out some sleds and things. >> Right, right. >> One of the interesting demonstrations was how it compared to the size of a shoe. Now apparently you hired someone at Dell specifically because they wear a size 14 shoe, (Charles laughs) so it was even more dramatic. >> That's right. >> But when you see it, and I would suggest that viewers go back and take a look at that segment, specifically on the hardware. You can see exactly what you just referenced. This idea that everything is accessible from the front. Yeah. >> So I want to dig in a couple things. So I want to push back a little bit on what you were saying about the horizontal 'cause there's the benefit, if you've got the horizontal infrastructure, you can run a lot more workloads. But I compare it to the enterprise 'cause I, that was the argument, I've made that argument with converged infrastructure versus say an Oracle vertical stack, but it turned out that actually Oracle ran Oracle better, okay? Is there an analog in telco or is this new open architecture going to be able to not only service the wide range of emerging apps but also be as resilient as the proprietary infrastructure? >> Yeah and you know, before I answer that, I also want to say that we've been writing a number of white papers. So we have actually three white papers we've just done with Dell looking at infrastructure blocks and looking at vertical versus horizontal and also looking at moving from the previous generation hardware to the next generation hardware. So all those details, you can find the white papers, and you can find them either in the Dell website or at the ACG research website >> ACGresearch.com? >> ACG research. Yeah, if you just search ACG research, you'll find- >> Yeah. >> Lots of white papers on TCO. So you know, what I want to say, relative to the vertical versus horizontal. Yeah, obviously in the vertical side, some of those things will run well, I mean it won't have issues. However, that being said, as we move to cloud native, you know, it's very high performance, okay? In terms of the stack, whether it be a Red Hat or a VMware or other cloud layers, that's really become much more mature. It now it's all CNF base, which is really containerized, very high performance. And so I don't think really performance is an issue. However, my feeling is that, if you want to offer new services and generate new revenue, you're not going to do it in vertical stacks, period. You're going to be able to do a packet core, you'll be able to do a ran over here. But now what if I want to offer a gaming service? What if I want to do metaverse? What if I want to do, you have to have an environment that's a multi-vendor environment that supports an ecosystem. Even in the RAN, when we look at the RIC, and the xApps and the rApps, these are multi-vendor environments that's going to create a lot of flexibility and you can't do that if you're restricted to, I can only have one vendor running on this hardware. >> Yeah, we're seeing these vendors work together and create RICs. That's obviously a key point, but what I'm hearing is that there may be trade offs, but the incremental value is going to overwhelm that. Second question I have, Peter is, TCO, I've been hearing a lot about 30%, you know, where's that 30% come from? Is it Op, is it from an OpEx standpoint? Is it labor, is it power? Is it, you mentioned, you know, cutting the number of servers in half. If I can unpack the granularity of that TCO, where's the benefit coming from? >> Yeah, the answer is yes. (Peter and Charles laugh) >> Okay, we'll do. >> Yeah, so- >> One side that, in terms of, where is the big bang for the bucks? >> So I mean, so you really need to look at the white paper to see details, but definitely power, definitely labor, definitely reducing the number of servers, you know, reducing the CapEx. The other thing is, is as you move to this really next generation horizontal telco cloud, there's the whole automation and orchestration, that is a key component as well. And it's enabled by what Dell is doing. It's enabled by the, because the thing is you're not going to have end-to-end automation if you have all this legacy stuff there or if you have these vertical stacks where you can't integrate. I mean you can automate that part and then you have separate automation here, you separate. you need to have integrated automation and orchestration across the whole thing. >> One other point I would add also, right, on the hardware perspective, right? With the customized hardware, what we allow operator to do is, take out the existing appliance and push a edge optimized server without reworking the entire infrastructure. There is a significant saving where you don't have to rethink about what is my power infrastructure, right? What is my security infrastructure? The server is designed to leverage the existing, what is already there. >> How should telco, Charles, plan for this transformation? Are there specific best practices that you would recommend in terms of the operational model? >> Great question. I think first thing is do an inventory of what you have. Understand what your constraints are and then come to Dell, we will love to consult with you, based on our experience on the best practices. We know how to minimize additional changes. We know how to help your support engineer, understand how to shift appliance based operation to a cloud-based operation. >> Is that a service you offer? Is that a pre-sales freebie? What is maybe both? >> It's both. >> Yeah. >> It's both. >> Yeah. >> Guys- >> Just really quickly. >> We're going to wrap. >> The, yeah. Dave loves the TCO discussion. I'm always thinking in terms of, well how do you measure TCO when you're comparing something where you can't do something to an environment where you're going to be able to do something new? And I know that that's always the challenge in any kind of emerging market where things are changing, any? >> Well, I mean we also look at, not only TCO, but we look at overall business case. So there's basically service at GLD and revenue and then there's faster time to revenues. Well, and actually ACG, we actually have a platform called the BAE or Business Analytics Engine that's a very sophisticated simulation cloud-based platform, where we can actually look at revenue month by month. And we look at what's the impact of accelerating revenue by three months. By four months. >> So you're looking into- >> By six months- >> So you're forward looking. You're just not consistently- >> So we're not just looking at TCO, we're looking at the overall business case benefit. >> Yeah, exactly right. There's the TCO, which is the hard dollars. >> Right. >> CFO wants to see that, he or she needs to see that. But you got to, you can convince that individual, that there's a business case around it. >> Peter: Yeah. >> And then you're going to sign up for that number. >> Peter: Yeah. >> And they're going to be held to it. That's the story the world wants. >> At the end of the day, telcos have to be offered new services 'cause look at all the money that's been spent. >> Dave: Yeah, that's right. >> On investment on 5G and everything else. >> 0.5 trillion over the next seven years. All right, guys, we got to go. Sorry to cut you off. >> Okay, thank you very much. >> But we're wall to wall here. All right, thanks so much for coming on. >> Dave: Fantastic. >> All right, Dave Vellante, for Dave Nicholson. Lisa Martin's in the house. John Furrier in Palo Alto Studios. Keep it right there. MWC 23 live from the Fira in Barcelona. (light airy music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR, this is Breaking Analysis with Dave Vellante. >> While never really meant to be a consumer tech event, the rapid ascendancy of smartphones sucked much of the air out of Mobile World Congress over the years, now MWC. And while the device manufacturers continue to have a major presence at the show, the maturity of intelligent devices, longer life cycles, and the disaggregation of the network stack, have put enterprise technologies front and center in the telco business. Semiconductor manufacturers, network equipment players, infrastructure companies, cloud vendors, software providers, and a spate of startups are eyeing the trillion dollar plus communications industry as one of the next big things to watch this decade. Hello, and welcome to this week's Wikibon CUBE Insights, powered by ETR. In this Breaking Analysis, we bring you part two of our ongoing coverage of MWC '23, with some new data on enterprise players specifically in large telco environments, a brief glimpse at some of the pre-announcement news and corresponding themes ahead of MWC, and some of the key announcement areas we'll be watching at the show on theCUBE. Now, last week we shared some ETR data that showed how traditional enterprise tech players were performing, specifically within the telecoms vertical. Here's a new look at that data from ETR, which isolates the same companies, but cuts the data for what ETR calls large telco. The N in this cut is 196, down from 288 last week when we included all company sizes in the dataset. Now remember the two dimensions here, on the y-axis is net score, or spending momentum, and on the x-axis is pervasiveness in the data set. The table insert in the upper left informs how the dots and companies are plotted, and that red dotted line, the horizontal line at 40%, that indicates a highly elevated net score. Now while the data are not dramatically different in terms of relative positioning, there are a couple of changes at the margin. So just going down the list and focusing on net score. Azure is comparable, but slightly lower in this sector in the large telco than it was overall. Google Cloud comes in at number two, and basically swapped places with AWS, which drops slightly in the large telco relative to overall telco. Snowflake is also slightly down by one percentage point, but maintains its position. Remember Snowflake, overall, its net score is much, much higher when measuring across all verticals. Snowflake comes down in telco, and relative to overall, a little bit down in large telco, but it's making some moves to attack this market that we'll talk about in a moment. Next are Red Hat OpenStack and Databricks. About the same in large tech telco as they were an overall telco. Then there's Dell next that has a big presence at MWC and is getting serious about driving 16G adoption, and new servers, and edge servers, and other partnerships. Cisco and Red Hat OpenShift basically swapped spots when moving from all telco to large telco, as Cisco drops and Red Hat bumps up a bit. And VMware dropped about four percentage points in large telco. Accenture moved up dramatically, about nine percentage points in big telco, large telco relative to all telco. HPE dropped a couple of percentage points. Oracle stayed about the same. And IBM surprisingly dropped by about five points. So look, I understand not a ton of change in terms of spending momentum in the large sector versus telco overall, but some deltas. The bottom line for enterprise players is one, they're just getting started in this new disruption journey that they're on as the stack disaggregates. Two, all these players have experience in delivering horizontal solutions, but now working with partners and identifying big problems to be solved, and three, many of these companies are generally not the fastest moving firms relative to smaller disruptive disruptors. Now, cloud has been an exception in fairness. But the good news for the legacy infrastructure and IT companies is that the telco transformation and the 5G buildout is going to take years. So it's moving at a pace that is very favorable to many of these companies. Okay, so looking at just some of the pre-announcement highlights that have hit the wire this week, I want to give you a glimpse of the diversity of innovation that is occurring in the telecommunication space. You got semiconductor manufacturers, device makers, network equipment players, carriers, cloud vendors, enterprise tech companies, software companies, startups. Now we've included, you'll see in this list, we've included OpeRAN, that logo, because there's so much buzz around the topic and we're going to come back to that. But suffice it to say, there's no way we can cover all the announcements from the 2000 plus exhibitors at the show. So we're going to cherry pick here and make a few call outs. Hewlett Packard Enterprise announced an acquisition of an Italian private cellular network company called AthoNet. Zeus Kerravala wrote about it on SiliconANGLE if you want more details. Now interestingly, HPE has a partnership with Solana, which also does private 5G. But according to Zeus, Solona is more of an out-of-the-box solution, whereas AthoNet is designed for the core and requires more integration. And as you'll see in a moment, there's going to be a lot of talk at the show about private network. There's going to be a lot of news there from other competitors, and we're going to be watching that closely. And while many are concerned about the P5G, private 5G, encroaching on wifi, Kerravala doesn't see it that way. Rather, he feels that these private networks are really designed for more industrial, and you know mission critical environments, like factories, and warehouses that are run by robots, et cetera. 'Cause these can justify the increased expense of private networks. Whereas wifi remains a very low cost and flexible option for, you know, whatever offices and homes. Now, over to Dell. Dell announced its intent to go hard after opening up the telco network with the announcement that in the second half of this year it's going to begin shipping its infrastructure blocks for Red Hat. Remember it's like kind of the converged infrastructure for telco with a more open ecosystem and sort of more flexible, you know, more mature engineered system. Dell has also announced a range of PowerEdge servers for a variety of use cases. A big wide line bringing forth its 16G portfolio and aiming squarely at the telco space. Dell also announced, here we go, a private wireless offering with airspan, and Expedo, and a solution with AthoNet, the company HPE announced it was purchasing. So I guess Dell and HPE are now partnering up in the private wireless space, and yes, hell is freezing over folks. We'll see where that relationship goes in the mid- to long-term. Dell also announced new lab and certification capabilities, which we said last week was going to be critical for the further adoption of open ecosystem technology. So props to Dell for, you know, putting real emphasis and investment in that. AWS also made a number of announcements in this space including private wireless solutions and associated managed services. AWS named Deutsche Telekom, Orange, T-Mobile, Telefonica, and some others as partners. And AWS announced the stepped up partnership, specifically with T-Mobile, to bring AWS services to T-Mobile's network portfolio. Snowflake, back to Snowflake, announced its telecom data cloud. Remember we showed the data earlier, it's Snowflake not as strong in the telco sector, but they're continuing to move toward this go-to market alignment within key industries, realigning their go-to market by vertical. It also announced that AT&T, and a number of other partners, are collaborating to break down data silos specifically in telco. Look, essentially, this is Snowflake taking its core value prop to the telco vertical and forming key partnerships that resonate in the space. So think simplification, breaking down silos, data sharing, eventually data monetization. Samsung previewed its future capability to allow smartphones to access satellite services, something Apple has previously done. AMD, Intel, Marvell, Qualcomm, are all in the act, all the semiconductor players. Qualcomm for example, announced along with Telefonica, and Erickson, a 5G millimeter network that will be showcased in Spain at the event this coming week using Qualcomm Snapdragon chipset platform, based on none other than Arm technology. Of course, Arm we said is going to dominate the edge, and is is clearly doing so. It's got the volume advantage over, you know, traditional Intel, you know, X86 architectures. And it's no surprise that Microsoft is touting its open AI relationship. You're going to hear a lot of AI talk at this conference as is AI is now, you know, is the now topic. All right, we could go on and on and on. There's just so much going on at Mobile World Congress or MWC, that we just wanted to give you a glimpse of some of the highlights that we've been watching. Which brings us to the key topics and issues that we'll be exploring at MWC next week. We touched on some of this last week. A big topic of conversation will of course be, you know, 5G. Is it ever going to become real? Is it, is anybody ever going to make money at 5G? There's so much excitement around and anticipation around 5G. It has not lived up to the hype, but that's because the rollout, as we've previous reported, is going to take years. And part of that rollout is going to rely on the disaggregation of the hardened telco stack, as we reported last week and in previous Breaking Analysis episodes. OpenRAN is a big component of that evolution. You know, as our RAN intelligent controllers, RICs, which essentially the brain of OpenRAN, if you will. Now as we build out 5G networks at massive scale and accommodate unprecedented volumes of data and apply compute-hungry AI to all this data, the issue of energy efficiency is going to be front and center. It has to be. Not only is it a, you know, hot political issue, the reality is that improving power efficiency is compulsory or the whole vision of telco's future is going to come crashing down. So chip manufacturers, equipment makers, cloud providers, everybody is going to be doubling down and clicking on this topic. Let's talk about AI. AI as we said, it is the hot topic right now, but it is happening not only in consumer, with things like ChatGPT. And think about the theme of this Breaking Analysis in the enterprise, AI in the enterprise cannot be ChatGPT. It cannot be error prone the way ChatGPT is. It has to be clean, reliable, governed, accurate. It's got to be ethical. It's got to be trusted. Okay, we're going to have Zeus Kerravala on the show next week and definitely want to get his take on private networks and how they're going to impact wifi. You know, will private networks cannibalize wifi? If not, why not? He wrote about this again on SiliconANGLE if you want more details, and we're going to unpack that on theCUBE this week. And finally, as always we'll be following the data flows to understand where and how telcos, cloud players, startups, software companies, disruptors, legacy companies, end customers, how are they going to make money from new data opportunities? 'Cause we often say in theCUBE, don't ever bet against data. All right, that's a wrap for today. Remember theCUBE is going to be on location at MWC 2023 next week. We got a great set. We're in the walkway in between halls four and five, right in Congress Square, stand CS-60. Look for us, we got a full schedule. If you got a great story or you have news, stop by. We're going to try to get you on the program. I'll be there with Lisa Martin, co-hosting, David Nicholson as well, and the entire CUBE crew, so don't forget to come by and see us. I want to thank Alex Myerson, who's on production and manages the podcast, and Ken Schiffman, as well, in our Boston studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our editor-in-chief over at SiliconANGLE.com. He does some great editing. Thank you. All right, remember all these episodes they are available as podcasts wherever you listen. All you got to do is search Breaking Analysis podcasts. I publish each week on Wikibon.com and SiliconANGLE.com. All the video content is available on demand at theCUBE.net, or you can email me directly if you want to get in touch David.Vellante@SiliconANGLE.com or DM me @DVellante, or comment on our LinkedIn posts. And please do check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. We'll see you next week at Mobile World Congress '23, MWC '23, or next time on Breaking Analysis. (bright music)

>> Hello and welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE. Got a variety of interviews across the gamut from topics, women in tech, mentoring, pipelining, developers, open source, executives. Stanford's having International Women's Day celebration with the women in data science, which we're streaming that live as well. Variety of programs. In this segment, Meagen Eisenberg, friend of theCUBE, she's the CMO of Laceworks, is an amazing executive, got a great journey story as a CMO but she's also actively advising startups, companies and really pays it forward. I want to say Meagen, thank you for coming on the program and thanks for sharing. >> Yeah, thank you for having me. I'm happy to be here. >> Well, we're going to get into some of the journey celebrations that you've gone through and best practice what you've learned is pay that forward. But I got to say, one of the things that really impresses me about you as an executive is you get stuff done. You're a great CMO but also you're advised a lot of companies, you have a lot of irons in the fires and you're advising companies and sometimes they're really small startups to bigger companies, and you're paying it forward, which I love. That's kind of the spirit of this day. >> Yeah, I mean, I agree with you. When I think about my career, a lot of it was looking to mentors women out in the field. This morning I was at a breakfast by Eileen and we had the CEO of General Motors on, and she was talking about her journey nine years as a CEO. And you know, and she's paying it forward with us. But I think about, you know, when you're advising startups, you know, I've gathered knowledge and pattern recognition and to be able to share that is, you know, I enjoy it. >> Yeah. And the startups are also fun too, but it's not always easy and it can get kind of messy as you know. Some startups don't make it some succeed and it's always like the origination story is kind of rewritten and then that's that messy middle. And then it's like that arrows that don't look like a straight line but everyone thinks it's great and you know, it's not for the faint of heart. And Teresa Carlson, who I've interviewed many times, former Amazon, now she's the president of Flexport, she always says, sometimes startups on certain industries aren't for the faint of heart so you got to have a little bit of metal, right? You got to be tough. And some cases that you don't need that, but startups, it's not always easy. What have you learned? >> Yeah, I mean, certainly in the startup world, grit, creativity. You know, when I was at TripActions travel company, pandemic hits, nobody's traveling. You cut budget, you cut heads, but you focus on the core, right? You focus on what you need to survive. And creativity, I think, wins. And, you know, as a CMO when you're marketing, how do you get through that noise? Even the security space, Lacework, it's a fragmented market. You've got to be differentiated and position yourself and you know, be talking to the right target audience and customers. >> Talk about your journey over the years. What have you learned? What's some observations? Can you share any stories and best practices that someone watching could learn from? I know there's a lot of people coming into the tech space with the generative AI things going on in Cloud computing, scaling to the edge, there's a lot more aperture for technical jobs as well as just new roles and new roles that haven't, you really don't go to college for anymore. You got cybersecurity you're in. What are some of the things that you've done over your career if you can share and some best practices? >> Yeah, I think number one, continual learning. When I look through my career, I was constantly reading, networking. Part of the journey is who you're meeting along the way. As you become more senior, your ability to hire and bring in talent matters a lot. I'm always trying to meet with new people. Yeah, if I look at my Amazon feed of books I've bought, right, it kind of chronicle of my history of things I was learning about. Right now I'm reading a lot about cybersecurity, how the, you know, how how they tell me the world ends is the one I'm reading most recently. But you've got to come up to speed and then know the product, get in there and talk to customers. Certainly on the marketing front, anytime I can talk with the customer and find out how they're using us, why they love us, that, you know, helps me better position and differentiate our company. >> By the way, that book is amazing. I saw Nicole speak on Tuesday night with John Markoff and Palo Alto here. What a great story she told there. I recommend that book to everyone. It goes in and she did eight years of research into that book around zero day marketplaces to all the actors involved in security. And it was very interesting. >> Yeah, I mean, it definitely wakes you up, makes you think about what's going on in the world. Very relevant. >> It's like, yeah, it was happening all the time, wasn't it. All the hacking. But this brings me, this brings up an interesting point though, because you're in a cybersecurity area, which by the way, it's changing very fast. It's becoming a bigger industry. It's not just male dominated, although it is now, it's still male dominated, but it's becoming much more and then just tech. >> Yeah, I mean it's a constantly evolving threat landscape and we're learning, and I think more than ever you need to be able to use the data that companies have and, you know, learn from it. That's one of the ways we position ourselves. We're not just about writing rules that won't help you with those zero day attacks. You've got to be able to understand your particular environment and at any moment if it changes. And that's how we help you detect a threat. >> How is, how are things going with you? Is there any new things you guys got going on? Initiatives or programs for women in tech and increasing the range of diversity inclusion in the industry? Because again, this industry's getting much wider too. It's not just specialized, it's also growing. >> Yes, actually I'm excited. We're launching secured by women, securedbywomen.com and it's very much focused on women in the industry, which some studies are showing it's about 25% of security professionals are women. And we're going to be taking nominations and sponsoring women to go to upcoming security events. And so excited to launch that this month and really celebrate women in security and help them, you know, part of that continual learning that I talked about, making sure they're there learning, having the conversations at the conferences, being able to network. >> I have to ask you, what inspired you to pursue the career in tech? What was the motivation? >> You know, if I think way back, originally I wanted to be on the art side and my dad said, "You can do anything as long as it's in the sciences." And so in undergrad I did computer science and MIS. Graduated with MIS and computer science minor. And when I came out I was a IT engineer at Cisco and you know, that kind of started my journey and decided to go back and get my MBA. And during that process I fell in love with marketing and I thought, okay, I understand the buyer, I can come out and market technology to the IT world and developers. And then from there went to several tech companies. >> I mean my father was an engineer. He had the same kind of thing. You got to be an engineer, it's a steady, stable job. But