>> 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) >> Hey, everyone, welcome back to Las Vegas, "theCube" is on our fourth day of covering AWS re:Invent, live from the Venetian Expo Center. This week has been amazing. We've created a ton of content, as you know, 'cause you've been watching. But, there's been north of 55,000 people here, hundreds of thousands online. We've had amazing conversations across the AWS ecosystem. Lisa Martin, Paul Gillan. Paul, what's your, kind of, take on day four of the conference? It's still highly packed. >> Oh, there's lots of people here. (laughs) >> Yep. Unusual for the final day of a conference. I think Werner Vogels, if I'm pronouncing it right kicked things off today when he talked about asymmetry and how the world is, you know, asymmetric. We build symmetric software, because it's convenient to do so, but asymmetric software actually scales and evolves much better. And I think that that was a conversation starter for a lot of what people are talking about here today, which is how the cloud changes the way we think about building software. >> Absolutely does. >> Our next guest, Holger Mueller, that's one of his key areas of focus. And Holger, welcome, thanks for joining us on the "theCube". >> Thanks for having me. >> What did you take away from the keynote this morning? >> Well, how do you feel on the final day of the marathon, right? We're like 23, 24 miles. Hit the ball yesterday, right? >> We are going strong Holger. And, of course, >> Yeah. >> you guys, we can either talk about business transformation with cloud or the World Cup. >> Or we can do both. >> The World Cup, hands down. World Cup. (Lisa laughs) Germany's out, I'm unbiased now. They just got eliminated. >> Spain is out now. >> What will the U.S. do against Netherlands tomorrow? >> They're going to win. What's your forecast? U.S. will win? >> They're going to win 2 to 1. >> What do you say, 2:1? >> I'm optimistic, but realistic. >> 3? >> I think Netherlands. >> Netherlands will win? >> 2 to nothing. >> Okay, I'll vote for the U.S.. >> Okay, okay >> 3:1 for the U.S.. >> Be optimistic. >> Root for the U.S.. >> Okay, I like that. >> Hope for the best wherever you work. >> Tomorrow you'll see how much soccer experts we are. >> If your prediction was right. (laughs) >> (laughs) Ja, ja. Or yours was right, right, so. Cool, no, but the event, I think the event is great to have 50,000 people. Biggest event of the year again, right? Not yet the 70,000 we had in 2019. But it's great to have the energy. I've never seen the show floor going all the way down like this, right? >> I haven't either. >> I've never seen that. I think it's a record. Often vendors get the space here and they have the keynote area, and the entertainment area, >> Yeah. >> and the food area, and then there's an exposition, right? This is packed. >> It's packed. >> Maybe it'll pay off. >> You don't see the big empty booths that you often see. >> Oh no. >> Exactly, exactly. You know, the white spaces and so on. >> No. >> Right. >> Which is a good thing. >> There's lots of energy, which is great. And today's, of course, the developer day, like you said before, right now Vogels' a rockstar in the developer community, right. Revered visionary on what has been built, right? And he's becoming a little professorial is my feeling, right. He had these moments before too, when it was justifying how AWS moved off the Oracle database about the importance of data warehouses and structures and why DynamoDB is better and so on. But, he had a large part of this too, and this coming right across the keynotes, right? Adam Selipsky talking about Antarctica, right? Scott against almonds and what went wrong. He didn't tell us, by the way, which often the tech winners forget. Scott banked on technology. He had motorized sleds, which failed after three miles. So, that's not the story to tell the technology. Let everything down. Everybody went back to ponies and horses and dogs. >> Maybe goes back to these asynchronous behavior. >> Yeah. >> The way of nature. >> And, yesterday, Swami talking about the bridges, right? The root bridges, right? >> Right. >> So, how could Werner pick up with his video at the beginning. >> Yeah. >> And then talk about space and other things? So I think it's important to educate about event-based architecture, right? And we see this massive transformation. Modern software has to be event based, right? Because, that's how things work and we didn't think like this before. I see this massive transformation in my other research area in other platforms about the HR space, where payrolls are being rebuilt completely. And payroll used to be one of the three peaks of ERP, right? You would size your ERP machine before the cloud to financial close, to run the payroll, and to do an MRP manufacturing run if you're manufacturing. God forbid you run those three at the same time. Your machine wouldn't be able to do that, right? So it was like start the engine, start the boosters, we are running payroll. And now the modern payroll designs like you see from ADP or from Ceridian, they're taking every payroll relevant event. You check in time wise, right? You go overtime, you take a day of vacation and right away they trigger and run the payroll, so it's up to date for you, up to date for you, which, in this economy, is super important, because we have more gig workers, we have more contractors, we have employees who are leaving suddenly, right? The great resignation, which is happening. So, from that perspective, it's the modern way of building software. So it's great to see Werner showing that. The dirty little secrets though is that is more efficient software for the cloud platform vendor too. Takes less resources, gets less committed things, so it's a much more scalable architecture. You can move the events, you can work asynchronously much better. And the biggest showcase, right? What's the biggest transactional showcase for an eventually consistent asynchronous transactional application? I know it's a mouthful, but we at Amazon, AWS, Amazon, right? You buy something on Amazon they tell you it's going to come tomorrow. >> Yep. >> They don't know it's going to come tomorrow by that time, because it's not transactionally consistent, right? We're just making every ERP vendor, who lives in transactional work, having nightmares of course, (Lisa laughs) but for them it's like, yes we have the delivery to promise, a promise to do that, right? But they come back to you and say, "Sorry, we couldn't make it, delivery didn't work and so on. It's going to be a new date. We are out of the product.", right? So these kind of event base asynchronous things are more and more what's going to scale around the world. It's going to be efficient for everybody, it's going to be better customer experience, better employee experience, ultimately better user experience, it's going to be better for the enterprise to build, but we have to learn to build it. So big announcement was to build our environment to build better eventful applications from today. >> Talk about... This is the first re:Invent... Well, actually, I'm sorry, it's the second re:Invent under Adam Selipsky. >> Right. Adam Selipsky, yep. >> But his first year. >> Right >> We're hearing a lot of momentum. What's your takeaway with what he delivered with the direction Amazon is going, their vision? >> Ja, I think compared to the Jassy times, right, we didn't see the hockey stick slide, right? With a number of innovations and releases. That was done in 2019 too, right? So I think it's a more pedestrian pace, which, ultimately, is good for everybody, because it means that when software vendors go slower, they do less width, but more depth. >> Yeah. >> And depth is what customers need. So Amazon's building more on the depth side, which is good news. I also think, and that's not official, right, but Adam Selipsky came from Tableau, right? >> Yeah. So he is a BI analytics guy. So it's no surprise we have three