that time, computer science, I mean we've seen the evolution of computer science now it's the most popular degree at Berkeley we've heard and around the world and the education formats are changing. You're seeing a lot of people's self-training on YouTube. The field has really changed. What are some of the challenges you see for folks trying to get into the industry and how would you advise today if you were talking to your young self, what would you, what would be the narrative? >> Yeah, I mean my drawback then was HTML pages were coming out and I thought it would be fun to design, you know, webpages. So you find something you're passionate about in the space today, whether it's gaming or it's cybersecurity. Go and be excited about it and apply and don't give up, right? Do whatever you can to read and learn. And you're right, there are a ton of online self-help. I always try to hire women and people who are continual learners and are teaching themselves something. And I try to find that in an interview to know that they, because when you come to a business, you're there to solve problems and challenges. And the folks that can do that and be innovative and learn, those are the ones I want on my team. >> It's interesting, you know, technology is now impacting society and we need everyone involved to participate and give requirements. And that kind of leads my next question for you is, like, in your opinion, or let me just step back, let me rephrase. What are some of the things that you see technology being used for, for society right now that will impact people's lives? Because this is not a gender thing. We need everybody involved 'cause society is now digital. Technology's pervasive. The AI trends now we're seeing is clearly unmasking to the mainstream that there's some cool stuff happening. >> Yeah, I mean, I think ChatGPT, think about that. All the different ways we're using it we're writing content and marketing with it. We're, you know, I just read an article yesterday, folks are using it to write children's stories and then selling those stories on Amazon, right? And the amount that they can produce with it. But if you think about it, there's unlimited uses with that technology and you've got all the major players getting involved on it. That one major launch and piece of technology is going to transform us in the next six months to a year. And it's the ability to process so much data and then turn that into just assets that we use and the creativity that's building on top of it. Even TripActions has incorporated ChatGPT into your ability to figure out where you want when you're traveling, what's happening in that city. So it's just, you're going to see that incorporated everywhere. >> I mean we've done an interview before TripAction, your other company you were at. Interesting point you don't have to type in a box to say, I'm traveling, I want a hotel. You can just say, I'm going to Barcelona for Mobile World Congress, I want to have a good time. I want some tapas and a nice dinner out. >> Yes. Yeah. That easy. We're making it easy. >> It's efficiency. >> And actually I was going to say for women specifically, I think the reason why we can do so much today is all the technology and apps that we have. I think about DoorDash, I think about Waze you know, when I was younger you had to print out instructions. Now I get in the car real quick, I need to go to soccer practice, I enter it, I need to pick them up at someone's house. I enter it. It's everything's real time. And so it takes away all the things that I don't add value to and allows me to focus on what I want in business. And so there's a bunch of, you know, apps out there that have allowed me to be so much more efficient and productive that my mother didn't have for sure when I was growing up. >> That is an amazing, I think that actually illustrates, in my opinion, the best example of ChatGPT because the maps and GPS integration were two techs, technologies merged together that replace driving and looking at the map. You know, like how do you do that? Like now it's automatically. This is what's going to happen to creative, to writing, to ideation. I even heard Nicole from her book read said that they're using ChatGPT to write zero day exploits. So you seeing it... >> That's scary stuff. You're right. >> You're seeing it everywhere. Super exciting. Well, I got to ask you before you get into some of the Lacework things that you're involved with, cause I think you're doing great work over there is, what was the most exciting projects you've worked on in your career? You came in Cisco, very technical company, so got the technical chops, CSMIS which stands for Management of Information Science for all the young people out there, that was the state of the art back then. What are some of the exciting things you've done? >> Yeah, I mean, I think about, I think about MongoDB and learning to market to developers. Taking the company public in 2017. Launching Atlas database as a service. Now there's so much more of that, you know, the PLG motion, going to TripActions, you know, surviving a pandemic, still being able to come out of that and all the learnings that went with it. You know, they recently, I guess rebranded, so they're Navan now. And then now back in the security space, you know, 14 years ago I was at ArcSite and we were bought by HP. And so getting back into the security world is exciting and it's transformed a ton as you know, it's way more complicated than it was. And so just understanding the pain of our customers and how we protect them as is fun. And I like, you know, being there from a marketing standpoint. >> Well we really appreciate you coming on and sharing that. I got to ask you, for folks watching they might be interested in some advice that you might have for them and their career in tech. I know a lot of young people love the tech. It's becoming pervasive in our lives, as we mentioned. What advice would you give for folks watching that want to start a career in tech? >> Yeah, so work hard, right? Study, network, your first job, be the best at it because every job after that you get pulled into a network. And every time I move, I'm hiring people from the last job, two jobs before, three jobs before. And I'm looking for people that are working hard, care, you know, are continual learners and you know, add value. What can you do to solve problems at your work and add value? >> What's your secret networking hack or growth hack or tip that you can share? Because you're a great networker by the way. You're amazing and you do add a lot of value. I've seen you in action. >> Well, I try never to eat alone. I've got breakfast, I've got lunch, I've got coffee breaks and dinner. And so when I'm at work, I try and always sit and eat with a team member, new group. If I'm out on the road, I'm, you know, meeting people for lunch, going for dinner, just, you know, don't sit at your desk by yourself and don't sit in the hotel room. Get out and meet with people. >> What do you think about now that we're out of the pandemic or somewhat out of the pandemic so to speak, events are back. >> Yes. >> RSA is coming up. It's a big event. The bigger events are getting bigger and then the other events are kind of smaller being distributed. What's your vision of how events are evolving? >> Yeah, I mean, you've got to be in person. Those are the relationships. Right now more than ever people care about renewals and you are building that rapport. And if you're not meeting with your customers, your competitors are. So what I would say is get out there Lacework, we're going to be at RSA, we're going to be at re:Inforce, we're going to be at all of these events, building relationships, you know, coffee, lunch, and yeah, I think the future of events are here to stay and those that don't embrace in person are going to give up business. They're going to lose market share to us. >> And networking is obviously very key on events as well. >> Yes. >> A good opportunity as always get out to the events. What's the event networking trick or advice do you give folks that are going to get out to the networking world? >> Yeah, schedule ahead of time. Don't go to an event and expect people just to come by for great swag. You should be partnering with your sales team and scheduling ahead of time, getting on people's calendars. Don't go there without having 100 or 200 meetings already booked. >> Got it. All right. Let's talk about you, your career. You're currently at Lacework. It's a very hot company in a hot field, security, very male dominated, you're a leader there. What's it like? What's the strategies? How does a woman get in there and be successful? What are some tricks, observations, any data you can share? What's the best practice? What's the secret sauce from Meagen Eisenberg? >> Yes. Yeah, for Meagen Eisenberg. For Lacework, you know, we're focused on our customers. There's nothing better than getting, being close to them, solving their pain, showcasing them. So if you want to go into security, focus on their, the issues and their problems and make sure they're aware of what you're delivering. I mean, we're focused on cloud security and we go from build time to run time. And that's the draw for me here is we had a lot of, you know, happy, excited customers by what we were doing. And what we're doing is very different from legacy security providers. And it is tapping into the trend of really understanding how much data you have and what's happening in the data to detect the anomalies and the threats that are there. >> You know, one of the conversations that I was just having with a senior leader, she was amazing and I asked her what she thought of the current landscape, the job market, the how to get promoted through the careers, all those things. And the response was interesting. I want to get your reaction. She said interdisciplinary skills are critical. And now more than ever, the having that, having a set of skills, technical and social and emotional are super valuable. Do you agree? What's your reaction to that and what would, how would you reframe that? >> Yeah, I mean, I completely agree. You can't be a leader without balance. You've got to know your craft because you're developing and training your team, but you also need to know the, you know, how to build relationships. You're not going to be successful as a C-level exec if you're not partnering across the functions. As a CMO I need to partner with product, I need to partner with the head of sales, I need to partner with finance. So those relationships matter a ton. I also need to attract the right talent. I want to have solid people on the team. And what I will say in the security, cybersecurity space, there's a talent shortage and you cannot hire enough people to protect your company in that space. And that's kind of our part of it is we reduce the number of alerts that you're getting. So you don't need hundreds of people to detect an issue. You're using technology to show, you know, to highlight the issue and then your team can focus on those alerts that matter. >> Yeah, there's a lot of emerging markets where leveling up and you don't need pedigree. You can just level up skill-wise pretty quickly. Which brings me to the next question for you is how do you keep up with all the tech day-to-day and how should someone watching stay on top of it? Because I mean, you got to be on top of this stuff and you got to ride the wave. It's pretty turbulent, but it's still growing and changing. >> Yeah, it's true. I mean, there's a lot of reading. I'm watching the news. Anytime something comes out, you know, ChatGPT I'm playing with it. I've got a great network and sharing. I'm on, you know, LinkedIn reading articles all the time. I have a team, right? Every time I hire someone, they bring new information and knowledge in and I'm you know, Cal Poly had this learn by doing that was the philosophy at San Luis Obispo. So do it. Try it, don't be afraid of it. I think that's the advice. >> Well, I love some of the points you mentioned community and network. You mentioned networking. That brings up the community question, how could people get involved? What communities are out there? How should they approach communities? 'Cause communities are also networks, but also they're welcoming people in that form networks. So it's a network of networks. So what's your take on how to engage and work with communities? How do you find your tribe? If someone's getting into the business, they want support, they might want technology learnings, what's your approach? >> Yeah, so a few, a few different places. One, I'm part of the operator collective, which is a strong female investment group that's open and works a lot with operators and they're in on the newest technologies 'cause they're investing in it. Chief I think is a great organization as well. You've got a lot of, if you're in marketing, there's a ton of CMO networking events that you can go to. I would say any field, even for us at Lacework, we've got some strong CISO networks and we do dinners around you know, we have one coming up in the Bay area, in Boston, New York, and you can come and meet other CISOs and security leaders. So when I get an invite and you know we all do, I will go to it. I'll carve out the time and meet with others. So I think, you know, part of the community is get out there and, you know, join some of these different groups. >> Meagen, thank you so much for spending the time. Final question for you. How do you see the future of tech evolving and how do you see your role in it? >> Yeah, I mean, marketing's changing wildly. There's so many different channels. You think about all the social media channels that have changed over the last five years. So when I think about the future of tech, I'm looking at apps on my phone. I have three daughters, 13, 11, and 8. I'm telling you, they come to me with new apps and new technology all the time, and I'm paying attention what they're, you know, what they're participating in and what they want to be a part of. And certainly it's going to be a lot more around the data and AI. I think we're only at the beginning of that. So we will continue to, you know, learn from it and wield it and deal with the mass amount of data that's out there. >> Well, you saw TikTok just got banned by the European Commission today around their staff. Interesting times. >> It is. >> Meagen, thank you so much as always. You're a great tech athlete. Been following your career for a while, a long time. You're an amazing leader. Thank you for sharing your story here on theCUBE, celebration of International Women's Day. Every day is IWD and thanks for coming on. >> Thank you for having me. >> Okay. I'm John Furrier here in theCUBE Studios in Palo Alto. Thank you for watching, more to come stay with us. (bright 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)

(gentle music) >> Hello, everyone. Welcome to this Cube Conversation. I'm John Furrier, host of theCUBE here, in our Palo Alto Studios. We're featuring OctoML. I'm with the CEO, Luis Ceze. Chief Executive Officer, Co-founder of OctoML. I'm John Furrier of theCUBE. Thanks for joining us today. Luis, great to see you. Last time we spoke was at "re:MARS" Amazon's event. Kind of a joint event between (indistinct) and Amazon, kind of put a lot together. Great to see you. >> Great to see you again, John. I really have good memories of that interview. You know, that was definitely a great time. Great to chat with you again. >> The world of ML and AI, machine learning and AI is really hot. Everyone's talking about it. It's really great to see that advance. So I'm looking forward to this conversation but before we get started, introduce who you are in OctoML. >> Sure. I'm Luis Ceze, Co-founder and CEO at OctoML. I'm also professor of Computer Science at University of Washington. You know, OctoML grew out of our efforts on the Apache CVM project, which is a compiler in runtime system that enables folks to run machine learning models in a broad set of harder in the Edge and in the Cloud very efficiently. You know, we grew that project and grew that community, definitely saw there was something to pain point there. And then we built OctoML, OctoML is about three and a half years old now. And the mission, the company is to enable customers to deploy models very efficiently in the Cloud. And make them, you know, run. Do it quickly, run fast, and run at a low cost, which is something that's especially timely right now. >> I like to point out also for the folks 'casue they should know that you're also a professor in the Computer Science department at University of Washington. A great program there. This is a really an inflection point with AI machine learning. The computer science industry has been waiting for decades to advance AI with all this new cloud computing, all the hardware and silicon advancements, GPUs. This is the perfect storm. And you know, this the computer science now we we're seeing an acceleration. Can you share your view, and you're obviously a professor in that department but also, an entrepreneur. This is a great time for computer science. Explain why. >> Absolutely, yeah, no. Just like the confluence of you know, advances in what, you know, computers can do as devices to computer information. Plus, you know, advances in AI that enable applications that you know, we thought it was highly futuristic and now it's just right there today. You know, AI that can generate photo realistic images from descriptions, you know, can write text that's pretty good. Can help augment, you know, human creativity in a really meaningful way. So you see the confluence of capabilities and the creativity of humankind into new applications is just extremely exciting, both from a researcher point of view as well as an entrepreneur point of view, right. >> What should people know about these large language models we're seeing with ChatGPT and how Google has got a lot of work going on that air. There's been a lot of work recently. What's different now about these models, and why are they so popular and effective now? What's the difference between now, and say five years ago, that makes it more- >> Oh, yeah. It's a huge inflection on their capabilities, I always say like emergent behavior, right? So as these models got more complex and our ability to train and deploy them, you know, got to this point... You know, they really crossed a threshold into doing things that are truly surprising, right? In terms of generating, you know, exhalation for things generating tax, summarizing tax, expending tax. And you know, exhibiting what to some may look like reasoning. They're not quite reasoning fundamentally. They're generating tax that looks like they're reasoning, but they do it so well, that it feels like was done by a human, right. So I would say that the biggest changes that, you know, now, they can actually do things that are extremely useful for business in people's lives today. And that wasn't the case five years ago. So that's in the model capabilities and that is being paired with huge advances in computing that enabled this to be... Enables this to be, you know, actually see line of sites to be deployed at scale, right. And that's where we come in, by the way, but yeah. >> Yeah, I want to get into that. And also, you know, the fusion of data integrating data sets at scales. Another one we're seeing a lot of happening now. It's not just some, you know, siloed, pre-built data modeling. It's a lot of agility and a lot of new integration capabilities of data. How is that impacting the dynamics? >> Yeah, absolutely. So I'll say that the ability to either take the data that has that exists in training a model to do something useful with it, and more interestingly I would say, using baseline foundational models and with a little bit of data, turn them into something that can do a specialized task really, really well. Created this really fast proliferation of really impactful applications, right? >> If every company now is looking at this trend and I'm seeing a lot... And I think every company will rebuild their business with machine learning. If they're not already doing it. And the folks that aren't will probably be dinosaurs will be out of business. This is a real business transformation moment where machine learning and AI, as it goes mainstream. I think it's just the beginning. This is where you guys come in, and you guys are poised for handling this frenzy to change business with machine learning models. How do you guys help customers as they look at this, you know, transition to get, you know, concept to production with machine learning? >> Great. Great questions, yeah, so I would say that it's fair to say there's a bunch of models out there that can do useful things right off the box, right? So and also, the ability to create models improved quite a bit. So the challenge now shifted to customers, you know. Everyone is looking to incorporating AI into their applications. So what we do for them is to, first of all, how do you do that quickly, without needing highly specialized, difficult to find engineering? And very importantly, how do you do that at cost that's accessible, right? So all of these fantastic models that we just talked about, they use an amount of computing that's just astronomical compared to anything else we've done in the past. It means the costs that come with it, are also very, very high. So it's important to enable customers to, you know, incorporate AI into their applications, to their use cases in a way that they can do, with the people that they have, and the costs that they can afford, such that they can have, you know, the maximum impacting possibly have. And finally, you know, helping them deal with hardware availability, as you know, even though we made a lot of progress in making computing cheaper and cheaper. Even to this day, you know, you can never get enough. And getting an allocation, getting the right hardware to run these incredibly hungry models is hard. And we help customers deal with, you know, harder availability as well. >> Yeah, for the folks watching as a... If you search YouTube, there's an interview we did last year at "re:MARS," I mentioned that earlier, just a great interview. You talked about this hardware independence, this traction. I want to get into that, because if you look at all the foundation models that are out there right now, that are getting traction, you're seeing two trends. You're seeing proprietary and open source. And obviously, open source always wins in my opinion, but, you know, there's this iPhone moment and android moment that one of your investors John Torrey from Madrona, talked about was is iPhone versus Android moment, you know, one's proprietary hardware and they're very specialized high performance and then open source. This is an important distinction and you guys are hardware independent. What's the... Explain what all this means. >> Yeah. Great set of questions. First of all, yeah. So, you know, OpenAI, and of course, they create ChatGPT and they offer an API to run these models that does amazing things. But customers have to be able to go and send their data over to OpenAI, right? So, and run the model there and get the outputs. Now, there's open source models that can do amazing things as well, right? So they typically open source models, so they don't lag behind, you know, these proprietary closed models by more than say, you know, six months or so, let's say. And it means that enabling customers to take the models that they want and deploy under their control is something that's very valuable, because one, you don't have to expose your data to externally. Two, you can customize the model even more to the things that you wanted to do. And then three, you can run on an infrastructure that can be much more cost effective than having to, you know, pay somebody else's, you know, cost and markup, right? So, and where we help them is essentially help customers, enable customers to take machine learning models, say an open source model, and automate the process of putting them into production, optimize them to run with the right performance, and more importantly, give them the independence to run where they need to run, where they can run best, right? >> Yeah, and also, you know, I point out all the time that, you know, there's never any stopping the innovation of hardware silicon. You're seeing cloud computing more coming in there. So, you know, being hardware independent has some advantages. And if you look at OpenAI, for instance, you mentioned ChatGPT, I think this is interesting because I think everyone is scratching their head, going, "Okay, I need to move to this new generation." What's your pro tip and advice for folks who want to move to, or businesses that want to say move to machine learning? How do they get started? What are some of the considerations they need to think about to deploy these models into production? >> Yeah, great though. Great set of questions. First of all, I mean, I'm sure they're very aware of the kind of things that you want to do with AI, right? So you could be interacting with customers, you know, automating, interacting with customers. It could be, you know, finding issues in production lines. It could be, you know... Generating, you know, making it easier to produce content and so on. Like, you know, customers, users would have an idea what they want to do. You know, from that it can actually determine, what kind of machine learning models would solve the problem that would, you know, fits that use case. But then, that's when the hard thing begins, right? So when you find a model, identify the model that can do the thing that you wanted to do, you need to turn that into a thing that you can deploy. So how do you go from machine learning model that does a thing that you need to do, to a container with the right executor, the artifact they can actually go and deploy, right? So we've seen customers doing that on their own, right? So, and it's got a bit of work, and that's why we are excited about the automation that we can offer and then turn that into a turnkey problem, right? So a turnkey process. >> Luis, talk about the use cases. If I don't mind going and double down on the previous answer. You got existing services, and then there's new AI applications, AI for applications. What are the use cases with existing stuff, and the new applications that are being built? >> Yeah, I mean, existing itself is, for example, how do you do very smart search and auto completion, you know, when you are editing documents, for example. Very, very smart search of documents, summarization of tax, expanding bullets into pros in a way that, you know, don't have to spend as much human time. Just some of the existing applications, right? So some of the new ones are like truly AI native ways of producing content. Like there's a company that, you know, we share investors and love what they're doing called runwayyML, for example. It's sort of like an AI first way of editing and creating visual content, right? So you could say you have a video, you could say make this video look like, it's night as opposed to dark, or remove that dog in the corner. You can do that in a way that you couldn't do otherwise. So there's like definitely AI native use cases. And yet not only in life sciences, you know, there's quite a bit of advances on AI-based, you know, therapies and diagnostics processes that are designed using automated processes. And this is something that I feel like, we were just scratching the surface there. There's huge opportunities there, right? >> Talk about the inference and AI and production kind of angle here, because cost is a huge concern when you look at... And there's a hardware and that flexibility there. So I can see how that could help, but is there a cost freight train that can get out of control here if you don't deploy properly? Talk about the scale problem around cost in AI. >> Yeah, absolutely. So, you know, very quickly. One thing that people tend to think about is the cost is. You know, training has really high dollar amounts it tends over index on that. But what you have to think about is that for every model that's actually useful, you're going to train it once, and then run it a large number of times in inference. That means that over the lifetime of a model, the vast majority of the compute cycles and the cost are going to go to inference. And that's what we address, right? So, and to give you some idea, if you're talking about using large language model today, you know, you can say it's going to cost a couple of cents per, you know, 2,000 words output. If you have a million users active, you know, a day, you know, if you're lucky and you have that, you can, this cost can actually balloon very quickly to millions of dollars a month, just in inferencing costs. You know, assuming you know, that you actually have access to the infrastructure to run it, right? So means that if you don't pay attention to these inference costs and that's definitely going to be a surprise. And affects the economics of the product where this is embedded in, right? So this is something that, you know, if there's quite a bit of attention being put on right now on how do you do search with large language models and you don't pay attention to the economics, you know, you can have a surprise. You have to change the business model there. >> Yeah. I think that's important to call out, because you don't want it to be a runaway cost structure where you architected it wrong and then next thing you know, you got to unwind that. I mean, it's more than technical debt, it's actually real debt, it's real money. So, talk about some of the dynamics with the customers. How are they architecting this? How do they get ahead of that problem? What do you guys do specifically to solve that? >> Yeah, I mean, well, we help customers. So, it's first of all, be hyper aware, you know, understanding what's going to be the cost for them deploying the models into production and showing them the possibilities of how you can deploy the model with different cost structure, right? So that's where, you know, the ability to have hardware independence is so important because once you have hardware independence, after you optimize models, obviously, you have a new, you know, dimension of freedom to choose, you know, what is the right throughput per dollar for you. And then where, and what are the options? And once you make that decision, you want to automate the process of putting into production. So the way we help customers is showing very clearly in their use case, you know, how they can deploy their models in a much more cost-effective way. You know, when the cases... There's a case study that we put out recently, showing a 4x reduction in deployment costs, right? So this is by doing a mix optimization and choosing the right hardware. >> How do you address the concern that someone might say, Luis said, "Hey, you know, I don't want to degrade performance and latency, and I don't want the user experience to suffer." What's the answer there? >> Two things. So first of all, all of the manipulations that we do in the model is to turn the model to efficient code without changing the behavior of the models. We wouldn't degrade the experience of the user by having the model be wrong more often. And we don't change that at all. The model behaves the way it was validated for. And then the second thing is, you know, user experience with respect to latency, it's all about a maximum... Like, you could say, I want a model to run at 50 milliseconds or less. If it's much faster than 15 seconds, you're not going to notice the difference. But if it's lower, you're going to notice a difference. So the key here is that, how do you find a set of options to deploy, that you are not overshooting performance in a way that's going to lead to costs that has no additional benefits. And this provides a huge, a very significant margin of choices, set of choices that you can optimize for cost without degrading customer experience, right. End user experience. >> Yeah, and I also point out the large language models like the ChatGPTs of the world, they're coming out with Dave Moth and I were talking on this breaking analysis around, this being like, over 10X more computational intensive on capabilities. So this hardware independence is a huge thing. So, and also supply chain, some people can't get servers by the way, so, or hardware these days. >> Or even more interestingly, right? So they do not grow in trees, John. Like GPUs is not kind of stuff that you plant an orchard until you have a bunch and then you can increase it, but no, these things, you know, take a while. So, and you can't increase it overnight. So being able to live with those cycles that are available to you is not just important for all for cost, but also important for people to scale and serve more users at, you know, at whatever pace that they come, right? >> You know, it's really great to talk to you, and congratulations on OctaML. Looking forward to the startup showcase, we'll be featuring you guys there. But I want to get your personal opinion as someone in the industry and also, someone who's been in the computer science area for your career. You know, computer science has always been great, and there's more people enrolling in computer science, more diversity than ever before, but there's also more computer science related fields. How is this opening up computer science and where's AI going with the computers, with the science? Can you share your vision on, you know, the aperture, or the landscape of CompSci, or CS students, and opportunities. >> Yeah, no, absolutely. I think it's fair to say that computer has been embedded in pretty much every aspect of human life these days. Human life these days, right? So for everything. And AI has been a counterpart, it been an integral component of computer science for a while. And this medicines that happened in the last 10, 15 years in AI has shown, you know, new application has I think re-energized how people see what computers can do. And you, you know, there is this picture in our department that shows computer science at the center called the flower picture, and then all the different paddles like life sciences, social sciences, and then, you know, mechanical engineering, all these other things that, and I feel like it can replace that center with computer science. I put AI there as well, you see AI, you know touching all these applications. AI in healthcare, diagnostics. AI in discovery in the sciences, right? So, but then also AI doing things that, you know, the humans wouldn't have to do anymore. They can do better things with their brains, right? So it's permitting every single aspect of human life from intellectual endeavor to day-to-day work, right? >> Yeah. And I think the ChatGPT and OpenAI has really kind of created a mainstream view that everyone sees value in it. Like you could be in the data center, you could be in bio, you could be in healthcare. I mean, every industry sees value. So this brings up what I can call the horizontally scalable use constance. And so this opens up the conversation, what's going to change from this? Because if you go horizontally scalable, which is a cloud concept as you know, that's going to create a lot of opportunities and some shifting of how you think about architecture around data, for instance. What's your opinion on what this will do to change the inflection of the role of architecting platforms and the role of data specifically? >> Yeah, so good question. There is a lot in there, by the way, I should have added the previous question, that you can use AI to do better AI as well, which is what we do, and other folks are doing as well. And so the point I wanted to make here is that it's pretty clear that you have a cloud focus component with a nudge focused counterparts. Like you have AI models, but both in the Cloud and in the Edge, right? So the ability of being able to run your AI model where it runs best also has a data advantage to it from say, from a privacy point of view. That's inherently could say, "Hey, I want to run something, you know, locally, strictly locally, such that I don't expose the data to an infrastructure." And you know that the data never leaves you, right? Never leaves the device. Now you can imagine things that's already starting to happen, like you do some forms of training and model customization in the model architecture itself and the system architecture, such that you do this as close to the user as possible. And there's something called federated learning that has been around for some time now that's finally happening is, how do you get a data from butcher places, you do, you know, some common learning and then you send a model to the Edges, and they get refined for the final use in a way that you get the advantage of aggregating data but you don't get the disadvantage of privacy issues and so on. >> It's super exciting. >> And some of the considerations, yeah. >> It's super exciting area around data infrastructure, data science, computer science. Luis, congratulations on your success at OctaML. You're in the middle of it. And the best thing about its businesses are looking at this and really reinventing themselves and if a business isn't thinking about restructuring their business around AI, they're probably will be out of business. So this is a great time to be in the field. So thank you for sharing your insights here in theCUBE. >> Great. Thank you very much, John. Always a pleasure talking to you. Always have a lot of fun. And we both speak really fast, I can tell, you know, so. (both laughing) >> I know. We'll not the transcript available, we'll integrate it into our CubeGPT model that we have Luis. >> That's right. >> Great. >> Great. >> Great to talk to you, thank you, John. Thanks, man, bye. >> Hey, this is theCUBE. I'm John Furrier, here in Palo Alto, Cube Conversation. Thanks for watching. (gentle music)

>> From the Cube Studios in Palo Alto in Boston, bringing you data-driven insights from The Cube and ETR. This is "Breaking Analysis" with Dave Vellante. >> The world's leading telcos are trying to shed the stigma of being monopolies lacking innovation. Telcos have been great at operational efficiency and connectivity and living off of transmission, and the costs and expenses or revenue associated with that transmission. But in a world beyond telephone poles and basic wireless and mobile services, how will telcos modernize and become more agile and monetize new opportunities brought about by 5G and private wireless and a spate of new innovations and infrastructure, cloud data and apps? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this breaking analysis and ahead of Mobile World Congress or now, MWC23, we explore the evolution of the telco business and how the industry is in many ways, mimicking transformations that took place decades ago in enterprise IT. We'll model some of the traditional enterprise vendors using ETR data and investigate how they're faring in the telecommunications sector, and we'll pose some of the key issues facing the industry this decade. First, let's take a look at what the GSMA has in store for MWC23. GSMA is the host of what used to be called Mobile World Congress. They've set the theme for this year's event as "Velocity" and they've rebranded MWC to reflect the fact that mobile technology is only one part of the story. MWC has become one of the world's premier events highlighting innovations not only in Telco, mobile and 5G, but the collision between cloud, infrastructure, apps, private networks, smart industries, machine intelligence, and AI, and more. MWC comprises an enormous ecosystem of service providers, technology companies, and firms from virtually every industry including sports and entertainment. And as well, GSMA, along with its venue partner at the Fira Barcelona, have placed a major emphasis on sustainability and public and private partnerships. Virtually every industry will be represented at the event because every industry is impacted by the trends and opportunities in this space. GSMA has said it expects 80,000 attendees at MWC this year, not quite back to 2019 levels, but trending in that direction. Of course, attendance from Chinese participants has historically been very high at the show, and obviously the continued travel issues from that region are affecting the overall attendance, but still very strong. And despite these concerns, Huawei, the giant Chinese technology company. has the largest physical presence of any exhibitor at the show. And finally, GSMA estimates that more than $300 million in economic benefit will result from the event which takes place at the end of February and early March. And The Cube will be back at MWC this year with a major presence thanks to our anchor sponsor, Dell Technologies and other supporters of our content program, including Enterprise Web, ArcaOS, VMware, Snowflake, Cisco, AWS, and others. And one of the areas we're interested in exploring is the evolution of the telco stack. It's a topic that's often talked about and one that we've observed taking place in the 1990s when the vertically integrated IBM mainframe monopoly gave way to a disintegrated and horizontal industry structure. And in many ways, the same thing is happening today in telecommunications, which is shown on the left-hand side of this diagram. Historically, telcos have relied on a hardened, integrated, and incredibly reliable, and secure set of hardware and software services that have been fully vetted and tested, and certified, and relied upon for decades. And at the top of that stack on the left are the crown jewels of the telco stack, the operational support systems and the business support systems. For the OSS, we're talking about things like network management, network operations, service delivery, quality of service, fulfillment assurance, and things like that. For the BSS systems, these refer to customer-facing elements of the stack, like revenue, order management, what products they sell, billing, and customer service. And what we're seeing is telcos have been really good at operational efficiency and making money off of transport and connectivity, but they've lacked the innovation in services and applications. They own the pipes and that works well, but others, be the over-the-top content companies, or private network providers and increasingly, cloud providers have been able to bypass the telcos, reach around them, if you will, and drive innovation. And so, the right-most diagram speaks to the need to disaggregate pieces of the stack. And while the similarities to the 1990s in enterprise IT are greater than the differences, there are things that are different. For example, the granularity of hardware infrastructure will not likely be as high where competition occurred back in the 90s at every layer of the value chain with very little infrastructure integration. That of course changed in the 2010s with converged infrastructure and hyper-converged and also software defined. So, that's one difference. And the advent of cloud, containers, microservices, and AI, none of that was really a major factor in the disintegration of legacy IT. And that probably means that disruptors can move even faster than did the likes of Intel and Microsoft, Oracle, Cisco, and the Seagates of the 1990s. As well, while many of the products and services will come from traditional enterprise IT names like Dell, HPE, Cisco, Red Hat, VMware, AWS, Microsoft, Google, et cetera, many of the names are going to be different and come from traditional network equipment providers. These are names like Ericsson and Huawei, and Nokia, and other names, like Wind River, and Rakuten, and Dish Networks. And there are enormous opportunities in data to help telecom companies and their competitors go beyond telemetry data into more advanced analytics and data monetization. There's also going to be an entirely new set of apps based on the workloads and use cases ranging from hospitals, sports arenas, race tracks, shipping ports, you name it. Virtually every vertical will participate in this transformation as the industry evolves its focus toward innovation, agility, and open ecosystems. Now remember, this is not a binary state. There are going to be greenfield companies disrupting the apple cart, but the incumbent telcos are going to have to continue to ensure newer systems work with their legacy infrastructure, in their OSS and BSS existing systems. And as we know, this is not going to be an overnight task. Integration is a difficult thing, transformations, migrations. So that's what makes this all so interesting because others can come in with Greenfield and potentially disrupt. There'll be interesting partnerships and ecosystems will form and coalitions will also form. Now, we mentioned that several traditional enterprise companies are or will be playing in this space. Now, ETR doesn't have a ton of data on specific telecom equipment and software providers, but it does have some interesting data that we cut for this breaking analysis. What we're showing here in this graphic is some of the names that we've followed over the years and how they're faring. Specifically, we did the cut within the telco sector. So the Y-axis here shows net score or spending velocity. And the horizontal axis, that shows the presence or pervasiveness in the data set. And that table insert in the upper left, that informs as to how the dots are plotted. You know, the two columns there, net score and the ends. And that red-dotted line, that horizontal line at 40%, that is an indicator of a highly elevated level. Anything above that, we consider quite outstanding. And what we'll do now is we'll comment on some of the cohorts and share with you how they're doing in telecommunications, and that sector, that vertical relative to their position overall in the data set. Let's start with the public cloud players. They're prominent in every industry. Telcos, telecommunications is no exception and it's quite an interesting cohort here. On the one hand, they can help telecommunication firms modernize and become more agile by eliminating the heavy lifting and you know, all the cloud, you know, value prop, data center costs, and the cloud benefits. At the same time, public cloud players are bringing their services to the edge, building out their own global networks and are a disruptive force to traditional telcos. All right, let's talk about Azure first. Their net score is basically identical to telco relative to its overall average. AWS's net score is higher in telco by just a few percentage points. Google Cloud platform is eight percentage points higher in telco with a 53% net score. So all three hyperscalers have an equal or stronger presence in telco than their average overall. Okay, let's look at the traditional enterprise hardware and software infrastructure cohort. Dell, Cisco, HPE, Red Hat, VMware, and Oracle. We've highlighted in this chart just as sort of indicators or proxies. Dell's net score's 10 percentage points higher in telco than its overall average. Interesting. Cisco's is a bit higher. HPE's is actually lower by about nine percentage points in the ETR survey, and VMware's is lower by about four percentage points. Now, Red Hat is really interesting. OpenStack, as we've previously reported is popular with telcos who want to build out their own private cloud. And the data shows that Red Hat OpenStack's net score is 15 percentage points higher in the telco sector than its overall average. OpenShift, on the other hand, has a net score that's four percentage points lower in telco than its overall average. So this to us talks to the pace of adoption of microservices and containers. You know, it's going to happen, but it's going to happen more slowly. Finally, Oracle's spending momentum is somewhat lower in the sector than its average, despite the firm having a decent telco business. IBM and Accenture, heavy services companies are both lower in this sector than their average. And real quickly, snowflake's net score is much lower by about 12 percentage points relative to its very high average net score of 62%. But we look for them to be a player in this space as telcos need to modernize their analytics stack and share data in a governed manner. Databricks' net score is also much lower than its average by about 13 points. And same, I would expect them to be a player as open architectures and cloud gains steam in telco. All right, let's close out now on what we're going to be talking about at MWC23 and some of the key issues that we'll be unpacking. We've talked about stack disaggregation in this breaking analysis, but the key here will be the pace at which it will reach the operational efficiency and reliability of closed stacks. Telcos, you know, in a large part, they're engineering heavy firms and much of their work takes place, kind of in the basement, in the dark. It's not really a big public hype machine, and they tend to move slowly and cautiously. While they understand the importance of agility, they're going to be careful because, you know, it's in their DNA. And so at the same time, if they don't move fast enough, they're going to get hurt and disrupted by competitors. So that's going to be a topic of conversation, and we'll be looking for proof points. And the other comment I'll make is around integration. Telcos because of their conservatism will benefit from better testing and those firms that can innovate on the testing front and have labs and certifications and innovate at that level, with an ecosystem are going to be in a better position. Because open sometimes means wild west. So the more players like Dell, HPE, Cisco, Red Hat, et cetera, that do that and align with their ecosystems and provide those resources, the faster adoption is going to go. So we'll be looking for, you know, who's actually doing that, Open RAN or Radio Access Networks. That fits in this discussion because O-RAN is an emerging network architecture. It essentially enables the use of open technologies from an ecosystem and over time, look at O-RAN is going to be open, but the questions, you know, a lot of questions remain as to when it will be able to deliver the operational efficiency of traditional RAN. Got some interesting dynamics going on. Rakuten is a company that's working hard on this problem, really focusing on operational efficiency. Then you got Dish Networks. They're also embracing O-RAN. They're coming at it more from service innovation. So that's something that we'll be monitoring and unpacking. We're going to look at cloud as a disruptor. On the one hand, cloud can help drive agility, as we said earlier and optionality, and innovation for incumbent telcos. But the flip side is going to also do the same for startups trying to disrupt and cloud attracts startups. While some of the telcos are actually embracing the cloud, many are being cautious. So that's going to be an interesting topic of discussion. And there's private wireless networks and 5G, and hyperlocal private networks, they're being deployed, you know, at the edge. This idea of open edge is also a really hot topic and this trend is going to accelerate. You know, the importance here is that the use cases are going to be widely varied. The needs of a hospital are going to be different than those of a sports venue are different from a remote drilling location, and energy or a concert venue. Things like real-time AI inference and data flows are going to bring new services and monetization opportunities. And many firms are going to be bypassing traditional telecommunications networks to build these out. Satellites as well, we're going to see, you know, in this decade, you're going to have, you're going to look down at Google Earth and you're going to see real-time. You know, today you see snapshots and so, lots of innovations going in that space. So how is this going to disrupt industries and traditional industry structures? Now, as always, we'll be looking at data angles, right? 'Cause it's in The Cube's DNA to follow the data and what opportunities and risks data brings. The Cube is going to be on location at MWC23 at the end of the month. We got a great set. We're in the walkway between halls four and five, right in Congress Square, it's booths CS60. So we'll have a full, they're called Stan CS60. We have a full schedule. I'm going to be there with Lisa Martin, Dave Nicholson and the entire Cube crew, so don't forget to stop by. All right, that's a wrap. I want to thank Alex Myerson, who's on production and manages the podcast, 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 editor-in-chief over at Silicon Angle, does some great stuff for us. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search "Breaking Analysis" podcasts I publish each week on wikibon.com and silicon angle.com. And all the video content is available on demand at thecube.net. You can email me directly at david.vellante@silicon angle.com. You can DM me at dvellante or comment on my LinkedIn post. Please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for The Cube Insights powered by ETR. Thanks for watching and we'll see you at Mobile World Congress, and/or at next time on "Breaking Analysis." (bright music) (bright music fades)