data lake offerings, right? Security data lake, we have a healthcare data lake and we have a supply chain data lake, right? Where all, again, the epigonos mentioned them I was like, "Oh, my god, Amazon's coming to supply chain.", but it's actually data lakes, which is an interesting part. But, I think it's not a surprise that someone who comes heavily out of the analytics BI world, it's off ringside, if I was pitching internally to him maybe I'd do something which he's is familiar with and I think that's what we see in the major announcement of his keynote on Tuesday. >> I mean, speaking of analytics, one of the big announcements early on was Amazon is trying to bridge the gap between Aurora. >> Yep. >> And Redshift. >> Right. >> And setting up for continuous pipelines, continuous integration. >> Right. >> Seems to be a trend that is common to all database players. I mean, Oracle is doing the same thing. SAP is doing the same thing. MariaDB. Do you see the distinction between transactional and analytical databases going away? >> It's coming together, right? Certainly coming together, from that perspective, but there's a fundamental different starting point, right? And with the big idea part, right? The universal database, which does everything for you in one system, whereas the suite of specialized databases, right? Oracle is in the classic Oracle database in the universal database camp. On the other side you have Amazon, which built a database. This is one of the first few Amazon re:Invents. It's my 10th where there was no new database announced. Right? >> No. >> So it was always add another one specially- >> I think they have enough. >> It's a great approach. They have enough, right? So it's a great approach to build something quick, which Amazon is all about. It's not so great when customers want to leverage things. And, ultimately, which I think with Selipsky, AWS is waking up to the enterprise saying, "I have all this different database and what is in them matters to me." >> Yeah. >> "So how can I get this better?" So no surprise between the two most popular database, Aurora and RDS. They're bring together the data with some out of the box parts. I think it's kind of, like, silly when Swami's saying, "Hey, no ETL.". (chuckles) Right? >> Yeah. >> There shouldn't be an ETL from the same vendor, right? There should be data pipes from that perspective anyway. So it looks like, on the overall value proposition database side, AWS is moving closer to the universal database on the Oracle side, right? Because, if you lift, of course, the universal database, under the hood, you see, well, there's different database there, different part there, you do something there, you have to configure stuff, which is also the case but it's one part of it, right, so. >> With that shift, talk about the value that's going to be in it for customers regardless of industry. >> Well, the value for customers is great, because when software vendors, or platform vendors, go in depth, you get more functionality, you get more maturity you get easier ways of setting up the whole things. You get ways of maintaining things. And you, ultimately, get lower TCO to build them, which is super important for enterprise. Because, here, this is the developer cloud, right? Developers love AWS. Developers are scarce, expensive. Might not be want to work for you, right? So developer velocity getting more done with same amount of developers, getting less done, less developers getting more done, is super crucial, super important. So this is all good news for enterprise banking on AWS and then providing them more efficiency, more automation, out of the box. >> Some of your customer conversations this week, talk to us about some of the feedback. What's the common denominator amongst customers right now? >> Customers are excited. First of all, like, first event, again in person, large, right? >> Yeah. >> People can travel, people meet each other, meet in person. They have a good handle around the complexity, which used to be a huge challenge in the past, because people say, "Do I do this?" I know so many CXOs saying, "Yeah, I want to build, say, something in IoT with AWS. The first reference built it like this, the next reference built it completely different. The third one built it completely different again. So now I'm doubting if my team has the skills to build things successfully, because will they be smart enough, like your teams, because there's no repetitiveness and that repetitiveness is going to be very important for AWS to come up with some higher packaging and version numbers.", right? But customers like that message. They like that things are working better together. They're not missing the big announcement, right? One of the traditional things of AWS would be, and they made it even proud, as a system, Jassy was saying, "If we look at the IT spend and we see something which is, like, high margin for us and not served well and we announced something there, right?" So Quick Start, Workspaces, where all liaisons where AWS went after traditional IT spend and had an offering. We haven't had this in 2019, we don't have them in 2020. Last year and didn't have it now. So something is changing on the AWS side. It's a little bit too early to figure out what, but they're not chewing off as many big things as they used in the past. >> Right. >> Yep. >> Did you get the sense that... Keith Townsend, from "The CTO Advisor", was on earlier. >> Yep. >> And he said he's been to many re:Invents, as you have, and he said that he got the sense that this is Amazon's chance to do a victory lap, as he called it. That this is a way for Amazon to reinforce the leadership cloud. >> Ja. >> And really, kind of, establish that nobody can come close to them, nobody can compete with them. >> You don't think that- >> I don't think that's at all... I mean, love Keith, he's a great guy, but I don't think that's the mindset at all, right? So, I mean, Jassy was always saying, "It's still the morning of the day in the cloud.", right? They're far away from being done. They're obsessed over being right. They do more work with the analysts. We think we got something right. And I like the passion, from that perspective. So I think Amazon's far from being complacent and the area, which is the biggest bit, right, the biggest. The only thing where Amazon truly has floundered, always floundered, is the AI space, right? So, 2018, Werner Vogels was doing more technical stuff that "Oh, this is all about linear regression.", right? And Amazon didn't start to put algorithms on silicon, right? And they have a three four trail and they didn't announce anything new here, behind Google who's been doing this for much, much longer than TPU platform, so. >> But they have now. >> They're keen aware. >> Yep. >> They now have three, or they own two of their own hardware platforms for AI. >> Right. >> They support the Intel platform. They seem to be catching up in that area. >> It's very hard to catch up on hardware, right? Because, there's release cycles, right? And just the volume that, just talking about the largest models that we have right now, to do with the language models, and Google is just doing a side note of saying, "Oh, we supported 50 less or 30 less, not little spoken languages, which I've never even heard of, because they're under banked and under supported and here's the language model, right? And I think it's all about little bit the organizational DNA of a company. I'm a strong believer in that. And, you have to remember AWS comes from the retail side, right? >> Yeah. >> Their roll out of data centers follows their retail strategy. Open secret, right? But, the same thing as the scale of the AI is very very different than if you take a look over at Google where it makes sense of the internet, right? The scale right away >> Right. >> is a solution, which is a good solution for some of the DNA of AWS. Also, Microsoft Azure is good. There has no chance to even get off the ship of that