(upbeat music) >> Okay, welcome back everyone at Supercloud 2 event, live here in Palo Alto, theCUBE Studios live stage performance, virtually syndicating it all over the world. I'm John Furrier with Dave Vellante here as Cube alumni, and special influencer guest, Howie Xu, VP of Machine Learning and Zscaler, also part-time as a CUBE analyst 'cause he is that good. Comes on all the time. You're basically a CUBE analyst as well. Thanks for coming on. >> Thanks for inviting me. >> John: Technically, you're not really a CUBE analyst, but you're kind of like a CUBE analyst. >> Happy New Year to everyone. >> Dave: Great to see you. >> Great to see you, Dave and John. >> John: We've been talking about ChatGPT online. You wrote a great post about it being more like Amazon, not like Google. >> Howie: More than just Google Search. >> More than Google Search. Oh, it's going to compete with Google Search, which it kind of does a little bit, but more its infrastructure. So a clever point, good segue into this conversation, because this is kind of the beginning of these kinds of next gen things we're going to see. Things where it's like an obvious next gen, it's getting real. Kind of like seeing the browser for the first time, Mosaic browser. Whoa, this internet thing's real. I think this is that moment and Supercloud like enablement is coming. So this has been a big part of the Supercloud kind of theme. >> Yeah, you talk about Supercloud, you talk about, you know, AI, ChatGPT. I really think the ChatGPT is really another Netscape moment, the browser moment. Because if you think about internet technology, right? It was brewing for 20 years before early 90s. Not until you had a, you know, browser, people realize, "Wow, this is how wonderful this technology could do." Right? You know, all the wonderful things. Then you have Yahoo and Amazon. I think we have brewing, you know, the AI technology for, you know, quite some time. Even then, you know, neural networks, deep learning. But not until ChatGPT came along, people realize, "Wow, you know, the user interface, user experience could be that great," right? So I really think, you know, if you look at the last 30 years, there is a browser moment, there is iPhone moment. I think ChatGPT moment is as big as those. >> Dave: What do you see as the intersection of things like ChatGPT and the Supercloud? Of course, the media's going to focus, journalists are going to focus on all the negatives and the privacy. Okay. You know we're going to get by that, right? Always do. Where do you see the Supercloud and sort of the distributed data fitting in with ChatGPT? Does it use that as a data source? What's the link? >> Howie: I think there are number of use cases. One of the use cases, we talked about why we even have Supercloud because of the complexity, because of the, you know, heterogeneous nature of different clouds. In order for me as a developer, in order for me to create applications, I have so many things to worry about, right? It's a complexity. But with ChatGPT, with the AI, I don't have to worry about it, right? Those kind of details will be taken care of by, you know, the underlying layer. So we have been talking about on this show, you know, over the last, what, year or so about the Supercloud, hey, defining that, you know, API layer spanning across, you know, multiple clouds. I think that will be happening. However, for a lot of the things, that will be more hidden, right? A lot of that will be automated by the bots. You know, we were just talking about it right before the show. One of the profound statement I heard from Adrian Cockcroft about 10 years ago was, "Hey Howie, you know, at Netflix, right? You know, IT is just one API call away." That's a profound statement I heard about a decade ago. I think next decade, right? You know, the IT is just one English language away, right? So when it's one English language away, it's no longer as important, API this, API that. You still need API just like hardware, right? You still need all of those things. That's going to be more hidden. The high level thing will be more, you know, English language or the language, right? Any language for that matter. >> Dave: And so through language, you'll tap services that live across the Supercloud, is what you're saying? >> Howie: You just tell what you want, what you desire, right? You know, the bots will help you to figure out where the complexity is, right? You know, like you said, a lot of criticism about, "Hey, ChatGPT doesn't do this, doesn't do that." But if you think about how to break things down, right? For instance, right, you know, ChatGPT doesn't have Microsoft stock price today, obviously, right? However, you can ask ChatGPT to write a program for you, retrieve the Microsoft stock price, (laughs) and then just run it, right? >> Dave: Yeah. >> So the thing to think about- >> John: It's only going to get better. It's only going to get better. >> The thing people kind of unfairly criticize ChatGPT is it doesn't do this. But can you not break down humans' task into smaller things and get complex things to be done by the ChatGPT? I think we are there already, you know- >> John: That to me is the real game changer. That's the assembly of atomic elements at the top of the stack, whether the interface is voice or some programmatic gesture based thing, you know, wave your hand or- >> Howie: One of the analogy I used in my blog was, you know, each person, each professional now is a quarterback. And we suddenly have, you know, a lot more linebacks or you know, any backs to work for you, right? For free even, right? You know, and then that's sort of, you should think about it. You are the quarterback of your day-to-day job, right? Your job is not to do everything manually yourself. >> Dave: You call the play- >> Yes. >> Dave: And they execute. Do your job. >> Yes, exactly. >> Yeah, all the players are there. All the elves are in the North Pole making the toys, Dave, as we say. But this is the thing, I want to get your point. This change is going to require a new kind of infrastructure software relationship, a new kind of operating runtime, a new kind of assembler, a new kind of loader link things. This very operating systems kind of concepts. >> Data intensive, right? How to process the data, how to, you know, process so gigantic data in parallel, right? That's actually a tough job, right? So if you think about ChatGPT, why OpenAI is ahead of the game, right? You know, Google may not want to acknowledge it, right? It's not necessarily they do, you know, not have enough data scientist, but the software engineering pieces, you know, behind it, right? To train the model, to actually do all those things in parallel, to do all those things in a cost effective way. So I think, you know, a lot of those still- >> Let me ask you a question. Let me ask you a question because we've had this conversation privately, but I want to do it while we're on stage here. Where are all the alpha geeks and developers and creators and entrepreneurs going to gravitate to? You know, in every wave, you see it in crypto, all the alphas went into crypto. Now I think with ChatGPT, you're going to start to see, like, "Wow, it's that moment." A lot of people are going to, you know, scrum and do startups. CTOs will invent stuff. There's a lot of invention, a lot of computer science and customer requirements to figure out. That's new. Where are the alpha entrepreneurs going to go to? What do you think they're going to gravitate to? If you could point to the next layer to enable this super environment, super app environment, Supercloud. 'Cause there's a lot to do to enable what you just said. >> Howie: Right. You know, if you think about using internet as the analogy, right? You know, in the early 90s, internet came along, browser came along. You had two kind of companies, right? One is Amazon, the other one is walmart.com. And then there were company, like maybe GE or whatnot, right? Really didn't take advantage of internet that much. I think, you know, for entrepreneurs, suddenly created the Yahoo, Amazon of the ChatGPT native era. That's what we should be all excited about. But for most of the Fortune 500 companies, your job is to surviving sort of the big revolution. So you at least need to do your walmart.com sooner than later, right? (laughs) So not be like GE, right? You know, hand waving, hey, I do a lot of the internet, but you know, when you look back last 20, 30 years, what did they do much with leveraging the- >> So you think they're going to jump in, they're going to build service companies or SaaS tech companies or Supercloud companies? >> Howie: Okay, so there are two type of opportunities from that perspective. One is, you know, the OpenAI ish kind of the companies, I think the OpenAI, the game is still open, right? You know, it's really Close AI today. (laughs) >> John: There's room for competition, you mean? >> There's room for competition, right. You know, you can still spend you know, 50, $100 million to build something interesting. You know, there are company like Cohere and so on and so on. There are a bunch of companies, I think there is that. And then there are companies who's going to leverage those sort of the new AI primitives. I think, you know, we have been talking about AI forever, but finally, finally, it's no longer just good, but also super useful. I think, you know, the time is now. >> John: And if you have the cloud behind you, what do you make the Amazon do differently? 'Cause Amazon Web Services is only going to grow with this. It's not going to get smaller. There's more horsepower to handle, there's more needs. >> Howie: Well, Microsoft already showed what's the future, right? You know, you know, yes, there is a kind of the container, you know, the serverless that will continue to grow. But the future is really not about- >> John: Microsoft's shown the future? >> Well, showing that, you know, working with OpenAI, right? >> Oh okay. >> They already said that, you know, we are going to have ChatGPT service. >> $10 billion, I think they're putting it. >> $10 billion putting, and also open up the Open API services, right? You know, I actually made a prediction that Microsoft future hinges on OpenAI. I think, you know- >> John: They believe that $10 billion bet. >> Dave: Yeah. $10 billion bet. So I want to ask you a question. It's somewhat academic, but it's relevant. For a number of years, it looked like having first mover advantage wasn't an advantage. PCs, spreadsheets, the browser, right? Social media, Friendster, right? Mobile. Apple wasn't first to mobile. But that's somewhat changed. The cloud, AWS was first. You could debate whether or not, but AWS okay, they have first mover advantage. Crypto, Bitcoin, first mover advantage. Do you think OpenAI will have first mover advantage? >> It certainly has its advantage today. I think it's year two. I mean, I think the game is still out there, right? You know, we're still in the first inning, early inning of the game. So I don't think that the game is over for the rest of the players, whether the big players or the OpenAI kind of the, sort of competitors. So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest, to get, you know, another shot to the OpenAI sort of the level?" You know, I did a- (laughs) >> Line up. >> That's classic VC. "How much does it cost me to replicate?" >> I'm pretty sure he asked the question to a bunch of guys, right? >> Good luck with that. (laughs) >> So we kind of did some napkin- >> What'd you come up with? (laughs) >> $100 million is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So 100 million. >> John: Hundreds of millions. >> Yeah, yeah, yeah. 100 million order of magnitude is what I came up with. You know, we can get into details, you know, in other sort of the time, but- >> Dave: That's actually not that much if you think about it. >> Howie: Exactly. So when he heard me articulating why is that, you know, he's thinking, right? You know, he actually, you know, asked me, "Hey, you know, there's this company. Do you happen to know this company? Can I reach out?" You know, those things. So I truly believe it's not a billion or 10 billion issue, it's more like 100. >> John: And also, your other point about referencing the internet revolution as a good comparable. The other thing there is online user population was a big driver of the growth of that. So what's the equivalent here for online user population for AI? Is it more apps, more users? I mean, we're still early on, it's first inning. >> Yeah. We're kind of the, you know- >> What's the key metric for success of this sector? Do you have a read on that? >> I think the, you know, the number of users is a good metrics, but I think it's going to be a lot of people are going to use AI services without even knowing they're using it, right? You know, I think a lot of the applications are being already built on top of OpenAI, and then they are kind of, you know, help people to do marketing, legal documents, you know, so they're already inherently OpenAI kind of the users already. So I think yeah. >> Well, Howie, we've got to wrap, but I really appreciate you coming on. I want to give you a last minute to wrap up here. In your experience, and you've seen many waves of innovation. You've even had your hands in a lot of the big waves past three inflection points. And obviously, machine learning you're doing now, you're deep end. Why is this Supercloud movement, this wave of Supercloud and the discussion of this next inflection point, why is it so important? For the folks watching, why should they be paying attention to this particular moment in time? Could you share your super clip on Supercloud? >> Howie: Right. So this is simple from my point of view. So why do you even have cloud to begin with, right? IT is too complex, too complex to operate or too expensive. So there's a newer model. There is a better model, right? Let someone else operate it, there is elasticity out of it, right? That's great. Until you have multiple vendors, right? Many vendors even, you know, we're talking about kind of how to make multiple vendors look like the same, but frankly speaking, even one vendor has, you know, thousand services. Now it's kind of getting, what Kid was talking about what, cloud chaos, right? It's the evolution. You know, the history repeats itself, right? You know, you have, you know, next great things and then too many great things, and then people need to sort of abstract this out. So it's almost that you must do this. But I think how to abstract this out is something that at this time, AI is going to help a lot, right? You know, like I mentioned, right? A lot of the abstraction, you don't have to think about API anymore. I bet 10 years from now, you know, IT is one language away, not API away. So think about that world, right? So Supercloud in, in my opinion, sure, you kind of abstract things out. You have, you know, consistent layers. But who's going to do that? Is that like we all agreed upon the model, agreed upon those APIs? Not necessary. There are certain, you know, truth in that, but there are other truths, let bots take care of, right? Whether you know, I want some X happens, whether it's going to be done by Azure, by AWS, by GCP, bots will figure out at a given time with certain contacts with your security requirement, posture requirement. I'll think that out. >> John: That's awesome. And you know, Dave, you and I have been talking about this. We think scale is the new ratification. If you have first mover advantage, I'll see the benefit, but scale is a huge thing. OpenAI, AWS. >> Howie: Yeah. Every day, we are using OpenAI. Today, we are labeling data for them. So you know, that's a little bit of the- (laughs) >> John: Yeah. >> First mover advantage that other people don't have, right? So it's kind of scary. So I'm very sure that Google is a little bit- (laughs) >> When we do our super AI event, you're definitely going to be keynoting. (laughs) >> Howie: I think, you know, we're talking about Supercloud, you know, before long, we are going to talk about super intelligent cloud. (laughs) >> I'm super excited, Howie, about this. Thanks for coming on. Great to see you, Howie Xu. Always a great analyst for us contributing to the community. VP of Machine Learning and Zscaler, industry legend and friend of theCUBE. Thanks for coming on and sharing really, really great advice and insight into what this next wave means. This Supercloud is the next wave. "If you're not on it, you're driftwood," says Pat Gelsinger. So you're going to see a lot more discussion. We'll be back more here live in Palo Alto after this short break. >> Thank you. (upbeat music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> Confidential computing is a technology that aims to enhance data privacy and security, by providing encrypted computation on sensitive data and isolating data, and apps that are fenced off enclave during processing. The concept of, I got to start over. I fucked that up, I'm sorry. That's not right, what I said was not right. On Dave in five, four, three. Confidential computing is a technology that aims to enhance data privacy and security by providing encrypted computation on sensitive data, isolating data from apps and a fenced off enclave during processing. The concept of confidential computing is gaining popularity, especially in the cloud computing space, where sensitive data is often stored and of course processed. However, there are some who view confidential computing as an unnecessary technology in a marketing ploy by cloud providers aimed at calming customers who are cloud phobic. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we revisit the notion of confidential computing, and to do so, we'll invite two Google experts to the show. But before we get there, let's summarize briefly. There's not a ton of ETR data on the topic of confidential computing, I mean, it's a technology that's deeply embedded into silicon and computing architectures. But at the highest level, security remains the number one priority being addressed by IT decision makers in the coming year as shown here. And this data is pretty much across the board by industry, by region, by size of company. I mean we dug into it and the only slight deviation from the mean is in financial services. The second and third most cited priorities, cloud migration and analytics are noticeably closer to cybersecurity in financial services than in other sectors, likely because financial services has always been hyper security conscious, but security is still a clear number one priority in that sector. The idea behind confidential computing is to better address threat models for data in execution. Protecting data at rest and data in transit have long been a focus of security approaches, but more recently, silicon manufacturers have introduced architectures that separate data and applications from the host system, ARM, Intel, AMD, Nvidia and other suppliers are all on board, as are the big cloud players. Now, the argument against confidential computing is that it narrowly focuses on memory encryption and it doesn't solve the biggest problems in security. Multiple system images, updates, different services and the entire code flow aren't directly addressed by memory encryption. Rather to truly attack these problems, many believe that OSs need to be re-engineered with the attacker and hacker in mind. There are so many variables and at the end of the day, critics say the emphasis on confidential computing made by cloud providers is overstated and largely hype. This tweet from security researcher Rodrigo Bronco, sums up the sentiment of many skeptics. He says, "Confidential computing is mostly a marketing campaign from memory encryption. It's not driving the industry towards the hard open problems. It is selling an illusion." Okay. Nonetheless, encrypting data in use and fencing off key components of the system isn't a bad thing, especially if it comes with the package essentially for free. There has been a lack of standardization and interoperability between different confidential computing approaches. But the confidential computing consortium was established in 2019 ostensibly to accelerate the market and influence standards. Notably, AWS is not part of the consortium, likely because the politics of the consortium were probably a conundrum for AWS because the base technology defined by the consortium is seen as limiting by AWS. This is my guess, not AWS' words. But I think joining the consortium would validate a definition which AWS isn't aligned with. And two, it's got to lead with this Annapurna acquisition. It was way ahead with ARM integration, and so it's probably doesn't feel the need to validate its competitors. Anyway, one of the premier members of the confidential computing consortium is Google, along with many high profile names, including Aem, Intel, Meta, Red Hat, Microsoft, and others. And we're pleased to welcome two experts on confidential computing from Google to unpack the topic. Nelly Porter is Head of Product for GCP Confidential Computing and Encryption and Dr. Patricia Florissi is the Technical Director for the Office of the CTO at Google Cloud. Welcome Nelly and Patricia, great to have you. >> Great to be here. >> Thank you so much for having us. >> You're very welcome. Nelly, why don't you start and then Patricia, you can weigh in. Just tell the audience a little bit about each of your roles at Google Cloud. >> So I'll start, I'm owning a lot of interesting activities in Google and again, security or infrastructure securities that I usually own. And we are talking about encryption, end-to-end encryption, and confidential computing is a part of portfolio. Additional areas that I contribute to get with my team to Google and our customers is secure software supply chain because you need to trust your software. Is it operate in your confidential environment to have end-to-end security, about if you believe that your software and your environment doing what you expect, it's my role. >> Got it. Okay, Patricia? >> Well, I am a Technical Director in the Office of the CTO, OCTO for short in Google Cloud. And we are a global team, we include former CTOs like myself and senior technologies from large corporations, institutions and a lot of success for startups as well. And we have two main goals, first, we walk side by side with some of our largest, more strategic or most strategical customers and we help them solve complex engineering technical problems. And second, we advice Google and Google Cloud Engineering, product management on emerging trends and technologies to guide the trajectory of our business. We are unique group, I think, because we have created this collaborative culture with our customers. And within OCTO I spend a lot of time collaborating with customers in the industry at large on technologies that can address privacy, security, and sovereignty of data in general. >> Excellent. Thank you for that both of you. Let's get into it. So Nelly, what is confidential computing from Google's perspective? How do you define it? >> Confidential computing is a tool and one of the tools in our toolbox. And confidential computing is a way how we would help our customers to complete this very interesting end-to-end lifecycle of the data. And when customers bring in the data to cloud and want to protect it as they ingest it to the cloud, they protect it at rest when they store data in the cloud. But what was missing for many, many years is ability for us to continue protecting data and workloads of our customers when they run them. And again, because data is not brought to cloud to have huge graveyard, we need to ensure that this data is actually indexed. Again, there is some insights driven and drawn from this data. You have to process this data and confidential computing here to help. Now we have end-to-end protection of our customer's data when they bring the workloads and data to cloud thanks to confidential computing. >> Thank you for that. Okay, we're going to get into the architecture a bit, but before we do Patricia, why do you think this topic of confidential computing is such an important technology? Can you explain? Do you think it's transformative for customers and if so, why? >> Yeah, I would maybe like to use one thought, one way, one intuition behind why confidential computing matters because at the end of the day, it reduces more and more the customer's thrush boundaries and the attack surface. That's about reducing that periphery, the boundary in which the customer needs to mind about trust and safety. And in a way is a natural progression that you're using encryption to secure and protect data in the same way that we are encrypting data in transit and at rest. Now, we are also encrypting data while in the use. And among other beneficials, I would say one of the most transformative ones is that organizations will be able to collaborate with each other and retain the confidentiality of the data. And that is across industry, even though it's highly focused on, I wouldn't say highly focused but very beneficial for highly regulated industries, it applies to all of industries. And if you look at financing for example, where bankers are trying to detect fraud and specifically double finance where a customer is actually trying to get a finance on an asset, let's say a boat or a house, and then it goes to another bank and gets another finance on that asset. Now bankers would be able to collaborate and detect fraud while preserving confidentiality and privacy of the data. >> Interesting and I want to understand that a little bit more but I got to push you a little bit on this, Nellie if I can, because there's a narrative out there that says confidential computing is a marketing ploy I talked about this up front, by cloud providers that are just trying to placate people that are scared of the cloud. And I'm presuming you don't agree with that, but I'd like you to weigh in here. The argument is confidential computing is just memory encryption, it doesn't address many other problems. It is over hyped by cloud providers. What do you say to that line of thinking? >> I absolutely disagree as you can imagine Dave, with this statement. But the most importantly is we mixing a multiple concepts I guess, and exactly as Patricia said, we need to look at the end-to-end story, not again, is a mechanism. How confidential computing trying to execute and protect customer's data and why it's so critically important. Because what confidential computing was able to do, it's in addition to isolate our tenants in multi-tenant environments the cloud offering to offer additional stronger isolation, they called it cryptographic isolation. It's why customers will have more trust to customers and to other customers, the tenants running on the same host but also us because they don't need to worry about against rats and more malicious attempts to penetrate the environment. So what confidential computing is helping us to offer our customers stronger isolation between tenants in this multi-tenant environment, but also incredibly important, stronger isolation of our customers to tenants from us. We also writing code, we also software providers, we also make mistakes or have some zero days. Sometimes again us introduce, sometimes introduced by our adversaries. But what I'm trying to say by creating this cryptographic layer of isolation between us and our tenants and among those tenants, we really providing meaningful security to our customers and eliminate some of the worries that they have running on multi-tenant spaces or even collaborating together with very sensitive data knowing that this particular protection is available to them. >> Okay, thank you. Appreciate that. And I think malicious code is often a threat model missed in these narratives. You know, operator access. Yeah, maybe I trust my cloud's provider, but if I can fence off your access even better, I'll sleep better at night separating a code from the data. Everybody's ARM, Intel, AMD, Nvidia and others, they're all doing it. I wonder if Nell, if we could stay with you and bring up the slide on the architecture. What's architecturally different with confidential computing versus how operating systems and VMs have worked traditionally? We're showing a slide here with some VMs, maybe you could take us through that. >> Absolutely, and Dave, the whole idea for Google and now industry way of dealing with confidential computing is to ensure that three main property is actually preserved. Customers don't need to change the code. They can operate in those VMs exactly as they would with normal non-confidential VMs. But to give them this opportunity of lift and shift though, no changing the apps and performing and having very, very, very low latency and scale as any cloud can, some things that Google actually pioneer in confidential computing. I think we need to open and explain how this magic was actually done, and as I said, it's again the whole entire system have to change to be able to provide this magic. And I would start with we have this concept of root of trust and root of trust where we will ensure that this machine within the whole entire host has integrity guarantee, means nobody changing my code on the most low level of system, and we introduce this in 2017 called Titan. So our specific ASIC, specific inch by inch system on every single motherboard that we have that ensures that your low level former, your actually system code, your kernel, the most powerful system is actually proper configured and not changed, not tempered. We do it for everybody, confidential computing