at Google, right? And these leaders with Google and it's not getting smaller, right? We didn't hear anything. I mean so much focused on data. Why do they focus so much on data? Because, data is the first step for AI. If AWS was doing a victory lap, data would've been done. They would own data, right? They would have a competitor to BigQuery Omni from the Google side to get data from the different clouds. There's crickets on that topic, right? So I think they know that they're catching up on the AI side, but it's really, really hard. It's not like in software where you can't acquire someone they could acquire in video. >> Not at Core Donovan. >> Might play a game, but that's not a good idea, right? So you can't, there's no shortcuts on the hardware side. As much as I'm a software guy and love software and don't like hardware, it's always a pain, right? There's no shortcuts there and there's nothing, which I think, has a new Artanium instance, of course, certainly, but they're not catching up. The distance is the same, yep. >> One of the things is funny, one of our guests, I think it was Tuesday, it was, it was right after Adam's keynote. >> Sure. >> Said that Adam Selipsky stood up on stage and talked about data for 52 minutes. >> Yeah. Right. >> It was timed, 52 minutes. >> Right. >> Huge emphasis on that. One of the things that Adam said to John Furrier when they were able to sit down >> Yeah >> a week or so ago at an event preview, was that CIOs and CEOs are not coming to Adam to talk about technology. They want to talk about transformation. They want to talk about business transformation. >> Sure, yes, yes. >> Talk to me in our last couple of minutes about what CEOs and CIOs are coming to you saying, "Holger, help us figure this out. We have to transform the business." >> Right. So we advise, I'm going quote our friends at Gartner, once the type A company. So we'll use technology aggressively, right? So take everything in the audience with a grain of salt, followers are the laggards, and so on. So for them, it's really the cusp of doing AI, right? Getting that data together. It has to be in the cloud. We live in the air of infinite computing. The cloud makes computing infinite, both from a storage, from a compute perspective, from an AI perspective, and then define new business models and create new best practices on top of that. Because, in the past, everything was fine out on premise, right? We talked about the (indistinct) size. Now in the cloud, it's just the business model to say, "Do I want to have a little more AI? Do I want a to run a little more? Will it give me the insight in the business?". So, that's the transformation that is happening, really. So, bringing your data together, this live conversation data, but not for bringing the data together. There's often the big win for the business for the first time to see the data. AWS is banking on that. The supply chain product, as an example. So many disparate systems, bring them them together. Big win for the business. But, the win for the business, ultimately, is when you change the paradigm from the user showing up to do something, to software doing stuff for us, right? >> Right. >> We have too much in this operator paradigm. If the user doesn't show up, doesn't find the click, doesn't find where to go, nothing happens. It can't be done in the 21st century, right? Software has to look over your shoulder. >> Good point. >> Understand one for you, autonomous self-driving systems. That's what CXOs, who're future looking, will be talked to come to AWS and all the other cloud vendors. >> Got it, last question for you. We're making a sizzle reel on Instagram. >> Yeah. >> If you had, like, a phrase, like, or a 30 second pitch that would describe re:Invent 2022 in the direction the company's going. What would that elevator pitch say? >> 30 second pitch? >> Yeah. >> All right, just timing. AWS is doing well. It's providing more depth, less breadth. Making things work together. It's catching up in some areas, has some interesting offerings, like the healthcare offering, the security data lake offering, which might change some things in the industry. It's staying the course and it's going strong. >> Ah, beautifully said, Holger. Thank you so much for joining Paul and me. >> Might have been too short. I don't know. (laughs) >> About 10 seconds left over. >> It was perfect, absolutely perfect. >> Thanks for having me. >> Perfect sizzle reel. >> Appreciate it. >> We appreciate your insights, what you're seeing this week, and the direction the company is going. We can't wait to see what happens in the next year. And, yeah. >> Thanks for having me. >> And of course, we've been on so many times. We know we're going to have you back. (laughs) >> Looking forward to it, thank you. >> All right, for Holger Mueller and Paul Gillan, I'm Lisa Martin. You're watching "theCube", the leader in live enterprise and emerging tech coverage. (upbeat music)

>>Hey guys and girls, welcome back to The Cube's Live coverage of AWS Reinvent 22 from Sin City. We've been here, this is our third day of coverage. We started Monday night first. Full day of the show was yesterday. Big news yesterday. Big news. Today we're hearing north of 50,000 people, and I'm hearing hundreds of thousands online. We've been having great conversations with AWS folks in the ecosystem, AWS customers, partners, ISVs, you name it. We're pleased to welcome back one of our alumni to the program, talking about partner ecosystem. Dominic Rav Vida joins us, the VP of Developer relations at single store. It's so great to have you on the program. Dominic. Thanks for coming. >>Thanks. Great. Great to see you >>Again. Great to see you too. We go way back. >>We do, yeah. >>So let's talk about reinvent 22. This is the 11th reinvent. Yeah. What are some of the things that you've heard this week that are exciting that are newsworthy from single stores perspective? >>I think in particular what we heard AWS announce on the zero ETL between Aurora and Redshift, I think it's, it's significant in that AWS has provided lots of services for building blocks for applications for a long time. And that's a great amount of flexibility for developers. But there are cases where, you know, it's a common thing to need to move data from transactional systems to analytics systems and making that easy with zero etl, I think it's a significant thing and in general we see in the market and especially in the data management market in the cloud, a unification of different types of workloads. So I think that's a step in the right direction for aws. And I think for the market as a whole, why it's significant for single store is, that's our specialty in particular, is to unify transactions and analytics for realtime applications and analytics. When you've got customer facing analytic applications and you need low latency data from realtime streaming data sources and you've gotta crunch and compute that. Those are diverse types of workloads over document transactional workloads as well as, you know, analytical workloads of various shapes and the data types could be diverse from geospatial time series. And then you've gotta serve that because we're all living in this digital service first world and you need that relevant, consistent, fresh data. And so that unification is what we think is like the big thing in data right >>Now. So validation for single store, >>It does feel like that. I mean, I'd say in the recent like six months, you've seen announcements from Google with Alloy db basically adding the complement to their workload types. You see it with Snowflake adding the complement to their traditional analytical workload site. You see it with Mongo and others. And yeah, we do feel it was validation cuz at single store we completed the functionality for what we call universal storage, which is, is the industry's first third type of storage after row store and column store, single store dbs, universal storage, unifies those. So on a single copy of data you can form these diverse workloads. And that was completed three years ago. So we sort of see like, you know, we're onto something >>Here. Welcome to the game guys. >>That's right. >>What's the value in that universal storage for customers, whether it's a healthcare organization, a financial institution, what's the value in it in those business outcomes that you guys are really helping to fuel? >>I think in short, if there were like a, a bumper sticker for that message, it's like, are you ready for the next interaction? The next interaction with your customer, the next interaction with your supply chain partner, the next interaction with your internal stakeholders, your operational managers being ready for that interaction means you've gotta have the historical data at the ready, accessible, efficiently accessible, and and, and queryable along with the most recent fresh data. And that's the context that's expected and be able to serve that instantaneously. So being ready for that next interaction is what single store helps companies do. >>Talk about single store helping customers. You know, every company these days has to be a data company. I always think, whether it's my grocery store that has all my information and helps keep me fed or a gas station or a car dealer or my bank. And we've also here, one of the things that John Furrier got to do, and he does this every year before aws, he gets to sit down with the CEO and gets really kind of a preview of what's gonna happen at at the show, right? And Adams Lisky said to him some interesting very poignant things. One is that that data, we talk about data democratization, but he says the role of the data analyst is gonna go away. Or that maybe that term in, in that every person within an organization, whether you're marketing, sales, ops, finance, is going to be analyzing data for their jobs to become data driven. Right? How does single store help customers really become data companies, especially powering data intensive apps like I know you do. >>Yeah, that's, there's a lot of talk about that and, and I think there's a lot of work that's been done with companies to make that easier to analyze data in all these different job functions. While we do that, it's not really our starting point because, and our starting point is like operationalizing that analytics as part of the business. So you can think of it in terms of database terms. Like is it batch analysis? Batch analytics after the fact, what happened last week? What happened last month? That's a lot of what those data teams are doing and those analysts are doing. What single store focuses more is in putting those insights into action for the business operations, which typically is more on the application side, it's the API side, you might call it a data product. If you're monetizing your data and you're transacting with that providing as an api, or you're delivering it as software as a service, and you're providing an end-to-end function for, you know, our marketing marketer, then we help power those kinds of real time data applications that have the interactivity and have that customer touchpoint or that partner touchpoint. So you can say we sort of, we put the data in action in that way. >>And that's the most, one of the most important things is putting data in action. If it's, it can be gold, it can be whatever you wanna call it, but if you can't actually put it into action, act on insights in real time, right? The value goes way down or there's liability, >>Right? And I think you have to do that with privacy in mind as well, right? And so you have to take control of that data and use it for your business strategy And the way that you can do that, there's technology like single store makes that possible in ways that weren't possible before. And I'll give you an example. So we have a, a customer named Fathom Analytics. They provide web analytics for marketers, right? So if you're in marketing, you understand this use case. Any demand gen marketer knows that they want to see what the traffic that hits their site is. What are the page views, what are the click streams, what are the sequences? Have these visitors to my website hit certain goals? So the big name in that for years of course has been Google Analytics and that's a free service. And you interact with that and you can see how your website's performing. >>So what Fathom does is a privacy first alternative to Google Analytics. And when you think about, well, how is that possible that they, and as a paid service, it's as software, as a service, how, first of all, how can you keep up with that real time deluge of clickstream data at the rate that Google Analytics can do it? That's the technical problem. But also at the data layer, how could you keep up with Google has, you know, in terms of databases And Fathom's answer to that is to use single store. Their, their prior architecture had four different types of database technologies under the hood. They were using Redis to have fast read time cash. They were using MySEQ database as the application database they were using. They were looking at last search to do full tech search. And they were using DynamoDB as part of a another kind of fast look up fast cash. They replaced all four of those with single store. And, and again, what they're doing is like sort of battling defacto giant in Google Analytics and having a great success at doing that for posting tens of thousands of websites. Some big names that you've heard of as well. >>I can imagine that's a big reduction from four to one, four x reduction in databases. The complexities that go away, the simplification that happens, I can imagine is quite huge for them. >>And we've done a study, an independent study with Giga Home Research. We published this back in June looking at total cost of ownership with benchmarks and the relevant benchmarks for transactions and analytics and databases are tpcc for transactions, TPC H for analytics, TPC DS for analytics. And we did a TCO study using those benchmark datas on a combination of transactional and analytical databases together and saw some pretty big improvements. 60% improvement over Myse Snowflake, for >>Instance. Awesome. Big business outcomes. We only have a few seconds left, so you've already given me a bumper sticker. Yeah. And I know I live in Silicon Valley, I've seen those billboards. I know single store has done some cheeky billboard marketing campaigns. But if you had a new billboard to create from your perspective about single store, what does it say? >>I, I think it's that, are you, are you ready for the next interaction? Because business is won and lost in every moment, in every location, in every digital moment passing by. And if you're not ready to, to interact and transact rather your systems on your behalf, then you're behind the curve. It's easy to be displaced people swipe left and pick your competitor. So I think that's the next bumper sticker. I may, I would say our, my favorite billboard so far of what we've run is cover your SaaS, which is what is how, what is the data layer to, to manage the next level of SaaS applications, the next generation. And we think single store is a big part >>Of that. Cover your SaaS. Love it. Dominic, thank you so much for joining me, giving us an update on single store from your perspective, what's going on there, kind of really where you are in the market. We appreciate that. We'll have to >>Have you back. Thank you. Glad to >>Be here. All right. For Dominic rta, I'm Lisa Martin. You're watching The Cube, the leader in live, emerging and enterprise tech coverage.