included, but for confidential computing is what we have to change, we bring in AMD or future silicon vendors and we have to trust their former, their way to deal with our confidential environments. And that's why we have obligation to validate intelligent not only our software and our former but also former and software of our vendors, silicon vendors. So we actually, when we booting this machine as you can see, we validate that integrity of all of this system is in place. It means nobody touching, nobody changing, nobody modifying it. But then we have this concept of AMD Secure Processor, it's special ASIC best specific things that generate a key for every single VM that our customers will run or every single node in Kubernetes or every single worker thread in our Hadoop spark capability. We offer all of that and those keys are not available to us. It's the best case ever in encryption space because when we are talking about encryption, the first question that I'm receiving all the time, "Where's the key? Who will have access to the key?" because if you have access to the key then it doesn't matter if you encrypted or not. So, but the case in confidential computing why it's so revolutionary technology, us cloud providers who don't have access to the keys, they're sitting in the hardware and they fed to memory controller. And it means when hypervisors that also know about this wonderful things saying I need to get access to the memories, that this particular VM I'm trying to get access to. They do not decrypt the data, they don't have access to the key because those keys are random, ephemeral and per VM, but most importantly in hardware not exportable. And it means now you will be able to have this very interesting world that customers or cloud providers will not be able to get access to your memory. And what we do, again as you can see, our customers don't need to change their applications. Their VMs are running exactly as it should run. And what you've running in VM, you actually see your memory clear, it's not encrypted. But God forbid is trying somebody to do it outside of my confidential box, no, no, no, no, no, you will now be able to do it. Now, you'll see cyber test and it's exactly what combination of these multiple hardware pieces and software pieces have to do. So OS is also modified and OS is modified such way to provide integrity. It means even OS that you're running in your VM box is not modifiable and you as customer can verify. But the most interesting thing I guess how to ensure the super performance of this environment because you can imagine Dave, that's increasing and it's additional performance, additional time, additional latency. So we're able to mitigate all of that by providing incredibly interesting capability in the OS itself. So our customers will get no changes needed, fantastic performance and scales as they would expect from cloud providers like Google. >> Okay, thank you. Excellent, appreciate that explanation. So you know again, the narrative on this is, well, you've already given me guarantees as a cloud provider that you don't have access to my data, but this gives another level of assurance, key management as they say is key. Now humans aren't managing the keys, the machines are managing them. So Patricia, my question to you is in addition to, let's go pre-confidential computing days, what are the sort of new guarantees that these hardware based technologies are going to provide to customers? >> So if I am a customer, I am saying I now have full guarantee of confidentiality and integrity of the data and of the code. So if you look at code and data confidentiality, the customer cares and they want to know whether their systems are protected from outside or unauthorized access, and that we covered with Nelly that it is. Confidential computing actually ensures that the applications and data antennas remain secret. The code is actually looking at the data, only the memory is decrypting the data with a key that is ephemeral, and per VM, and generated on demand. Then you have the second point where you have code and data integrity and now customers want to know whether their data was corrupted, tempered with or impacted by outside actors. And what confidential computing ensures is that application internals are not tempered with. So the application, the workload as we call it, that is processing the data is also has not been tempered and preserves integrity. I would also say that this is all verifiable, so you have attestation and this attestation actually generates a log trail and the log trail guarantees that provides a proof that it was preserved. And I think that the offers also a guarantee of what we call sealing, this idea that the secrets have been preserved and not tempered with, confidentiality and integrity of code and data. >> Got it. Okay, thank you. Nelly, you mentioned, I think I heard you say that the applications is transparent, you don't have to change the application, it just comes for free essentially. And we showed some various parts of the stack before, I'm curious as to what's affected, but really more importantly, what is specifically Google's value add? How do partners participate in this, the ecosystem or maybe said another way, how does Google ensure the compatibility of confidential computing with existing systems and applications? >> And a fantastic question by the way, and it's very difficult and definitely complicated world because to be able to provide these guarantees, actually a lot of work was done by community. Google is very much operate and open. So again our operating system, we working this operating system repository OS is OS vendors to ensure that all capabilities that we need is part of the kernels are part of the releases and it's available for customers to understand and even explore if they have fun to explore a lot of code. We have also modified together with our silicon vendors kernel, host kernel to support this capability and it means working this community to ensure that all of those pages are there. We also worked with every single silicon vendor as you've seen, and it's what I probably feel that Google contributed quite a bit in this world. We moved our industry, our community, our vendors to understand the value of easy to use confidential computing or removing barriers. And now I don't know if you noticed Intel is following the lead and also announcing a trusted domain extension, very similar architecture and no surprise, it's a lot of work done with our partners to convince work with them and make this capability available. The same with ARM this year, actually last year, ARM announced future design for confidential computing, it's called confidential computing architecture. And it's also influenced very heavily with similar ideas by Google and industry overall. So it's a lot of work in confidential computing consortiums that we are doing, for example, simply to mention, to ensure interop as you mentioned, between different confidential environments of cloud providers. They want to ensure that they can attest to each other because when you're communicating with different environments, you need to trust them. And if it's running on different cloud providers, you need to ensure that you can trust your receiver when you sharing your sensitive data workloads or secret with them. So we coming as a community and we have this at Station Sig, the community-based systems that we want to build, and influence, and work with ARM and every other cloud providers to ensure that they can interop. And it means it doesn't matter where confidential workloads will be hosted, but they can exchange the data in secure, verifiable and controlled by customers really. And to do it, we need to continue what we are doing, working open and contribute with our ideas and ideas of our partners to this role to become what we see confidential computing has to become, it has to become utility. It doesn't need to be so special, but it's what what we've wanted to become. >> Let's talk about, thank you for that explanation. Let's talk about data sovereignty because when you think about data sharing, you think about data sharing across the ecosystem in different regions and then of course data sovereignty comes up, typically public policy, lags, the technology industry and sometimes it's problematic. I know there's a lot of discussions about exceptions but Patricia, we have a graphic on data sovereignty. I'm interested in how confidential computing ensures that data sovereignty and privacy edicts are adhered to, even if they're out of alignment maybe with the pace of technology. One of the frequent examples is when you delete data, can you actually prove the data is deleted with a hundred percent certainty, you got to prove that and a lot of other issues. So looking at this slide, maybe you could take us through your thinking on data sovereignty. >> Perfect. So for us, data sovereignty is only one of the three pillars of digital sovereignty. And I don't want to give the impression that confidential computing addresses it at all, that's why we want to step back and say, hey, digital sovereignty includes data sovereignty where we are giving you full control and ownership of the location, encryption and access to your data. Operational sovereignty where the goal is to give our Google Cloud customers full visibility and control over the provider operations, right? So if there are any updates on hardware, software stack, any operations, there is full transparency, full visibility. And then the third pillar is around software sovereignty, where the customer wants to ensure that they can run their workloads without dependency on the provider's software. So they have sometimes is often referred as survivability that you can actually survive if you are untethered to the cloud and that you can use open source. Now, let's take a deep dive on data sovereignty, which by the way is one of my favorite topics. And we typically focus on saying, hey, we need to care about data residency. We care where the data resides because where the data is at rest or in processing need to typically abides to the jurisdiction, the regulations of the jurisdiction where the data resides. And others say, hey, let's focus on data protection, we want to ensure the confidentiality, and integrity, and availability of the data, which confidential computing is at the heart of that data protection. But it is yet another element that people typically don't talk about when talking about data sovereignty, which is the element of user control. And here Dave, is about what happens to the data when I give you access to my data, and this reminds me of security two decades ago, even a decade ago, where we started the security movement by putting firewall protections and logging accesses. But once you were in, you were able to do everything you wanted with the data. An insider had access to all the infrastructure, the data, and the code. And that's similar because with data sovereignty, we care about whether it resides, who is operating on the data, but the moment that the data is being processed, I need to trust that the processing of the data we abide by user's control, by the policies that I put in place of how my data is going to be used. And if you look at a lot of the regulation today and a lot of the initiatives around the International Data Space Association, IDSA and Gaia-X, there is a movement of saying the two parties, the provider of the data and the receiver of the data going to agree on a contract that describes what my data can be used for. The challenge is to ensure that once the data crosses boundaries, that the data will be used for the purposes that it was intended and specified in the contract. And if you actually bring together, and this is the exciting part, confidential computing together with policy enforcement. Now, the policy enforcement can guarantee that the data is only processed within the confines of a confidential computing environment, that the workload is in cryptographically verified that there is the workload that was meant to process the data and that the data will be only used when abiding to the confidentiality and integrity safety of the confidential computing environment. And that's why we believe confidential computing is one necessary and essential technology that will allow us to ensure data sovereignty, especially when it comes to user's control. >> Thank you for that. I mean it was a deep dive, I mean brief, but really detailed. So I appreciate that, especially the verification of the enforcement. Last question, I met you two because as part of my year-end prediction post, you guys sent in some predictions and I wasn't able to get to them in the predictions post, so I'm thrilled that you were able to make the time to come on the program. How widespread do you think the adoption of confidential computing will be in '23 and what's the maturity curve look like this decade in your opinion? Maybe each of you could give us a brief answer. >> So my prediction in five, seven years as I started, it will become utility, it will become TLS. As of freakin' 10 years ago, we couldn't believe that websites will have certificates and we will support encrypted traffic. Now we do, and it's become ubiquity. It's exactly where our confidential computing is heeding and heading, I don't know we deserve yet. It'll take a few years of maturity for us, but we'll do that. >> Thank you. And Patricia, what's your prediction? >> I would double that and say, hey, in the very near future, you will not be able to afford not having it. I believe as digital sovereignty becomes ever more top of mind with sovereign states and also for multinational organizations, and for organizations that want to collaborate with each other, confidential computing will become the norm, it will become the default, if I say mode of operation. I like to compare that today is inconceivable if we talk to the young technologists, it's inconceivable to think that at some point in history and I happen to be alive, that we had data at rest that was non-encrypted, data in transit that was not encrypted. And I think that we'll be inconceivable at some point in the near future that to have unencrypted data while we use. >> You know, and plus I think the beauty of the this industry is because there's so much competition, this essentially comes for free. I want to thank you both for spending some time on Breaking Analysis, there's so much more we could cover. I hope you'll come back to share the progress that you're making in this area and we can double click on some of these topics. Really appreciate your time. >> Anytime. >> Thank you so much, yeah. >> In summary, while confidential computing is being touted by the cloud players as a promising technology for enhancing data privacy and security, there are also those as we said, who remain skeptical. The truth probably lies somewhere in between and it will depend on the specific implementation and the use case as to how effective confidential computing will be. Look as with any new tech, it's important to carefully evaluate the potential benefits, the drawbacks, and make informed decisions based on the specific requirements in the situation and the constraints of each individual customer. But the bottom line is silicon manufacturers are working with cloud providers and other system companies to include confidential computing into their architectures. Competition in our view will moderate price hikes and at the end of the day, this is under-the-covers technology that essentially will come for free, so we'll take it. I want to thank our guests today, Nelly and Patricia from Google. And thanks to Alex Myerson who's on production and manages the podcast. Ken Schiffman as well out of our Boston studio. Kristin Martin and Cheryl Knight help get the word out on social media and in our newsletters, and Rob Hoof is our editor-in-chief over at siliconangle.com, does some great editing for us. Thank you all. Remember all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com where you can get all the news. If you want to get in touch, you can email me at david.vellante@siliconangle.com or DM me at D Vellante, and you can also comment on my LinkedIn post. Definitely you want to check out etr.ai for the best survey data in the enterprise tech business. I know we didn't hit on a lot today, but there's some amazing data and it's always being updated, so check that out. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (subtle music)

(light music) >> Hi everybody. Welcome to this Cube Conversation. I'm really pleased to announce a collaboration with Rob Strechay. He's a guest cube analyst, and we'll be working together to extract the signal from the noise. Rob is a long-time product pro, working at a number of firms including AWS, HP, HPE, NetApp, Snowplow. I did a stint as an analyst at Enterprise Strategy Group. Rob, good to see you. Thanks for coming into our Marlboro Studios. >> Well, thank you for having me. It's always great to be here. >> I'm really excited about working with you. We've known each other for a long time. You've been in the Cube a bunch. You know, you're in between gigs, and I think we can have a lot of fun together. Covering events, covering trends. So. let's get into it. What's happening out there? We're sort of exited the isolation economy. Things were booming. Now, everybody's tapping the brakes. From your standpoint, what are you seeing out there? >> Yeah. I'm seeing that people are really looking how to get more out of their data. How they're bringing things together, how they're looking at the costs of Cloud, and understanding how are they building out their SaaS applications. And understanding that when they go in and actually start to use Cloud, it's not only just using the base services anymore. They're looking at, how do I use these platforms as a service? Some are easier than others, and they're trying to understand, how do I get more value out of that relationship with the Cloud? They're also consolidating the number of Clouds that they have, I would say to try to better optimize their spend, and getting better pricing for that matter. >> Are you seeing people unhook Clouds, or just reduce maybe certain Cloud activities and going maybe instead of 60/40 going 90/10? >> Correct. It's more like the 90/10 type of rule where they're starting to say, Hey I'm not going to get rid of Azure or AWS or Google. I'm going to move a portion of this over that I was using on this one service. Maybe I got a great two-year contract to start with on this platform as a service or a database as a service. I'm going to unhook from that and maybe go with an independent. Maybe with something like a Snowflake or a Databricks on top of another Cloud, so that I can consolidate down. But it also gives them more flexibility as well. >> In our last breaking analysis, Rob, we identified six factors that were reducing Cloud consumption. There were factors and customer tactics. And I want to get your take on this. So, some of the factors really, you got fewer mortgage originations. FinTech, obviously big Cloud user. Crypto, not as much activity there. Lower ad spending means less Cloud. And then one of 'em, which you kind of disagreed with was less, less analytics, you know, fewer... Less frequency of calculations. I'll come back to that. But then optimizing compute using Graviton or AMD instances moving to cheaper storage tiers. That of course makes sense. And then optimize pricing plans. Maybe going from On Demand, you know, to, you know, instead of pay by the drink, buy in volume. Okay. So, first of all, do those make sense to you with the exception? We'll come back and talk about the analytics piece. Is that what you're seeing from customers? >> Yeah, I think so. I think that was pretty much dead on with what I'm seeing from customers and the ones that I go out and talk to. A lot of times they're trying to really monetize their, you know, understand how their business utilizes these Clouds. And, where their spend is going in those Clouds. Can they use, you know, lower tiers of storage? Do they really need the best processors? Do they need to be using Intel or can they get away with AMD or Graviton 2 or 3? Or do they need to move in? And, I think when you look at all of these Clouds, they always have pricing curves that are arcs from the newest to the oldest stuff. And you can play games with that. And understanding how you can actually lower your costs by looking at maybe some of the older generation. Maybe your application was written 10 years ago. You don't necessarily have to be on the best, newest processor for that application per se. >> So last, I want to come back to this whole analytics piece. Last June, I think it was June, Dev Ittycheria, who's the-- I call him Dev. Spelled Dev, pronounced Dave. (chuckles softly) Same pronunciation, different spelling. Dev Ittycheria, CEO of Mongo, on the earnings call. He was getting, you know, hit. Things were starting to get a little less visible in terms of, you know, the outlook. And people were pushing him like... Because you're in the Cloud, is it easier to dial down? And he said, because we're the document database, we support transaction applications. We're less discretionary than say, analytics. Well on the Snowflake earnings call, that same month or the month after, they were all over Slootman and Scarpelli. Oh, the Mongo CEO said that they're less discretionary than analytics. And Snowflake was an interesting comment. They basically said, look, we're the Cloud. You can dial it up, you can dial it down, but the area under the curve over a period of time is going to be the same, because they get their customers to commit. What do you say? You disagreed with the notion that people are running their calculations less frequently. Is that because they're trying to do a better job of targeting customers in near real time? What are you seeing out there? >> Yeah, I think they're moving away from using people and more expensive marketing. Or, they're trying to figure out what's my Google ad spend, what's my Meta ad spend? And what they're trying to do is optimize that spend. So, what is the return on advertising, or the ROAS as they would say. And what they're looking to do is understand, okay, I have to collect these analytics that better understand where are these people coming from? How do they get to my site, to my store, to my whatever? And when they're using it, how do they they better move through that? What you're also seeing is that analytics is not only just for kind of the retail or financial services or things like that, but then they're also, you know, using that to make offers in those categories. When you move back to more, you know, take other companies that are building products and SaaS delivered products. They may actually go and use this analytics for making the product better. And one of the big reasons for that is maybe they're dialing back how many product managers they have. And they're looking to be more data driven about how they actually go and build the product out or enhance the product. So maybe they're, you know, an online video service and they want to understand why people are either using or not using the whiteboard inside the product. And they're collecting a lot of that product analytics in a big way so that they can go through that. And they're doing it in a constant manner. This first party type tracking within applications is growing rapidly by customers. >> So, let's talk about who wins in that. So, obviously the Cloud guys, AWS, Google and Azure. I want to come back and unpack that a little bit. Databricks and Snowflake, we reported on our last breaking analysis, it kind of on a collision course. You know, a couple years ago we were thinking, okay, AWS, Snowflake and Databricks, like perfect sandwich. And then of course they started to become more competitive. My sense is they still, you know, compliment each other in the field, right? But, you know, publicly, they've got bigger aspirations, they get big TAMs that they're going after. But it's interesting, the data shows that-- So, Snowflake was off the charts in terms of spending momentum and our EPR surveys. Our partner down in New York, they kind of came into line. They're both growing in terms of market presence. Databricks couldn't get to IPO. So, we don't have as much, you know, visibility on their financials. You know, Snowflake obviously highly transparent cause they're a public company. And then you got AWS, Google and Azure. And it seems like AWS appears to be more partner friendly. Microsoft, you know, depends on what market you're in. And Google wants to sell BigQuery. >> Yeah. >> So, what are you seeing in the public Cloud from a data platform perspective? >> Yeah. I think that was pretty astute in what you were talking about there, because I think of the three, Google is definitely I think a little bit behind in how they go to market with their partners. Azure's done a fantastic job of partnering with these companies to understand and even though they may have Synapse as their go-to and where they want people to go to do AI and ML. What they're looking at is, Hey, we're going to also be friendly with Snowflake. We're also going to be friendly with a Databricks. And I think that, Amazon has always been there because that's where the market has been for these developers. So, many, like Databricks' and the Snowflake's have gone there first because, you know, Databricks' case, they built out on top of S3 first. And going and using somebody's object layer other than AWS, was not as simple as you would think it would be. Moving between those. >> So, one of the financial meetups I said meetup, but the... It was either the CEO or the CFO. It was either Slootman or Scarpelli talking at, I don't know, Merrill Lynch or one of the other financial conferences said, I think it was probably their Q3 call. Snowflake said 80% of our business goes through Amazon. And he said to this audience, the next day we got a call from Microsoft. Hey, we got to do more. And, we know just from reading the financial statements that Snowflake is getting concessions from Amazon, they're buying in volume, they're renegotiating their contracts. Amazon gets it. You know, lower the price, people buy more. Long term, we're all going to make more money. Microsoft obviously wants to get into that game with Snowflake. They understand the momentum. They said Google, not so much. And I've had customers tell me that they wanted to use Google's AI with Snowflake, but they can't, they got to go to to BigQuery. So, honestly, I haven't like vetted that so. But, I think it's true. But nonetheless, it seems like Google's a little less friendly with the data platform providers. What do you think? >> Yeah, I would say so. I think this is a place that Google looks and wants to own. Is that now, are they doing the right things long term? I mean again, you know, you look at Google Analytics being you know, basically outlawed in five countries in the EU because of GDPR concerns, and compliance and governance of data. And I think people are looking at Google and BigQuery in general and saying, is it the best place for me to go? Is it going to be in the right places where I need it? Still, it's still one of the largest used databases out there just because it underpins a number of the Google services. So you almost get, like you were saying, forced into BigQuery sometimes, if you want to use the tech on top. >> You do strategy. >> Yeah. >> Right? You do strategy, you do messaging. Is it the right call by Google? I mean, it's not a-- I criticize Google sometimes. But, I'm