(calm electronic music) >> Okay, welcome back everyone to theCUBE's coverage here live in New York City for AWS Summit 2022. I'm John Furrier, host of theCUBE, but a great conversation here as the day winds down. First of all, 10,000 plus people, this is a big event, just New York City. So sign of the times that some headwinds are happening? I don't think so, not in the cloud enterprise innovation game. Lot going on, this innovation conversation we're going to have now is about the confluence of cloud scale integration data and the future of how FinTech and other markets are going to change with technology. We got Chris Thomas, the CTO of Slalom, and Rob Krugman, chief digital officer at Broadridge. Gentlemen, thanks for coming on theCUBE. >> Thanks for having us. >> So we had a talk before we came on camera about your firm, what you guys do, take a quick minute to just give the scope and size of your firm and what you guys work on. >> Yeah, so Broadridge is a global financial FinTech company. We work on, part of our business is capital markets and wealth, and that's about a third of our business, about $7 trillion a day clearing through our platforms. And then the other side of our business is communications where we help all different types of organizations communicate with their shareholders, communicate with their customers across a variety of different digital channels and capabilities. >> Yeah, and Slalom, give a quick one minute on Slalom. I know you guys, but for the folks that don't know you. >> Yeah, no problem. So Slalom is a modern consulting firm focused on strategy, technology, and business transformation. And me personally, I'm part of the element lab, which is focused on forward thinking technology and disruptive technology in the next five to 10 years. >> Awesome, and that's the scope of this conversation. The next five to 10 years, you guys are working on a project together, you're kind of customer partners. You're building something. What are you guys working on? I can't wait to jump into it, explain. >> Sure, so similar to Chris, at Broadridge, we've created innovation capability, innovation incubation capability, and one of the first areas we're experimenting in is digital assets. So what we're looking to do is we're looking at a variety of different areas where we think consolidation network effects that we could bring can add a significant amount of value. And so the area we're working on is this concept of a wallet of wallets. How do we actually consolidate assets that are held across a variety of different wallets, maybe traditional locations- >> Digital wallets. >> Digital wallets, but maybe even traditional accounts, bring that together and then give control back to the consumer of who they want to share that information with, how they want their transactions to be able to control. So the idea of, people talk about Web 3 being the internet of value. I often think about it as the internet of control. How do you return control back to the individual so that they can make decisions about how and who has access to their information and assets? >> It's interesting, I totally like the value angle, but your point is what's the chicken and the egg here, the cart before the horse, you can look at it both ways and say, okay, control is going to drive the value. This is an interesting nuance, right? >> Yes, absolutely. >> So in this architectural world, they thought about the data plane and the control plane. Everyone's trying to go old school, middleware thinking. Let's own the data plane, we'll win everything. Not going to happen if it goes decentralized, right, Chris? >> Yeah, yeah. I mean, we're building a decentralized application, but it really is built on top of AWS. We have a serverless architecture that scales as our business scales built on top of things like S3, Lambda, DynamoDB, and of course using those security principles like Cognito and AWS Gateway, API Gateway. So we're really building an architecture of Web 3 on top of the Web 2 basics in the cloud. >> I mean, all evolutions are abstractions on top of each other, IG, DNS, Key, it goes the whole nine yards. In digital, at least, that's the way. Question about serverless real quick. I saw that Redshift just launched general availability of serverless in Redshift? >> Yes. >> You're starting to see the serverless now part of almost all the services in AWS. Is that enabling that abstraction, because most people don't see it that way. They go, oh, well, Amazon's not Web 3. They got databases, you could use that stuff. So how do you connect the dots and cross the bridge to the future with the idea that I might not think Web 2 or cloud is Web 3? >> I'll jump in quick. I mean, I think it's the decentralize. If you think about decentralization. serverless and decentralization, you could argue are the same way of, they're saying the same thing in different ways. One is thinking about it from a technology perspective. One is thinking about it from an ecosystem perspective and how things come together. You need serverless components that can talk to each other and communicate with each other to actually really reach the promise of what Web 3 is supposed to be. >> So digital bits or digital assets, I call it digital bits, 'cause I think zero ones. If you digitize everything and everything has value or now control drives the value. I could be a soccer team. I have apparel, I have value in my logos, I have photos, I have CUBE videos. I mean some say that this should be an NFT. Yeah, right, maybe, but digital assets have to be protected, but owned. So ownership drives it too, right? >> Absolutely. >> So how does that fit in, how do you explain that? 'Cause I'm trying to tie the dots here, connect the dots and tie it together. What do I get if I go down this road that you guys are building? >> So I think one of the challenges of digital assets right now is that it's a closed community. And I think the people that play in it, they're really into it. And so you look at things like NFTs and you look at some of the other activities that are happening and there are certain naysayers that look at it and say, this stuff is not based upon value. It's a bunch of artwork, it can't be worth this. Well, how about we do a time out there and we actually look at the underlying technology that's supporting this, the blockchain, and the potential ramifications of that across the entire financial ecosystem, and frankly, all different types of ecosystems of having this immutable record, where information gets stored and gets sent and the ability to go back to it at all times, that's where the real power is. So I think we're starting to see. We've hit a bit of a hiccup, if you will, in the cryptocurrencies. They're going to continue to be there. They won't all be there. A lot of them will probably disappear, but they'll be a finite number. >> What percentage of stuff do you think is vapor BS? If you had to pick an order of magnitude number. >> (laughs) I would say at least 75% of it. (John laughs) >> I mean, there's quite a few projects that are failing right now, but it's interesting in that in the crypto markets, they're failing gracefully. Because it's on the blockchain and it's all very transparent. Things are checked, you know immediately which companies are insolvent and which opportunities are still working. So it's very, very interesting in my opinion. >> Well, and I think the ones that don't have valid premises are the ones that are failing. Like Terra and some of these other ones, if you actually really looked at it, the entire industry knew these things were no good. But then you look at stable coins. And you look at what's going on with CBDCs. These are backed by real underlying assets that people can be comfortable with. And there's not a question of, is this going to happen? The question is, how quickly is it going to happen and how quickly are we going to be using digital currencies? >> It's interesting, we always talk about software, software as money now, money is software and gold and oil's moving over to that crypto. How do you guys see software? 'Cause we were just arguing in the queue, Dave Vellante and I, before you guys came on that the software industry pretty much does not exist anymore, it's open source. So everything's open source as an industry, but the value is integration, innovation. So it's not just software, it's the free. So you got to, it's integration. So how do you guys see this software driving crypto? Because it is software defined money at the end of the day. It's a token. >> No, I think that's absolutely one of the strengths of the crypto markets and the Web 3 market is it's governed by software. And because of that, you can build a trust framework. Everybody knows it's on the public blockchain. Everybody's aware of the software that's driving the rules and the rules of engagement in this blockchain. And it creates that trust network that says, hey, I can transact with you even though I don't know anything about you and I don't need a middleman to tell me I can trust you. Because this software drives that trust framework. >> Lot of disruption, lot of companies go out of business as a middleman in these markets. >> Listen, the intermediaries either have to disrupt themselves or they will be disrupted. I think that's what we're going to learn here. And it's going to start in financial services, but it's going to go to a lot of different places. I think the interesting thing that's happening now is for the first time, you're starting to see the regulators start to get involved. Which is actually a really good thing for the market. Because to Chris's point, transparency is here, how do you actually present that transparency and that trust back to consumers so they feel comfortable once that problem is solved. And I think everyone in the industry welcomes it. All of a sudden you have this ecosystem that people can play in, they can build and they can start to actually create real value. >> Every structural change that I've been involved in my 30 plus year career has been around inflection points. There was always some sort of underbelly. So I'm not going to judge crypto. It's been in the market for a while, but it's a good sign there's innovation happening. So as now, clarity comes into what's real. I think you guys are talking a conversation I think is refreshing because you're saying, okay, cloud is real, Lambda, serverless, all these tools. So Web 3 is certainly real because it's a future architecture, but it's attracting the young, it's a cultural shift. And it's also cooler than boring Web 2 and cloud. So I think the cultural shift, the fact that it's got data involved, there's some disruption around middleman and intermediaries, makes it very attractive to tech geeks. You look at, I read a stat, I heard a stat from a friend in the Bay Area that 30% of Cal computer science students are dropping out and jumping into crypto. So it's attracting the technical nerds, alpha geeks. It's a cultural revolution and there's some cool stuff going on from a business model standpoint. >> There's one thing missing. The thing that's missing, it's what we're trying to work on, I think is experience. I think if you're being honest about the entire marketplace, what you would agree is that this stuff is not easy to use today, and that's got to be satisfied. You need to do something that if it's the 85 year old grandma that wants to actually participate in these markets that not only can they feel comfortable, but they actually know how to do it. You can't use these crazy tools where you use these terms. And I think the industry, as it grows up, will satisfy a lot of those issues. >> And I think this is why I want to tie back and get your reaction to this. I think that's why you guys talking about building on top of AWS is refreshing, 'cause it's not dogmatic. Well, we can't use Amazon, it's not really Web 3. Well, a database could be used when you need it. You don't need to write everything through the blockchain. Databases are a very valuable capability, you get serverless. So all these things now can work together. So what do you guys see for companies that want to be Web 3 for all the good reasons and how do they leverage cloud specifically to get there? What are some things that you guys have learned that you can point to and share, you want to start? >> Well, I think not everything has to be open and public to everybody. You're going to want to have some things that are secret. You're going to want to encrypt some things. You're going to want to put some things within your own walls. And that's where AWS really excels. I think you can have the best of both worlds. So that's my perspective on it. >> The only thing I would add to it, so my view is it's 2022. I actually was joking earlier. I think I was at the first re:Invent. And I remember walking in and this was a new industry. >> It was tiny. >> This is foundational. Like cloud is not a, I don't view like, we shouldn't be having that conversation anymore. Of course you should build this stuff on top of the cloud. Of course you should build it on top of AWS. It just makes sense. And we should, instead of worrying about those challenges, what we should be worrying about are how do we make these applications easier to use? How do we actually- >> Energy efficient. >> How do we enable the promise of what these things are going to bring, and actually make it real, because if it happens, think about traditional assets. There's projects going on globally that are looking at how do you take equity securities and actually move them to the blockchain. When that stuff happens, boom. >> And I like what you guys are doing, I saw the news out through this crypto winter, some major wallet exchanges that have been advertising are hurting. Take me through what you guys are thinking, what the vision is around the wallet of wallets. Is it to provide an experience for the user or the market industry itself? What's the target, is it both? Share the design goals for the wallet of wallets. >> My favorite thing about innovation and innovation labs is that we can experiment. So I'll go in saying we don't know what the final answer is going to be, but this is the premise that we have. In this disparate decentralized ecosystem, you need some mechanism to be able to control what's actually happening at the consumer level. So I think the key target is how do you create an experience where the consumer feels like they're in control of that value? How do they actually control the underlying assets? And then how does it actually get delivered to them? Is it something that comes from their bank, from their broker? Is it coming from an independent organization? How do they manage all of that information? And I think the last part of it are the assets. It's easy to think about cryptos and NFTs, but thinking about traditional assets, thinking about identity information and healthcare records, all of that stuff is going to become part of this ecosystem. And imagine being able to go someplace and saying, oh, you need my information. Well, I'm going to give it to you off my phone and I'm going to give it to you for the next 24 hours so you can use it, but after that you have no access to it. Or you're my financial advisor, here's a view of what I actually have, my underlying assets. What do you recommend I do? So I think we're going to see an evolution in the market. >> Like a data clean room. >> Yeah, but that you control. >> Yes! (laughs) >> Yes! >> I think about it very similarly as well. As my journey into the crypto market has gone through different pathways, different avenues. And I've come to a place where I'm really managing eight different wallets and it's difficult to figure exactly where all my assets are and having a tool like this will allow me to visualize and aggregate those assets and maybe even recombine them in unique ways, I think is hugely valuable. >> My biggest fear is losing my key. >> Well, and that's an experience problem that has to be solved, but let me give you, my favorite use case in this space is, 'cause NFTs, right? People are like, what does NFTs really mean? Title insurance, right? Anyone buy a house or refinance your mortgage? You go through this crazy process that costs seven or eight thousand dollars every single time you close on something to get