not sure it's the wrong call to say, Hey, this is our ace in the hole. >> Yeah. >> We got to get people into BigQuery. Cause, first of all, BigQuery is a solid product. I mean it's Cloud native and it's, you know, by all, it gets high marks. So, why give the competition an advantage? Let's try to force people essentially into what is we think a great product and it is a great product. The flip side of that is, they're giving up some potential partner TAM and not treating the ecosystem as well as one of their major competitors. What do you do if you're in that position? >> Yeah, I think that that's a fantastic question. And the question I pose back to the companies I've worked with and worked for is, are you really looking to have vendor lock-in as your key differentiator to your service? And I think when you start to look at these companies that are moving away from BigQuery, moving to even, Databricks on top of GCS in Google, they're looking to say, okay, I can go there if I have to evacuate from GCP and go to another Cloud, I can stay on Databricks as a platform, for instance. So I think it's, people are looking at what platform as a service, database as a service they go and use. Because from a strategic perspective, they don't want that vendor locking. >> That's where Supercloud becomes interesting, right? Because, if I can run on Snowflake or Databricks, you know, across Clouds. Even Oracle, you know, they're getting into business with Microsoft. Let's talk about some of the Cloud players. So, the big three have reported. >> Right. >> We saw AWSs Cloud growth decelerated down to 20%, which is I think the lowest growth rate since they started to disclose public numbers. And they said they exited, sorry, they said January they grew at 15%. >> Yeah. >> Year on year. Now, they had some pretty tough compares. But nonetheless, 15%, wow. Azure, kind of mid thirties, and then Google, we had kind of low thirties. But, well behind in terms of size. And Google's losing probably almost $3 billion annually. But, that's not necessarily a bad thing by advocating and investing. What's happening with the Cloud? Is AWS just running into the law, large numbers? Do you think we can actually see a re-acceleration like we have in the past with AWS Cloud? Azure, we predicted is going to be 75% of AWS IAS revenues. You know, we try to estimate IAS. >> Yeah. >> Even though they don't share that with us. That's a huge milestone. You'd think-- There's some people who have, I think, Bob Evans predicted a while ago that Microsoft would surpass AWS in terms of size. You know, what do you think? >> Yeah, I think that Azure's going to keep to-- Keep growing at a pretty good clip. I think that for Azure, they still have really great account control, even though people like to hate Microsoft. The Microsoft sellers that are out there making those companies successful day after day have really done a good job of being in those accounts and helping people. I was recently over in the UK. And the UK market between AWS and Azure is pretty amazing, how much Azure there is. And it's growing within Europe in general. In the states, it's, you know, I think it's growing well. I think it's still growing, probably not as fast as it is outside the U.S. But, you go down to someplace like Australia, it's also Azure. You hear about Azure all the time. >> Why? Is that just because of the Microsoft's software state? It's just so convenient. >> I think it has to do with, you know, and you can go with the reasoning they don't break out, you know, Office 365 and all of that out of their numbers is because they have-- They're in all of these accounts because the office suite is so pervasive in there. So, they always have reasons to go back in and, oh by the way, you're on these old SQL licenses. Let us move you up here and we'll be able to-- We'll support you on the old version, you know, with security and all of these things. And be able to move you forward. So, they have a lot of, I guess you could say, levers to stay in those accounts and be interesting. At least as part of the Cloud estate. I think Amazon, you know, is hitting, you know, the large number. Laws of large numbers. But I think that they're also going through, and I think this was seen in the layoffs that they were making, that they're looking to understand and have profitability in more of those services that they have. You know, over 350 odd services that they have. And you know, as somebody who went there and helped to start yet a new one, while I was there. And finally, it went to beta back in September, you start to look at the fact that, that number of services, people, their own sellers don't even know all of their services. It's impossible to comprehend and sell that many things. So, I think what they're going through is really looking to rationalize a lot of what they're doing from a services perspective going forward. They're looking to focus on more profitable services and bringing those in. Because right now it's built like a layer cake where you have, you know, S3 EBS and EC2 on the bottom of the layer cake. And then maybe you have, you're using IAM, the authorization and authentication in there and you have all these different services. And then they call it EMR on top. And so, EMR has to pay for that entire layer cake just to go and compete against somebody like Mongo or something like that. So, you start to unwind the costs of that. Whereas Azure, went and they build basically ground up services for the most part. And Google kind of falls somewhere in between in how they build their-- They're a sort of layer cake type effect, but not as many layers I guess you could say. >> I feel like, you know, Amazon's trying to be a platform for the ecosystem. Yes, they have their own products and they're going to sell. And that's going to drive their profitability cause they don't have to split the pie. But, they're taking a piece of-- They're spinning the meter, as Ziyas Caravalo likes to say on every time Snowflake or Databricks or Mongo or Atlas is, you know, running on their system. They take a piece of the action. Now, Microsoft does that as well. But, you look at Microsoft and security, head-to-head competitors, for example, with a CrowdStrike or an Okta in identity. Whereas, it seems like at least for now, AWS is a more friendly place for the ecosystem. At the same time, you do a lot of business in Microsoft. >> Yeah. And I think that a lot of companies have always feared that Amazon would just throw, you know, bodies at it. And I think that people have come to the realization that a two pizza team, as Amazon would call it, is eight people. I think that's, you know, two slices per person. I'm a little bit fat, so I don't know if that's enough. But, you start to look at it and go, okay, if they're going to start out with eight engineers, if I'm a startup and they're part of my ecosystem, do I really fear them or should I really embrace them and try to partner closer with them? And I think the smart people and the smart companies are partnering with them because they're realizing, Amazon, unless they can see it to, you know, a hundred million, $500 million market, they're not going to throw eight to 16 people at a problem. I think when, you know, you could say, you could look at the elastic with OpenSearch and what they did there. And the licensing terms and the battle they went through. But they knew that Elastic had a huge market. Also, you had a number of ecosystem companies building on top of now OpenSearch, that are now domain on top of Amazon as well. So, I think Amazon's being pretty strategic in how they're doing it. I think some of the-- It'll be interesting. I think this year is a payout year for the cuts that they're making to some of the services internally to kind of, you know, how do we take the fat off some of those services that-- You know, you look at Alexa. I don't know how much revenue Alexa really generates for them. But it's a means to an end for a number of different other services and partners. >> What do you make of this ChatGPT? I mean, Microsoft obviously is playing that card. You want to, you want ChatGPT in the Cloud, come to Azure. Seems like AWS has to respond. And we know Google is, you know, sharpening its knives to come up with its response. >> Yeah, I mean Google just went and talked about Bard for the first time this week and they're in private preview or I guess they call it beta, but. Right at the moment to select, select AI users, which I have no idea what that means. But that's a very interesting way that they're marketing it out there. But, I think that Amazon will have to respond. I think they'll be more measured than say, what Google's doing with Bard and just throwing it out there to, hey, we're going into beta now. I think they'll look at it and see where do we go and how do we actually integrate this in? Because they do have a lot of components of AI and ML underneath the hood that other services use. And I think that, you know, they've learned from that. And I think that they've already done a good job. Especially for media and entertainment when you start to look at some of the ways that they use it for helping do graphics and helping to do drones. I think part of their buy of iRobot was the fact that iRobot was a big user of RoboMaker, which is using different models to train those robots to go around objects and things like that, so. >> Quick touch on Kubernetes, the whole DevOps World we just covered. The Cloud Native Foundation Security, CNCF. The security conference up in Seattle last week. First time they spun that out kind of like reinforced, you know, AWS spins out, reinforced from reinvent. Amsterdam's coming up soon, the CubeCon. What should we expect? What's hot in Cubeland? >> Yeah, I think, you know, Kubes, you're going to be looking at how OpenShift keeps growing and I think to that respect you get to see the momentum with people like Red Hat. You see others coming up and realizing how OpenShift has gone to market as being, like you were saying, partnering with those Clouds and really making it simple. I think the simplicity and the manageability of Kubernetes is going to be at the forefront. I think a lot of the investment is still going into, how do I bring observability and DevOps and AIOps and MLOps all together. And I think that's going to be a big place where people are going to be looking to see what comes out of CubeCon in Amsterdam. I think it's that manageability ease of use. >> Well Rob, I look forward to working with you on behalf of the whole Cube team. We're going to do more of these and go out to some shows extract the signal from the noise. Really appreciate you coming into our studio. >> Well, thank you for having me on. Really appreciate it. >> You're really welcome. All right, keep it right there, or thanks for watching. This is Dave Vellante for the Cube. And we'll see you next time. (light music)

>> From the Cube Studios in Palo Alto in Boston bringing you data-driven insights from the Cube and ETR. This is Breaking Analysis with Dave Vellante. >> The unraveling of market enthusiasm continued in Q4 of 2022 with the earnings reports from the US hyperscalers, the big three now all in. As we said earlier this year, even the cloud is an immune from the macro headwinds and the cracks in the armor that we saw from the data that we shared last summer, they're playing out into 2023. For the most part actuals are disappointing beyond expectations including our own. It turns out that our estimates for the big three hyperscaler's revenue missed by 1.2 billion or 2.7% lower than we had forecast from even our most recent November estimates. And we expect continued decelerating growth rates for the hyperscalers through the summer of 2023 and we don't think that's going to abate until comparisons get easier. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we share our view of what's happening in cloud markets not just for the hyperscalers but other firms that have hitched a ride on the cloud. And we'll share new ETR data that shows why these trends are playing out tactics that customers are employing to deal with their cost challenges and how long the pain is likely to last. You know, riding the cloud wave, it's a two-edged sword. Let's look at the players that have gone all in on or are exposed to both the positive and negative trends of cloud. Look the cloud has been a huge tailwind for so many companies like Snowflake and Databricks, Workday, Salesforce, Mongo's move with Atlas, Red Hats Cloud strategy with OpenShift and so forth. And you know, the flip side is because cloud is elastic what comes up can also go down very easily. Here's an XY graphic from ETR that shows spending momentum or net score on the vertical axis and market presence in the dataset on the horizontal axis provision or called overlap. This is data from the January 2023 survey and that the red dotted lines show the positions of several companies that we've highlighted going back to January 2021. So let's unpack this for a bit starting with the big three hyperscalers. The first point is AWS and Azure continue to solidify their moat relative to Google Cloud platform. And we're going to get into this in a moment, but Azure and AWS revenues are five to six times that of GCP for IaaS. And at those deltas, Google should be gaining ground much faster than the big two. The second point on Google is notice the red line on GCP relative to its starting point. While it appears to be gaining ground on the horizontal axis, its net score is now below that of AWS and Azure in the survey. So despite its significantly smaller size it's just not keeping pace with the leaders in terms of market momentum. Now looking at AWS and Microsoft, what we see is basically AWS is holding serve. As we know both Google and Microsoft benefit from including SaaS in their cloud numbers. So the fact that AWS hasn't seen a huge downward momentum relative to a January 2021 position is one positive in the data. And both companies are well above that magic 40% line on the Y-axis, anything above 40% we consider to be highly elevated. But the fact remains that they're down as are most of the names on this chart. So let's take a closer look. I want to start with Snowflake and Databricks. Snowflake, as we reported from several quarters back came down to Earth, it was up in the 80% range in the Y-axis here. And it's still highly elevated in the 60% range and it continues to move to the right, which is positive but as we'll address in a moment it's customers can dial down consumption just as in any cloud. Now, Databricks is really interesting. It's not a public company, it never made it to IPO during the sort of tech bubble. So we don't have the same level of transparency that we do with other companies that did make it through. But look at how much more prominent it is on the X-axis relative to January 2021. And it's net score is basically held up over that period of time. So that's a real positive for Databricks. Next, look at Workday and Salesforce. They've held up relatively well, both inching to the right and generally holding their net scores. Same from Mongo, which is the brown dot above its name that says Elastic, it says a little gets a little crowded which Elastic's actually the blue dot above it. But generally, SaaS is harder to dial down, Workday, Salesforce, Oracles, SaaS and others. So it's harder to dial down because commitments have been made in advance, they're kind of locked in. Now, one of the discussions from last summer was as Mongo, less discretionary than analytics i.e. Snowflake. And it's an interesting debate but maybe Snowflake customers, you know, they're also generally committed to a dollar amount. So over time the spending is going to be there. But in the short term, yeah maybe Snowflake customers can dial down. Now that highlighted dotted red line, that bolded one is Datadog and you can see it's made major strides on the X-axis but its net score has decelerated quite dramatically. Openshift's momentum in the survey has dropped although IBM just announced that OpenShift has a a billion dollar ARR and I suspect what's happening there is IBM consulting is bundling OpenShift into its modernization projects. It's got a, that sort of captive base if you will. And as such it's probably not as top of mind to the respondents but I'll bet you the developers are certainly aware of it. Now the other really notable call out here is CloudFlare, We've reported on them earlier. Cloudflare's net score has held up really well since January of 2021. It really hasn't seen the downdraft of some of these others, but it's making major major moves to the right gaining market presence. We really like how CloudFlare is performing. And the last comment is on Oracle which as you can see, despite its much, much lower net score continues to gain ground in the market and thrive from a profitability standpoint. But the data pretty clearly shows that there's a downdraft in the market. Okay, so what's happening here? Let's dig deeper into this data. Here's a graphic from the most recent ETR drill down asking customers that said they were going to cut spending what technique they're using to do so. Now, as we've previously reported, consolidating redundant vendors is by far the most cited approach but there's two key points we want to make here. One is reducing excess cloud resources. As you can see in the bars is the second most cited technique and it's up from the previous polling period. The second we're not showing, you know directly but we've got some red call outs there. Reducing cloud costs jumps to 29% and 28% respectively in financial services and tech telco. And it's much closer to second. It's basically neck and neck with consolidating redundant vendors in those two industries. So they're being really aggressive about optimizing cloud cost. Okay, so as we said, cloud is great 'cause you can dial it up but it's just as easy to dial down. We've identified six factors that customers tell us are affecting their cloud consumption and there are probably more, if you got more we'd love to hear them but these are the ones that are fairly prominent that have hit our radar. First, rising mortgage rates mean banks are processing fewer loans means less cloud. The crypto crash means less trading activity and that means less cloud resources. Third lower ad spend has led companies to reduce not only you know, their ad buying but also their frequency of running their analytics and their calculations. And they're also often using less data, maybe compressing the timeframe of the corpus down to a shorter time period. Also very prominent is down to the bottom left, using lower cost compute instances. For example, Graviton from AWS or AMD chips and tiering storage to cheaper S3 or deep archived tiers. And finally, optimizing based on better pricing plans. So customers are moving from, you know, smaller companies in particular moving maybe from on demand or other larger companies that are experimenting using on demand or they're moving to spot pricing or reserved instances or optimized savings plans. That all lowers cost and that means less cloud resource consumption and less cloud revenue. Now in the days when everything was on prem CFOs, what would they do? They would freeze CapEx and IT Pros would have to try to do more with less and often that meant a lot of manual tasks. With the cloud it's much easier to move things around. It still takes some thinking and some effort but it's dramatically simpler to do so. So you can get those savings a lot faster. Now of course the other huge factor is you can cut or you can freeze. And this graphic shows data from a recent ETR survey with 159 respondents and you can see the meaningful uptick in hiring freezes, freezing new IT deployments and layoffs. And as we've been reporting, this has been trending up since earlier last year. And note the call out, this is especially prominent in retail sectors, all three of these techniques jump up in retail and that's a bit of a concern because oftentimes consumer spending helps the economy make a softer landing out of a pullback. But this is a potential canary in the coal mine. If retail firms are pulling back it's because consumers aren't spending as much. And so we're keeping a close eye on that. So let's boil this down to the market data and what this all means. So in this graphic we show our estimates for Q4 IaaS revenues compared to the "actual" IaaS revenues. And we say quote because AWS is the only one that reports, you know clean revenue and IaaS, Azure and GCP don't report actuals. Why would they? Because it would make them look even, you know smaller relative to AWS. Rather, they bury the figures in overall cloud which includes their, you know G-Suite for Google and all the Microsoft SaaS. And then they give us little tidbits about in Microsoft's case, Azure, they give growth rates. Google gives kind of relative growth of GCP. So, and we use survey data and you know, other data to try to really pinpoint and we've been covering this for, I don't know, five or six years ever since the cloud really became a thing. But looking at the data, we had AWS growing at 25% this quarter and it came in at 20%. So a significant decline relative to our expectations. AWS announced that it exited December, actually, sorry it's January data showed about a 15% mid-teens growth rate. So that's, you know, something we're watching. Azure was two points off our forecast coming in at 38% growth. It said it exited December in the 35% growth range and it said that it's expecting five points of deceleration off of that. So think 30% for Azure. GCP came in three points off our expectation coming in 35% and Alibaba has yet to report but we've shaved a bid off that forecast based on some survey data and you know what maybe 9% is even still not enough. Now for the year, the big four hyperscalers generated almost 160 billion of revenue, but that was 7 billion lower than what what we expected coming into 2022. For 2023, we're expecting 21% growth for a total of 193.3 billion. And while it's, you know, lower, you know, significantly lower than historical expectations it's still four to five times the overall spending forecast that we just shared with you in our predictions post of between 4 and 5% for the overall market. We think AWS is going to come in in around 93 billion this year with Azure closing in at over 71 billion. This is, again, we're talking IaaS here. Now, despite Amazon focusing investors on the fact that AWS's absolute dollar growth is still larger than its competitors. By our estimates Azure will come in at more than 75% of AWS's forecasted revenue. That's a significant milestone. AWS is operating margins by the way declined significantly this past quarter, dropping from 30% of revenue to 24%, 30% the year earlier to 24%. Now that's still extremely healthy and we've seen wild fluctuations like this before so I don't get too freaked out about that. But I'll say this, Microsoft has a marginal cost advantage relative to AWS because one, it has a captive cloud on which to run its massive software estate. So it can just throw software at its own cloud and two software marginal costs. Marginal economics despite AWS's awesomeness in high degrees of automation, software is just a better business. Now the upshot for AWS is the ecosystem. AWS is essentially in our view positioning very smartly as a platform for data partners like Snowflake and Databricks, security partners like CrowdStrike and Okta and Palo Alto and many others and SaaS companies. You know, Microsoft is more competitive even though AWS does have competitive products. Now of course Amazon's competitive to retail companies so that's another factor but generally speaking for tech players, Amazon is a really thriving ecosystem that is a secret weapon in our view. AWS happy to spin the meter with its partners even though it sells competitive products, you know, more so in our view than other cloud players. Microsoft, of course is, don't forget is hyping now, we're hearing a lot OpenAI and ChatGPT we reported last week in our predictions post. How OpenAI is shot up in terms of market sentiment in ETR's emerging technology company surveys and people are moving to Azure to get OpenAI and get ChatGPT that is a an interesting lever. Amazon in our view has to have a response. They have lots of AI and they're going to have to make some moves there. Meanwhile, Google is emphasizing itself as an AI first company. In fact, Google spent at least five minutes of continuous dialogue, nonstop on its AI chops during its latest earnings call. So that's an area that we're watching very closely as the buzz around large language models continues. All right, let's wrap up with some assumptions for 2023. We think SaaS players are going to continue to be sticky. They're going to be somewhat insulated from all these downdrafts because they're so tied in and customers, you know they make the commitment up front, you've got the lock in. Now having said that, we do expect some backlash over time on the onerous and generally customer unfriendly pricing models of most large SaaS companies. But that's going to play out over a longer period of time. Now for cloud generally and the hyperscalers specifically we do expect accelerating growth rates into Q3 but the amplitude of the demand swings from this rubber band economy, we expect to continue to compress and become more predictable throughout the year. Estimates are coming down, CEOs we think are going to be more cautious when the market snaps back more cautious about hiring and spending and as such a perhaps we expect a more orderly return to growth which we think will slightly accelerate in Q4 as comps get easier. Now of course the big risk to these scenarios is of course the economy, the FED, consumer spending, inflation, supply chain, energy prices, wars, geopolitics, China relations, you know, all the usual stuff. But as always with our partners at ETR and the Cube community, we're here for you. We have the data and we'll be the first to report when we see a change at the margin. Okay, that's a wrap for today. I want to thank Alex Morrison who's on production and manages the podcast, Ken Schiffman as well out of our Boston studio getting this up on LinkedIn Live. Thank you for that. Kristen Martin also and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hof is our Editor-in-Chief over at siliconangle.com. He does some great editing for us. Thank you all. Remember all these episodes are available as podcast. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com, at siliconangle.com where you can see all the data and you want to get in touch. Just all you can do is email me david.vellante@siliconangle.com or DM me @dvellante if you if you got something interesting, I'll respond. If you don't, it's either 'cause I'm swamped or it's just not tickling me. You can comment on our LinkedIn post as well. And please check out ETR.ai for the best survey data in the enterprise tech business. This is Dave Vellante for the Cube Insights powered by ETR. Thanks for watching and we'll see you next time on Breaking Analysis. (gentle upbeat music)