title insurance so they could validate it. What if that title was actually sitting on the chain, you got an NFT that you put in your wallet and when it goes time to sell your house or to refinance, everything's there. Okay, I'm the owner of the house. I don't know, JP Morgan Chase has the actual mortgage. There's another lien, there's some taxes. >> It's like a link tree in the wallet. (laughs) >> Yeah, think about it, you got a smart contract. Boom, closing happens immediately. >> I think that's one of the most important things. I think people look at NFTs and they think, oh, this is art. And that's sort of how it started in the art and collectable space, but it's actually quickly moving towards utilities and tokenization and passes. And that's where I think the value is. >> And ownership and the token. >> Identity and ownership, especially. >> And the digital rights ownership and the economics behind it really have a lot of scale 'cause I appreciate the FinTech angle you are coming from because I can now see what's going on here with you. It's like, okay, we got to start somewhere. Let's start with the experience. The wallet's a tough nut to crack, 'cause that requires defacto participation in the industry as a defacto standard. So how are you guys doing there? Can you give an update and then how can people get, what's the project called and how do people get involved? >> Yeah, so we're still in the innovation, incubation stages. So we're not launching it yet. But what I will tell you is what a lot of our focus is, how do we make these transactional things that you do? How do we make it easy to pull all your assets together? How do we make it easy to move things from one location to the other location in ways that you're not using a weird cryptographic numeric value for your wallet, but you actually can use real nomenclature that you can renumber and it's easy to understand. Our expectation is that sometime in the fall, we'll actually be in a position to launch this. What we're going to do over the summer is we're going to start allowing people to play with it, get their feedback, and we're going to iterate. >> So sandbox in when, November? >> I think launch in the fall, sometime in the fall. >> Oh, this fall. >> But over the summer, what we're expecting is some type of friends and family type release where we can start to realize what people are doing and then fix the challenges, see if we're on the right track and make the appropriate corrections. >> So right now you guys are just together on this? >> Yep. >> The opening up friends and family or community is going to be controlled. >> It is, yeah. >> Yeah, as a group, I think one thing that's really important to highlight is that we're an innovation lab. We're working with Broadridge's innovation lab, that partnership across innovation labs has allowed us to move very, very quickly to build this. Actually, if you think about it, we were talking about this not too long ago and we're almost close to having an internal launch. So I think it's very rapid development. We follow a lot of the- >> There's buy-in across the board. >> Exactly, exactly, and we saw lot of very- >> So who's going to run this? A Dow, or your companies, is it going to be a separate company? >> So to be honest, we're not entirely sure yet. It's a new product that we're going to be creating. What we actually do with it. Our thought is within an innovation environment, there's three things you could do with something. You can make it a product within the existing infrastructure, you can create a new business unit or you can spin it off as something new. I do think this becomes a product within the organization based upon it's so aligned to what we do today, but we'll see. >> But you guys are financing it? >> Yes. >> As collective companies? >> Yeah, right. >> Got it, okay, cool. Well, let us know how we can help. If you guys want to do a remote in to theCUBE. I would love the mission you guys are on. I think this is the kind of work that every company should be doing in the new R and D. You got to jump in the deep end and swim as fast as possible. But I think you can do it. I think that is refreshing and that's smart. >> And you have to do it quick because this market, I think the one thing we would probably agree on is that it's moving faster than we could, every week there's something else that happens. >> Okay, so now you guys were at Consensus down in Austin when the winter hit and you've been in the business for a long time, you got to know the industries. You see where it's going. What was the big thing you guys learned, any scar tissue from the early data coming in from the collaboration? Was there some aha moments, was there some oh shoot moments? Oh, wow, I didn't think that was going to happen. Share some anecdotal stories from the experience. Good, bad, and if you want to be bold say ugly, too. >> Well, I think the first thing I want to say about the timing, it is the crypto winter, but I actually think now's a really great time to build something because everybody's continuing to build. Folks are focused on the future and that's what we are as well. In terms of some of the challenges, well, the Web 3 space is so new. And there's not a way to just go online and copy somebody else's work and rinse and repeat. We had to figure a lot of things on our own. We had to try different technologies, see which worked better and make sure that it was functioning the way we wanted it to function. Really, so it was not easy. >> They oversold that product out, that's good, like this team. >> But think about it, so the joke is that when winter is when real work happens. If you look at the companies that have not been affected by this it's the infrastructure companies and what it reminds me of, it's a little bit different, but 2001, we had the dot com bust. The entire industry blew up, but what came out of that? >> Everything that exists. >> Amazon, lots of companies grew up out of that environment. >> Everything that was promoted actually happened. >> Yes, but you know what didn't happen- >> Food delivery. >> But you know what's interesting that didn't happen- >> (laughs) Pet food, the soccer never happened. >> The whole Super Bowl, yes. (John laughs) In financial services we built on top of legacy. I think what Web 3 is doing, it's getting rid of that legacy infrastructure. And the banks are going to be involved. There's going to be new players and stuff. But what I'm seeing now is a doubling down of the infrastructure investment of saying okay, how do we actually make this stuff real so we can actually show the promise? >> One of the things I just shared, Rob, you'd appreciate this, is that the digital advertising market's changing because now banner ads and the old techniques are based on Web 2 infrastructure, basically DNS as we know it. And token problems are everywhere. Sites and silos are built because LinkedIn doesn't share information. And the sites want first party data. It's a hoarding exercise, so those practices are going to get decimated. So in comes token economics, that's going to get decimated. So you're already seeing the decline of media. And advertising, cookies are going away. >> I think it's going to change, it's going to be a flip, because I think right now you're not in control. Other people are in control. And I think with tokenomics and some of the other things that are going to happen, it gives back control to the individual. Think about it, right now you get advertising. Now you didn't say I wanted this advertising. Imagine the value of advertising when you say, you know what, I am interested in getting information about this particular type of product. The lead generation, the value of that advertising is significantly higher. >> Organic notifications. >> Yeah. >> Well, gentlemen, I'd love to follow up with you. I'm definitely going to ping in. Now I'm going to put CUBE coin back on the table. For our audience CUBE coin's coming. Really appreciate it, thanks