(upbeat music) >> Hello and welcome back to theCUBE's coverage of Cloud Native SecurityCon North America 2023. Its first inaugural event. It's theCUBE's coverage. We were there at the first event for a KubeCon before CNCF kind of took it over. It was in Seattle. And so in Seattle this week is Cloud Native SecurityCon. Of course, theCUBE is there covering via our Palo Alto Studios and our experts around the world who are bringing in Bassam Tabbara who's the CEO and founder of upbound.io. That's the URL, but Upbound is the company. The creators of Crossplane. Really kind of looking at the Crossplane, across the abstraction layer, across clouds. A big part of, as we call supercloud trend. Bassam, great to see you. You've been legend in the open source community. Great to have you on. >> Thanks, John. Always good to be on theCUBE. >> I really wanted to bring you in 'cause I want to get your perspective. You've seen the movie, you've seen open source software grow, it continues to grow. Now you're starting to see the Linux Foundation, which has CNCF really expanding their realm. They got the CloudNativeCon, KubeCon, which is Kubernetes event. That's gotten so massive and so successful. We've been to every single one as you know. I've seen you there and all of them as well. So that's going great. Now they got this new event that's spins out dedicated to security. Everybody wants to know why the new event? What's the focus? Is it needed? What will they do? What's different from KubeCon? Where do I play? And so there's a little bit of a question mark in the ecosystem around this event. And so we've been reporting on it. Looking good so far. People are buzzing, again, they're keeping it small. So that kind of managing expectations like any good event would do. But I think it's been successful, which I wanted like to get your take on how you see it. Is this good? Are you indifferent? Are you excited by this? What's your take? >> I mean, look, it's super exciting to see all the momentum around cloud native. Obviously there are different dimensions of cloud native securities, an important piece. Networking, storage, compute, like all those things I think tie back together and in some ways you can look at this event as a focused event on the security aspect as it relates to cloud native. And there are lots of vendors in this space. There's lots of interesting projects in the space, but the unifying theme is that they come together and probably around the Kubernetes API and the momentum around cloud native and with Kubernetes at the center of it. >> On the focus on Kubernetes, it seems this event is kind of classic security where you want to have deep dives. Again, I call it the event operating system 'cause you decouple, make things highly cohesive, and you link them together. I don't see a problem with it. I kind of like this. I gave it good reviews if they stay focused because security is super critical. There was references to bind and DNS. There's a lot of things in the infrastructure plumbing that need to be looked at or managed or figured out or just refactored for modernization needs. And I know you've done a lot with storage, for instance, storage, networking, kernel. There's a lot of things in the old tech or tech in the cloud that needs to be kind, I won't say rebooted, but maybe reset or jump. Do you see it that way? Are there things that need to get done or is it just that there's so much complexity in the different cloud cluster code thing going on? >> It's obviously security is a very, very big space and there are so many different aspects of it that people you can go into. I think the thing that's interesting around the cloud native community is that there is a unifying theme. Like forget the word cloud native for a second, but the unifying theme is that people are building around what looks like a standardized play around Kubernetes and the Kubernetes API. And as a result you can recast a lot of the technologies that we are used to in the past in a traditional security sense. You can recast them on top of this new standardized approach or on Kubernetes, whether it's policy or protecting a supply chain or scanning, or like a lot of the access control authorization, et cetera. All of those things can be either revived to apply to this cloud native play and the Kubernetes play or creating new opportunities for companies to actually build new and interesting projects and companies around a standardized play. >> Do you think this also will help the KubeCon be more focused around the developer areas there and just touching on security versus figuring out how to take something so important in KubeCon, which the stakeholders in KubeCon have have grown so big, I can see security sucking a lot of oxygen out of the room there. So here you move it over, you keep it over here. Will anything change on the KubeCon site? We'll be there in in Amsterdam in April. What do you think the impact will be? Good? Is it good for the community? Just good swim lanes? What's your take? >> Yeah, I still think KubeCon will be an umbrella event for the whole cloud native community. I suspect that you'll see some of the same vendors and projects and everything else represented in KubeCon. The way I think about all the branched cloud native events are essentially a way to have a more focused discussion, get people together to talk about security topics or networking topics or things that are more focused way. But I don't think it changes the the effect of KubeCon being the umbrella around all of it. So I think you'll see the same presence and maybe larger presence going forward at Amsterdam. We're planning to be there obviously and I'm excited to be there and I think it'll be a big event and having a smaller event is not going to diminish the effect of KubeCon. >> And if you look at the developer community they've all been online for a long time, from IRC chat to now Slack and now new technologies and stuff like Discord out there. The event world has changed post-pandemic. So it makes sense. And we're seeing this with all vendors, by the way, and projects. The digital community angle is huge because if you have a big tent event like KubeCon you can make that a rallying moment in the industry and then have similar smaller events that are highly focused that build off that that are just connective tissue or subnets, if you will, or communities targeted for really deeper conversations. And they could be smaller events. They don't have to be monster events, but they're connected and traverse into the main event. This might be the event format for the future for all companies, whether it's AWS or a company that has a community where you create this network effect, if you will, around the people. >> That's right. And if you look at things like AWS re:Invent, et cetera, I mean, that's a massive events. And in some ways it, if it was a set of smaller sub events, maybe it actually will flourish more. I don't know, I'm not sure. >> They just killed the San Francisco event. >> That's right. >> But they have re:Inforce, all right, so they just established that their big events are re:Invent and re:Inforce as their big. >> Oh, I didn't hear about re:Inforce. That's news to me. >> re:Inforce is their third event. So they're doing something similar as CloudNativeCon, which is you have to have an event and then they're going to create a lot of sub events underneath. So I think they are trying to do that. Very interesting. >> Very interesting for sure. >> So let's talk about what you guys are up to. I know from your standpoint, you had a lot of security conversations. How is Crossplane doing? Obviously, you saw our Supercloud coverage. You guys fit right into that model where clients, customers, enterprises are going to want to have multiple cloud operating environments for whatever the use case, whether you're using ChatGPT, you got to get an Azure instance up and running for that. Now with APIs, we're hearing a lot of developers doing that. So you're going to start to see this cross cloud as VMware calls, what we call it supercloud. There's more need for Crossplane like thinking. What's the update? >> For sure, and we see this very clearly as well. So the fact that there is a standardization layer, there is a layer that lets you converge the different vendors that you have, the different clouds that you have, the different hype models that you have, whether it's hybrid or private, public, et cetera. The unifying theme is that you're literally bringing all those things under one control plane that enables you to actually centralize and standardize on security, access control, helps you standardize on cost control, quota policy, as well as create a self-service experience for your developers. And so from a security standpoint, the beauty of this is like, you could use really popular projects like open policy agent or Kyverno or others if you want to do policy and do so uniformly across your entire stack, your entire footprint of tooling, vendors, services and across deployment models. Those things are possible because you're standardizing and consolidating on a control plane on top of all. And that's the thing that gets our customers excited. That we're seeing in the community that they could actually now normalize standardize on small number of projects and tools to manage everything. >> We were talking about that in our summary of the keynote yesterday. Dave Vellante and I were talking about the idea of clients want to have a redo of their security. They've been, just the tooling has been building up. They got zero trust in place, maybe with some big vendor, but now got the cloud native opportunity to refactor and reset and reinvent their security paradigm. And so that's the positive thing we're hearing. Now we're seeing enterprises want this cross cloud capabilities or Crossplane like thinking that you guys are talking about. What are your customers telling you? Can you share from an enterprise perspective where they're at in this journey? Because part of the security problems that we've been reporting on has been because clients are moving from IT to cloud native and not everyone's moved over yet. So they're highly vulnerable to ransomware and all kinds of other crap. So another attacks, so they're wide open, But people who are moving into cloud native, are they stepping up their game on this Crossplane opportunity? Where are they at? Can you share data on that? >> Yeah, we're grateful to be talking to a lot of customers these days. And the interesting thing is even if you talked about large financial institutions, banks, et cetera, the common theme that we hear is that they bought tools for each of the different departments and however they're organized. Sometimes you see the folks that are running databases, networking, being separated from say, the computer app developers or they're all these different departments within an organization. And for each one of those, they've made localized decisions for tooling and services that they bought. What we're seeing now consistently is that they're all together, getting together, and trying to figure out how to standardize on a smaller one set of tooling and services that goes across all the different departments and all different aspects of the business that they're running. And this is where this discussion gets a lot very interesting. If instead of buying a different policy tool for each department, or once that fits it you could actually standardize on policy or the entire footprint of services that they're managing. And you get that by standardizing on a control plane or standardizing on effectively one point of control for everything that they're doing. And that theme is like literally, it gets all our customers excited. This is why they're engaging in all of this. It's almost the holy grail. The thing that I've been trying to do for a long time. >> I know. >> And it's finally happening. >> I know you and I have talked about this many times, but I got to ask you the one thing that jumps into everybody's head when you hear control plane is lock-in. So how do you discuss that lock-in, perception from the reality of the situation? How do you unpack that for the customer? 'Cause they want choice at the end of the day. There's the preferred vendors for sure on the hyperscale side and app side and open source, but what's the lock-in? What does the lock-in conversation look like? Or do they even have that conversation? >> Yeah. To be honest, I mean, so their lock-in could be a two dimensions here. Most of our customers and people are using Crossplane or using app on product around it. Most of our do, concentrated in, say a one cloud vendor and have others. So I don't think this is necessarily about multicloud per se or being locked into one vendor. But they do manage many different services and they have legacy tooling and they have different systems that they bought at different stages and they want to bring them all together. And by bringing them all together that helps them make choices about consulting or even replacing some of them. But right now everything is siloed, everything is separate, both organizationally as well as the code bases or investments and tooling or contracts. Everything is just completely separated and it requires humans to put them together. And organizations actually try to gather around and put them together. I don't know if lock-in is the driving goal for this, but it is standardization consolidation. That's the driving initiative. >> And so unification and building is the big driver. They're building out >> Correct, and you can ask why are they doing that? What does standardization help with? It helps them to become more productive. They can move faster, they can innovate faster. Not as a ton of, like literally revenue written all over. So it's super important to them that they achieved this, increase their pace of innovation around this and they do that by standardizing. >> The great point in all this and your success at Upbound and now CNCF success with KubeCon + CloudNativeCon and now with the inaugural event of Cloud Native SecurityCon is that the customers are involved, a lot of end users are involved. There's a big driver not only from the industry and the developers and getting architecture right and having choice. The customers want this to happen. They're leaning in, they're part of it. So that's a big driver. Where does this go? If you had to throw a dart at the board five years from now Cloud Native SecurityCon, what does it look like if you had to predict the trajectory of this event and community? >> Yeah, I mean, look, I think the trajectory one is that we have what looks like a standardization layer emerging that is all encompassing. And as a result, there is a ton of opportunity for vendors, projects, communities to build around within on top of this layer. And essentially create, I think you talked about an operating system earlier and decentralized aspect of this, but it's an opportunity to actually, what it looks like for the first time we have a convergence happening industry-wide and through open source and open source foundations. And I think that means that there'll be new opportunity and lots of new projects and things that are created in the space. And it also means that if you don't attach this space, you'll likely be left out. >> Awesome. Bassam, great to have you on, great expert commentary, obviously multi CUBE alumni and supporter of theCUBE and as you become successful we really appreciate your support for helping us get the content out there. And best of luck to your team and thanks for weighing in on Cloud Native SecurityCon. >> Awesome. It's always good talking to you, John. Thank you. >> Great stuff. This is more CUBE coverage from Palo Alto, getting folks on the ground on location, getting us the stories in Seattle. Of course, Cloud Native SecurityCon, the inaugural event, which looks like will be the beginning of a series of multi-year journey for the CNCF, focusing on security. Of course, theCUBE's here to cover it, every angle of it, and extract the signal from the noise. I'm John Furrier, thanks for watching. (upbeat 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)

(upbeat music beginning) >> From the Cube Studios in Palo Alto and Boston, bringing you data-driven insights from the Cube and ETR, this is "Breaking Analysis" with Dave Vellante. >> Making predictions about the future of enterprise tech is more challenging if you strive to lay down forecasts that are measurable. In other words, if you make a prediction, you should be able to look back a year later and say, with some degree of certainty, whether the prediction came true or not, with evidence to back that up. Hello and welcome to this week's Wikibon Cube Insights, powered by ETR. In this breaking analysis, we aim to do just that, with predictions about the macro IT spending environment, cost optimization, security, lots to talk about there, generative AI, cloud, and of course supercloud, blockchain adoption, data platforms, including commentary on Databricks, snowflake, and other key players, automation, events, and we may even have some bonus predictions around quantum computing, and perhaps some other areas. To make all this happen, we welcome back, for the third year in a row, my colleague and friend Eric Bradley from ETR. Eric, thanks for all you do for the community, and thanks for being part of this program. Again. >> I wouldn't miss it for the world. I always enjoy this one. Dave, good to see you. >> Yeah, so let me bring up this next slide and show you, actually come back to me if you would. I got to show the audience this. These are the inbounds that we got from PR firms starting in October around predictions. They know we do prediction posts. And so they'll send literally thousands and thousands of predictions from hundreds of experts in the industry, technologists, consultants, et cetera. And if you bring up the slide I can show you sort of the pattern that developed here. 40% of these thousands of predictions were from cyber. You had AI and data. If you combine those, it's still not close to cyber. Cost optimization was a big thing. Of course, cloud, some on DevOps, and software. Digital... Digital transformation got, you know, some lip service and SaaS. And then there was other, it's kind of around 2%. So quite remarkable, when you think about the focus on cyber, Eric. >> Yeah, there's two reasons why I think it makes sense, though. One, the cybersecurity companies have a lot of cash, so therefore the PR firms might be working a little bit harder for them than some of their other clients. (laughs) And then secondly, as you know, for multiple years now, when we do our macro survey, we ask, "What's your number one spending priority?" And again, it's security. It just isn't going anywhere. It just stays at the top. So I'm actually not that surprised by that little pie chart there, but I was shocked that SaaS was only 5%. You know, going back 10 years ago, that would've been the only thing anyone was talking about. >> Yeah. So true. All right, let's get into it. First prediction, we always start with kind of tech spending. Number one is tech spending increases between four and 5%. ETR has currently got it at 4.6% coming into 2023. This has been a consistently downward trend all year. We started, you know, much, much higher as we've been reporting. Bottom line is the fed is still in control. They're going to ease up on tightening, is the expectation, they're going to shoot for a soft landing. But you know, my feeling is this slingshot economy is going to continue, and it's going to continue to confound, whether it's supply chains or spending. The, the interesting thing about the ETR data, Eric, and I want you to comment on this, the largest companies are the most aggressive to cut. They're laying off, smaller firms are spending faster. They're actually growing at a much larger, faster rate as are companies in EMEA. And that's a surprise. That's outpacing the US and APAC. Chime in on this, Eric. >> Yeah, I was surprised on all of that. First on the higher level spending, we are definitely seeing it coming down, but the interesting thing here is headlines are making it worse. The huge research shop recently said 0% growth. We're coming in at 4.6%. And just so everyone knows, this is not us guessing, we asked 1,525 IT decision-makers what their budget growth will be, and they came in at 4.6%. Now there's a huge disparity, as you mentioned. The Fortune 500, global 2000, barely at 2% growth, but small, it's at 7%. So we're at a situation right now where the smaller companies are still playing a little bit of catch up on digital transformation, and they're spending money. The largest companies that have the most to lose from a recession are being more trepidatious, obviously. So they're playing a "Wait and see." And I hope we don't talk ourselves into a recession. Certainly the headlines and some of their research shops are helping it along. But another interesting comment here is, you know, energy and utilities used to be called an orphan and widow stock group, right? They are spending more than anyone, more than financials insurance, more than retail consumer. So right now it's being driven by mid, small, and energy and utilities. They're all spending like gangbusters, like nothing's happening. And it's the rest of everyone else that's being very cautious. >> Yeah, so very unpredictable right now. All right, let's go to number two. Cost optimization remains a major theme in 2023. We've been reporting on this. You've, we've shown a chart here. What's the primary method that your organization plans to use? You asked this question of those individuals that cited that they were going to reduce their spend and- >> Mhm. >> consolidating redundant vendors, you know, still leads the way, you know, far behind, cloud optimization is second, but it, but cloud continues to outpace legacy on-prem spending, no doubt. Somebody, it was, the guy's name was Alexander Feiglstorfer from Storyblok, sent in a prediction, said "All in one becomes extinct." Now, generally I would say I disagree with that because, you know, as we know over the years, suites tend to win out over, you know, individual, you know, point products. But I think what's going to happen is all in one is going to remain the norm for these larger companies that are cutting back. They want to consolidate redundant vendors, and the smaller companies are going to stick with that best of breed and be more aggressive and try to compete more effectively. What's your take on that? >> Yeah, I'm seeing much more consolidation in vendors, but also consolidation in functionality. We're seeing people building out new functionality, whether it's, we're going to talk about this later, so I don't want to steal too much of our thunder right now, but data and security also, we're seeing a functionality creep. So I think there's further consolidation happening here. I think niche solutions are going to be less likely, and platform solutions are going to be more likely in a spending environment where you want to reduce your vendors. You want to have one bill to pay, not 10. Another thing on this slide, real quick if I can before I move on, is we had a bunch of people write in and some of the answer options that aren't on this graph but did get cited a lot, unfortunately, is the obvious reduction in staff, hiring freezes, and delaying hardware, were three of the top write-ins. And another one was offshore outsourcing. So in addition to what we're seeing here, there were a lot of write-in options, and I just thought it would be important to state that, but essentially the cost optimization is by and far the highest one, and it's growing. So it's actually increased in our citations over the last year. >> And yeah, specifically consolidating redundant vendors. And so I actually thank you for bringing that other up, 'cause I had asked you, Eric, is there any evidence that repatriation is going on and we don't see it in the numbers, we don't see it even in the other, there was, I think very little or no mention of cloud repatriation, even though it might be happening in this in a smattering. >> Not a single mention, not one single mention. I went through it for you. Yep. Not one write-in. >> All right, let's move on. Number three, security leads M&A in 2023. Now you might say, "Oh, well that's a layup," but let me set this up Eric, because I didn't really do a great job with the slide. I hid the, what you've done, because you basically took, this is from the emerging technology survey with 1,181 responses from November. And what we did is we took Palo Alto and looked at the overlap in Palo Alto Networks accounts with these vendors that were showing on this chart. And Eric, I'm going to ask you to explain why we put a circle around OneTrust, but let me just set it up, and then have you comment on the slide and take, give us more detail. We're seeing private company valuations are off, you know, 10 to 40%. We saw a sneak, do a down round, but pretty good actually only down 12%. We've seen much higher down rounds. Palo Alto Networks we think is going to get busy. Again, they're an inquisitive company, they've been sort of quiet lately, and we think CrowdStrike, Cisco, Microsoft, Zscaler, we're predicting all of those will make some acquisitions and we're thinking that the targets are somewhere in this mess of security taxonomy. Other thing we're predicting AI meets cyber big time in 2023, we're going to probably going to see some acquisitions of those companies that are leaning into AI. We've seen some of that with Palo Alto. And then, you know, your comment to me, Eric, was "The RSA conference is going to be insane, hopping mad, "crazy this April," (Eric laughing) but give us your take on this data, and why the red circle around OneTrust? Take us back to that slide if you would, Alex. >> Sure. There's a few things here. First, let me explain what we're looking at. So because we separate the public companies and the private companies into two separate surveys, this allows us the ability to cross-reference that data. So what we're doing here is in our public survey, the tesis, everyone who cited some spending with Palo Alto, meaning they're a Palo Alto customer, we then cross-reference that with the private tech companies. Who also are they spending with? So what you're seeing here is an overlap. These companies that we have circled are doing the best in Palo Alto's accounts. Now, Palo Alto went and bought Twistlock a few years ago, which this data slide predicted, to be quite honest. And so I don't know if they necessarily are going to go after Snyk. Snyk, sorry. They already have something in that space. What they do need, however, is more on the authentication space. So I'm looking at OneTrust, with a 45% overlap in their overall net sentiment. That is a company that's already existing in their accounts and could be very synergistic to them. BeyondTrust as well, authentication identity. This is something that Palo needs to do to move more down that zero trust path. Now why did I pick Palo first? Because usually they're very inquisitive. They've been a little quiet lately. Secondly, if you look at the backdrop in the markets, the IPO freeze isn't going to last forever. Sooner or later, the IPO markets are going to open up, and some of these private companies are going to tap into public equity. In the meantime, however, cash funding on the private side is drying up. If they need another round, they're not going to get it, and they're certainly not going to get it at the valuations they were getting. So we're seeing valuations maybe come down where they're a touch more attractive, and Palo knows this isn't going to last forever. Cisco knows that, CrowdStrike, Zscaler, all these companies that are trying to make a push to become that vendor that you're consolidating in, around, they have a chance now, they have a window where they need to go make some acquisitions. And that's why I believe leading up to RSA, we're going to see some movement. I think it's going to pretty, a really exciting time in security right now. >> Awesome. Thank you. Great explanation. All right, let's go on the next one. Number four is, it relates to security. Let's stay there. Zero trust moves from hype to reality in 2023. Now again, you might say, "Oh yeah, that's a layup." A lot of these inbounds that we got are very, you know, kind of self-serving, but we always try to put some meat in the bone. So first thing we do is we pull out some commentary from, Eric, your roundtable, your insights roundtable. And we have a CISO from a global hospitality firm says, "For me that's the highest priority." He's talking about zero trust because it's the best ROI, it's the most forward-looking, and it enables a lot of the business transformation activities that we want to do. CISOs tell me that they actually can drive forward transformation projects that have zero trust, and because they can accelerate them, because they don't have to go through the hurdle of, you know, getting, making sure that it's secure. Second comment, zero trust closes that last mile where once you're authenticated, they open up the resource to you in a zero trust way. That's a CISO of a, and a managing director of a cyber risk services enterprise. Your thoughts on this? >> I can be here all day, so I'm going to try to be quick on this one. This is not a fluff piece on this one. There's a couple of other reasons this is happening. One, the board finally gets it. Zero trust at first was just a marketing hype term. Now the board understands it, and that's why CISOs are able to push through it. And what they finally did was redefine what it means. Zero trust simply means moving away from hardware security, moving towards software-defined security, with authentication as its base. The board finally gets that, and now they understand that this is necessary and it's being moved forward. The other reason it's happening now is hybrid work is here to stay. We weren't really sure at first, large companies were still trying to push people back to the office, and it's going to happen. The pendulum will swing back, but hybrid work's not going anywhere. By basically on our own data, we're seeing that 69% of companies expect remote and hybrid to be permanent, with only 30% permanent in office. Zero trust works for a hybrid environment. So all of that is the reason why this is happening right now. And going back to our previous prediction, this is why we're picking Palo, this is why we're picking Zscaler to make these acquisitions. Palo Alto needs to be better on the authentication side, and so does Zscaler. They're both fantastic on zero trust network access, but they need the authentication software defined aspect, and that's why we think this is going to happen. One last thing, in that CISO round table, I also had somebody say, "Listen, Zscaler is incredible. "They're doing incredibly well pervading the enterprise, "but their pricing's getting a little high," and they actually think Palo Alto is well-suited to start taking some of that share, if Palo can make one move. >> Yeah, Palo Alto's consolidation story is very strong. Here's my question and challenge. Do you and me, so I'm always hardcore about, okay, you've got to have evidence. I want to look back at these things a year from now and say, "Did we get it right? Yes or no?" If we got it wrong, we'll tell you we got it wrong. So how are we going to measure this? I'd say a couple things, and you can chime in. One is just the number of vendors talking about it. That's, but the marketing always leads the reality. So the second part of that is we got to get evidence from the buying community. Can you help us with that? >> (laughs) Luckily, that's what I do. I have a data company that asks thousands of IT decision-makers what they're adopting and what they're increasing spend on, as well as what they're decreasing spend on and what they're replacing. So I have snapshots in time over the last 11 years where I can go ahead and compare and contrast whether this adoption is happening or not. So come back to me in 12 months and I'll let you know. >> Now, you know, I will. Okay, let's bring up the next one. Number five, generative AI hits where the Metaverse missed. Of course everybody's talking about ChatGPT, we just wrote last week in a breaking analysis with John Furrier and Sarjeet Joha our take on that. We think 2023 does mark a pivot point as natural language processing really infiltrates enterprise tech just as Amazon turned the data center into an API. We think going forward, you're going to be interacting with technology through natural language, through English commands or other, you know, foreign language commands, and investors are lining up, all the VCs are getting excited about creating something competitive to ChatGPT, according to (indistinct) a hundred million dollars gets you a seat at the table, gets you into the game. (laughing) That's before you have to start doing promotion. But he thinks that's what it takes to actually create a clone or something equivalent. We've seen stuff from, you know, the head of Facebook's, you know, AI saying, "Oh, it's really not that sophisticated, ChatGPT, "it's kind of like IBM Watson, it's great engineering, "but you know, we've got more advanced technology." We know Google's working on some really interesting stuff. But here's the thing. ETR just launched this survey for the February survey. It's in the field now. We circle open AI in this category. They weren't even in the survey, Eric, last quarter. So 52% of the ETR survey respondents indicated a positive sentiment toward open AI. I added up all the sort of different bars, we could double click on that. And then I got this inbound from Scott Stevenson of Deep Graham. He said "AI is recession-proof." I don't know if that's the case, but it's a good quote. So bring this back up and take us through this. Explain this chart for us, if you would. >> First of all, I like Scott's quote better than the Facebook one. I think that's some sour grapes. Meta just spent an insane amount of money on the Metaverse and that's a dud. Microsoft just spent money on open AI and it is hot, undoubtedly hot. We've only been in the field with our current ETS survey for a week. So my caveat is it's preliminary data, but I don't care if it's preliminary data. (laughing) We're getting a sneak peek here at what is the number one net sentiment and mindshare leader in the entire machine-learning AI sector within a week. It's beating Data- >> 600. 600 in. >> It's beating Databricks. And we all know Databricks is a huge established enterprise company, not only in machine-learning AI, but it's in the top 10 in the entire survey. We have over 400 vendors in this survey. It's number eight overall, already. In a week. This is not hype. This is real. And I could go on the NLP stuff for a while. Not only here are we seeing it in open AI and machine-learning and AI, but we're seeing NLP in security. It's huge in email security. It's completely transforming that area. It's one of the reasons I thought Palo might take Abnormal out. They're doing such a great job with NLP in this email side, and also in the data prep tools. NLP is going to take out data prep tools. If we have time, I'll discuss that later. But yeah, this is, to me this is a no-brainer, and we're already seeing it in the data. >> Yeah, John Furrier called, you know, the ChatGPT introduction. He said it reminded him of the Netscape moment, when we all first saw Netscape Navigator and went, "Wow, it really could be transformative." All right, number six, the cloud expands to supercloud as edge computing accelerates and CloudFlare is a big winner in 2023. We've reported obviously on cloud, multi-cloud, supercloud and CloudFlare, basically saying what multi-cloud should have been. We pulled this quote from Atif Kahn, who is the founder and CTO of Alkira, thanks, one of the inbounds, thank you. "In 2023, highly distributed IT environments "will become more the norm "as organizations increasingly deploy hybrid cloud, "multi-cloud and edge settings..." Eric, from one of your round tables, "If my sources from edge computing are coming "from the cloud, that means I have my workloads "running in the cloud. "There is no one better than CloudFlare," That's a senior director of IT architecture at a huge financial firm. And then your analysis shows CloudFlare really growing in pervasion, that sort of market presence in the dataset, dramatically, to near 20%, leading, I think you had told me that they're even ahead of Google Cloud in terms of momentum right now. >> That was probably the biggest shock to me in our January 2023 tesis, which covers the public companies in the cloud computing sector. CloudFlare has now overtaken GCP in overall spending, and I was shocked by that. It's already extremely pervasive in networking, of course, for the edge networking side, and also in security. This is the number one leader in SaaSi, web access firewall, DDoS, bot protection, by your definition of supercloud, which we just did a couple of weeks ago, and I really enjoyed that by the way Dave, I think CloudFlare is the one that fits your definition best, because it's bringing all of these aspects together, and most importantly, it's cloud agnostic. It does not need to rely on Azure or AWS to do this. It has its own cloud. So I just think it's, when we look at your definition of supercloud, CloudFlare is the poster child. >> You know, what's interesting about that too, is a lot of people are poo-pooing CloudFlare, "Ah, it's, you know, really kind of not that sophisticated." "You don't have as many tools," but to your point, you're can have those tools in the cloud, Cloudflare's doing serverless on steroids, trying to keep things really simple, doing a phenomenal job at, you know, various locations around the world. And they're definitely one to watch. Somebody put them on my radar (laughing) a while ago and said, "Dave, you got to do a breaking analysis on CloudFlare." And so I want to thank that person. I can't really name them, 'cause they work inside of a giant hyperscaler. But- (Eric laughing) (Dave chuckling) >> Real quickly, if I can from a competitive perspective too, who else is there? They've already taken share from Akamai, and Fastly is their really only other direct comp, and they're not there. And these guys are in poll position and they're the only game in town right now. I just, I don't see it slowing down. >> I thought one of your comments from your roundtable I was reading, one of the folks said, you know, CloudFlare, if my workloads are in the cloud, they are, you know, dominant, they said not as strong with on-prem. And so Akamai is doing better there. I'm like, "Okay, where would you want to be?" (laughing) >> Yeah, which one of those two would you rather be? >> Right? Anyway, all right, let's move on. Number seven, blockchain continues to look for a home in the enterprise, but devs will slowly begin to adopt in 2023. You know, blockchains have got a lot of buzz, obviously crypto is, you know, the killer app for blockchain. Senior IT architect in financial services from your, one of your insight roundtables said quote, "For enterprises to adopt a new technology, "there have to be proven turnkey solutions. "My experience in talking with my peers are, "blockchain is still an open-source component "where you have to build around it." Now I want to thank Ravi Mayuram, who's the CTO of Couchbase sent in, you know, one of the predictions, he said, "DevOps will adopt blockchain, specifically Ethereum." And he referenced actually in his email to me, Solidity, which is the programming language for Ethereum, "will be in every DevOps pro's playbook, "mirroring the boom in machine-learning. "Newer programming languages like Solidity "will enter the toolkits of devs." His point there, you know, Solidity for those of you don't know, you know, Bitcoin is not programmable. Solidity, you know, came out and that was their whole shtick, and they've been improving that, and so forth. But it, Eric, it's true, it really hasn't found its home despite, you know, the potential for smart contracts. IBM's pushing it, VMware has had announcements, and others, really hasn't found its way in the enterprise yet. >> Yeah, and I got to be honest, I don't think it's going to, either. So when we did our top trends series, this was basically chosen as an anti-prediction, I would guess, that it just continues to not gain hold. And the reason why was that first comment, right? It's very much a niche solution that requires a ton of custom work around it. You can't just plug and play it. And at the end of the day, let's be very real what this technology is, it's a database ledger, and we already have database ledgers in the enterprise. So why is this a priority to move to a different database ledger? It's going to be very niche cases. I like the CTO comment from Couchbase about it being adopted by DevOps. I agree with that, but it has to be a DevOps in a very specific use case, and a very sophisticated use case in financial services, most likely. And that's not across the entire enterprise. So I just think it's still going to struggle to get its foothold for a little bit longer, if ever. >> Great, thanks. Okay, let's move on. Number eight, AWS Databricks, Google Snowflake lead the data charge with Microsoft. Keeping it simple. So let's unpack this a little bit. This is the shared accounts peer position for, I pulled data platforms in for analytics, machine-learning and AI and database. So I could grab all these accounts or these vendors and see how they compare in those three sectors. Analytics, machine-learning and database. Snowflake and Databricks, you know, they're on a crash course, as you and I have talked about. They're battling to be the single source of truth in analytics. They're, there's going to be a big focus. They're already started. It's going to be accelerated in 2023 on open formats. Iceberg, Python, you know, they're all the rage. We heard about Iceberg at Snowflake Summit, last summer or last June. Not a lot of people had heard of it, but of course the Databricks crowd, who knows it well. A lot of other open source tooling. There's a company called DBT Labs, which you're going to talk about in a minute. George Gilbert put them on our radar. We just had Tristan Handy, the CEO of DBT labs, on at supercloud last week. They are a new disruptor in data that's, they're essentially making, they're API-ifying, if you will, KPIs inside the data warehouse and dramatically simplifying that whole data pipeline. So really, you know, the ETL guys should be shaking in their boots with them. Coming back to the slide. Google really remains focused on BigQuery adoption. Customers have complained to me that they would like to use Snowflake with Google's AI tools, but they're being forced to go to BigQuery. I got to ask Google about that. AWS continues to stitch together its bespoke data stores, that's gone down that "Right tool for the right job" path. David Foyer two years ago said, "AWS absolutely is going to have to solve that problem." We saw them start to do it in, at Reinvent, bringing together NoETL between Aurora and Redshift, and really trying to simplify those worlds. There's going to be more of that. And then Microsoft, they're just making it cheap and easy to use their stuff, you know, despite some of the complaints that we hear in the community, you know, about things like Cosmos, but Eric, your take? >> Yeah, my concern here is that Snowflake and Databricks are fighting each other, and it's allowing AWS and Microsoft to kind of catch up against them, and I don't know if that's the right move for either of those two companies individually, Azure and AWS are building out functionality. Are they as good? No they're not. The other thing to remember too is that AWS and Azure get paid anyway, because both Databricks and Snowflake run on top of 'em. So (laughing) they're basically collecting their toll, while these two fight it out with each other, and they build out functionality. I think they need to stop focusing on each other, a little bit, and think about the overall strategy. Now for Databricks, we know they came out first as a machine-learning AI tool. They were known better for that spot, and now they're really trying to play catch-up on that data storage compute spot, and inversely for Snowflake, they were killing it with the compute separation from storage, and now they're trying to get into the MLAI spot. I actually wouldn't be surprised to see them make some sort of acquisition. Frank Slootman has been a little bit quiet, in my opinion there. The other thing to mention is your comment about DBT Labs. If we look at our emerging technology survey, last survey when this came out, DBT labs, number one leader in that data integration space, I'm going to just pull it up real quickly. It looks like they had a 33% overall net sentiment to lead data analytics integration. So they are clearly growing, it's fourth straight survey consecutively that they've grown. The other name we're seeing there a little bit is Cribl, but DBT labs is by far the number one player in this space. >> All right. Okay, cool. Moving on, let's go to number nine. With Automation mixer resurgence in 2023, we're showing again data. The x axis is overlap or presence in the dataset, and the vertical axis is shared net score. Net score is a measure of spending momentum. As always, you've seen UI path and Microsoft Power Automate up until the right, that red line, that 40% line is generally considered elevated. UI path is really separating, creating some distance from Automation Anywhere, they, you know, previous quarters they were much closer. Microsoft Power Automate came on the scene in a big way, they loom large with this "Good enough" approach. I will say this, I, somebody sent me a results of a (indistinct) survey, which showed UiPath actually had more mentions than Power Automate, which was surprising, but I think that's not been the case in the ETR data set. We're definitely seeing a shift from back office to front soft office kind of workloads. Having said that, software testing is emerging as a mainstream use case, we're seeing ML and AI become embedded in end-to-end automations, and low-code is serving the line of business. And so this, we think, is going to increasingly have appeal to organizations in the coming year, who want to automate as much as possible and not necessarily, we've seen a lot of layoffs in tech, and people... You're going to have to fill the gaps with automation. That's a trend that's going to continue. >> Yep, agreed. At first that comment about Microsoft Power Automate having less citations than UiPath, that's shocking to me. I'm looking at my chart right here where Microsoft Power Automate was cited by over 60% of our entire survey takers, and UiPath at around 38%. Now don't get me wrong, 38% pervasion's fantastic, but you know you're not going to beat an entrenched Microsoft. So I don't really know where that comment came from. So UiPath, looking at it alone, it's doing incredibly well. It had a huge rebound in its net score this last survey. It had dropped going through the back half of 2022, but we saw a big spike in the last one. So it's got a net score of over 55%. A lot of people citing adoption and increasing. So that's really what you want to see for a name like this. The problem is that just Microsoft is doing its playbook. At the end of the day, I'm going to do a POC, why am I going to pay more for UiPath, or even take on another separate bill, when we know everyone's consolidating vendors, if my license already includes Microsoft Power Automate? It might not be perfect, it might not be as good, but what I'm hearing all the time is it's good enough, and I really don't want another invoice. >> Right. So how does UiPath, you know, and Automation Anywhere, how do they compete with that? Well, the way they compete with it is they got to have a better product. They got a product that's 10 times better. You know, they- >> Right. >> they're not going to compete based on where the lowest cost, Microsoft's got that locked up, or where the easiest to, you know, Microsoft basically give it away for free, and that's their playbook. So that's, you know, up to UiPath. UiPath brought on Rob Ensslin, I've interviewed him. Very, very capable individual, is now Co-CEO. So he's kind of bringing that adult supervision in, and really tightening up the go to market. So, you know, we know this company has been a rocket ship, and so getting some control on that and really getting focused like a laser, you know, could be good things ahead there for that company. Okay. >> One of the problems, if I could real quick Dave, is what the use cases are. When we first came out with RPA, everyone was super excited about like, "No, UiPath is going to be great for super powerful "projects, use cases." That's not what RPA is being used for. As you mentioned, it's being used for mundane tasks, so it's not automating complex things, which I think UiPath was built for. So if you were going to get UiPath, and choose that over Microsoft, it's going to be 'cause you're doing it for more powerful use case, where it is better. But the problem is that's not where the enterprise is using it. The enterprise are using this for base rote tasks, and simply, Microsoft Power Automate can do that. >> Yeah, it's interesting. I've had people on theCube that are both Microsoft Power Automate customers and UiPath customers, and I've asked them, "Well you know, "how do you differentiate between the two?" And they've said to me, "Look, our users and personal productivity users, "they like Power Automate, "they can use it themselves, and you know, "it doesn't take a lot of, you know, support on our end." The flip side is you could do that with UiPath, but like you said, there's more of a focus now on end-to-end enterprise automation and building out those capabilities. So it's increasingly a value play, and that's going to be obviously the challenge going forward. Okay, my last one, and then I think you've got some bonus ones. Number 10, hybrid events are the new category. Look it, if I can get a thousand inbounds that are largely self-serving, I can do my own here, 'cause we're in the events business. (Eric chuckling) Here's the prediction though, and this is a trend we're seeing, the number of physical events is going to dramatically increase. That might surprise people, but most of the big giant events are going to get smaller. The exception is AWS with Reinvent, I think Snowflake's going to continue to grow. So there are examples of physical events that are growing, but generally, most of the big ones are getting smaller, and there's going to be many more smaller intimate regional events and road shows. These micro-events, they're going to be stitched together. Digital is becoming a first class citizen, so people really got to get their digital acts together, and brands are prioritizing earned media, and they're beginning to build their own news networks, going direct to their customers. And so that's a trend we see, and I, you know, we're right in the middle of it, Eric, so you know we're going to, you mentioned RSA, I think that's perhaps going to be one of those crazy ones that continues to grow. It's shrunk, and then it, you know, 'cause last year- >> Yeah, it did shrink. >> right, it was the last one before the pandemic, and then they sort of made another run at it last year. It was smaller but it was very vibrant, and I think this year's going to be huge. Global World Congress is another one, we're going to be there end of Feb. That's obviously a big big show, but in general, the brands and the technology vendors, even Oracle is going to scale down. I don't know about Salesforce. We'll see. You had a couple of bonus predictions. Quantum and maybe some others? Bring us home. >> Yeah, sure. I got a few more. I think we touched upon one, but I definitely think the data prep tools are facing extinction, unfortunately, you know, the Talons Informatica is some of those names. The problem there is that the BI tools are kind of including data prep into it already. You know, an example of that is Tableau Prep Builder, and then in addition, Advanced NLP is being worked in as well. ThoughtSpot, Intelius, both often say that as their selling point, Tableau has Ask Data, Click has Insight Bot, so you don't have to really be intelligent on data prep anymore. A regular business user can just self-query, using either the search bar, or even just speaking into what it needs, and these tools are kind of doing the data prep for it. I don't think that's a, you know, an out in left field type of prediction, but it's the time is nigh. The other one I would also state is that I think knowledge graphs are going to break through this year. Neo4j in our survey is growing in pervasion in Mindshare. So more and more people are citing it, AWS Neptune's getting its act together, and we're seeing that spending intentions are growing there. Tiger Graph is also growing in our survey sample. I just think that the time is now for knowledge graphs to break through, and if I had to do one more, I'd say real-time streaming analytics moves from the very, very rich big enterprises to downstream, to more people are actually going to be moving towards real-time streaming, again, because the data prep tools and the data pipelines have gotten easier to use, and I think the ROI on real-time streaming is obviously there. So those are three that didn't make the cut, but I thought deserved an honorable mention. >> Yeah, I'm glad you did. Several weeks ago, we did an analyst prediction roundtable, if you will, a cube session power panel with a number of data analysts and that, you know, streaming, real-time streaming was top of mind. So glad you brought that up. Eric, as always, thank you very much. I appreciate the time you put in beforehand. I know it's been crazy, because you guys are wrapping up, you know, the last quarter survey in- >> Been a nuts three weeks for us. (laughing) >> job. I love the fact that you're doing, you know, the ETS survey now, I think it's quarterly now, right? Is that right? >> Yep. >> Yep. So that's phenomenal. >> Four times a year. I'll be happy to jump on with you when we get that done. I know you were really impressed with that last time. >> It's unbelievable. This is so much data at ETR. Okay. Hey, that's a wrap. Thanks again. >> Take care Dave. Good seeing you. >> All right, many thanks to our team here, Alex Myerson as production, he manages the podcast force. Ken Schiffman as well is a critical component of our East Coast studio. Kristen Martin and Cheryl Knight help get the word out on social media and in our newsletters. And Rob Hoof is our editor-in-chief. He's at siliconangle.com. He's just a great editing for us. Thank you all. Remember all these episodes that are available as podcasts, wherever you listen, podcast is doing great. Just search "Breaking analysis podcast." Really appreciate you guys listening. I publish each week on wikibon.com and siliconangle.com, or you can email me directly if you want to get in touch, david.vellante@siliconangle.com. That's how I got all these. I really appreciate it. I went through every single one with a yellow highlighter. It took some time, (laughing) but I appreciate it. You could DM me at dvellante, or comment on our LinkedIn post and please check out etr.ai. Its data is amazing. Best survey data in the enterprise tech business. This is Dave Vellante for theCube Insights, powered by ETR. Thanks for watching, and we'll see you next time on "Breaking Analysis." (upbeat music beginning) (upbeat music ending)