for sharing your insights. Great conversation. >> Excellent, thank you for having us. >> Excellent, thank you so much. >> theCUBE's coverage here from New York City. I'm John Furrier, we'll be back with more live coverage to close out the day. Stay with us, we'll be right back. >> Excellent. (calm electronic music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. This is Data as Code, The Future of Enterprise Data and Analytics. This is also season two, episode two of our ongoing series with exciting partners from the AWS ecosystem who are here to talk with us about data and analytics. I'm your host, Lisa Martin. Two guests join me, one, a cube alumni. Venkat Venkataramani is here CEO & Co-Founder of Rockset. Good to see you again. And Doug Moore, VP of cloud platforms at Command Alkon. You're here to talk to me about how Command Alkon implemented real time analytics in just days with Rockset. Guys, welcome to the program. >> Thanks for having us. >> Yeah, great to be here. >> Doug, give us a little bit of a overview of Command Alkon, what type of business you are? what your mission is? That good stuff. >> Yeah, great. I'll pref it by saying I've been in this industry for only three years. The 30 years prior I was in financial services. So this was really exciting and eye opening. It actually plays into the story of how we met Rockset. So that's why I wanted to preface that. But Command Alkon is in the business, is in the what's called The Heavy Building Materials Industry. And I had never heard of it until I got here. But if you think about large projects like building buildings, cities, roads anything that requires concrete asphalt or just really big trucks, full of bulky materials that's the heavy building materials industry. So for over 40 years Command Alkon has been the north American leader in providing software to quarries and production facilities to help mine and load these materials and to produce them and then get them to the job site. So that's what our supply chain is, is from the quarry through the development of these materials, then out to the to a heavy building material job site. >> Got it, and now how historically in the past has the movement of construction materials been coordinated? What was that like before you guys came on the scene? >> You'll love this answer. So 'cause, again, it's like a step back in time. When I got here the people told me that we're trying to come up with the platform that there are 27 industries studied globally. And our industry is second to last in terms of automation which meant that literally everything is still being done with paper and a lot of paper. So when one of those, let's say material is developed, concrete asphalt is produced and then needs to get to the job site. They start by creating a five part printed ticket or delivery description that then goes to multiple parties. It ends up getting touched physically over 50 times for every delivery. And to give you some idea what kind of scale it is there are over 330 million of these type deliveries in north America every year. So it's really a lot of favor and a lot of manual work. So that was the state of really where we were. And obviously there are compelling reasons certainly today but even 3, 4, 5 years ago to automate that and digitize it. >> Wow, tremendous potential to go nowhere but up with the amount of paper, the lack of, of automation. So, you guys Command Alkon built a platform, a cloud software construction software platform. Talk to me of about that. Why you built it, what was the compelling event? I mean, I think you've kind of already explained the compelling event of all the paper but give us a little bit more context. >> Yeah. That was the original. And then we'll get into what happened two years ago which has made it even more compelling but essentially with everything on premises there's really in a huge amount of inefficiency. So, people have heard the enormous numbers that it takes to build up a highway or a really large construction project. And a lot of that is tied up in these inefficiencies. So we felt like with our significant presence in this market, that if we could figure out how to automate getting this data into the cloud so that at least the partners in the supply chain could begin sharing information. That's not on paper a little bit closer to real time that we could make has an impact on everything from the timing it takes to do a project to even the amount of carbon dioxide that's admitted, for example from trucks running around and being delayed and not being coordinated well. >> So you built the connect platform you started on Amazon DynamoDB and ran into some performance challenges. Talk to us about the, some of those performance bottlenecks and how you found Venkat and Rockset. >> So from the beginning, we were fortunate, if you start building a cloud three years ago you're you have a lot of opportunity to use some of the what we call more fully managed or serverless offerings from Amazon and all the cloud vendors have them but Amazon is the one we're most familiar with throughout the past 10 years. So we went head first into saying, we're going to do everything we can to not manage infrastructure ourselves. So we can really focus on solving this problem efficiently. And it paid off great. And so we chose dynamo as our primary database and it still was a great decision. We have obviously hundreds of millions of billions of these data points in dynamo. And it's great from a transactional perspective, but at some point you need to get the data back out. And what plays into the story of the beginning when I came here with no background basically in this industry, is that, and as did most of the other people on my team, we weren't really sure what questions were going to be asked of the data. And that's super, super important with a NoSQL database like dynamo. You sort of have to know in advance what those usage patterns are going to be and what people are going to want to get back out of it. And that's what really began to strain us on both performance and just availability of information. >> Got it. Venkat, let's bring you into the conversation. Talk to me about some of the challenges that Doug articulated the, is industry with such little automation so much paper. Are you finding that still out there for in quite a few industries that really have nowhere to go but up? >> I think that's a very good point. We talk about digital transformation 2.0 as like this abstract thing. And then you meet like disruptors and innovators like Doug, and you realize how much impact, it has on the real world. But now it's not just about disrupting, and digitizing all of these records but doing it at a faster pace than ever before, right. I think this is really what digital transformation in the cloud really enable tools you do that, a small team in a, with a very very big mission and responsibility like what Doug team have been, shepherding here. They're able to move very, very, very fast, to be able to kind of accelerate this. And, they're not only on the forefront of digitizing and transforming a very big, paper-heavy kind of process, but real-time analytics and real time reporting is a requirement, right? Nobody's wondering where is my supply chain three days ago? Are my, one of the most important thing in heavy construction is to keep running on a schedule. If you fall behind, there's no way to catch up because there's so many things that falls apart. Now, how do you make sure you don't fall behind, realtime analytics and realtime reporting on how many trucks are supposed to be delivered today? Halfway through the day, are they on track? Are they getting behind? And all of those things is not just able to manage the data but also be able to get reporting and analytics on that is a extremely important aspect of this. So this is like a combination of digital transformation happening in the cloud in realtime and realtime analytics being in the forefront of it. And so we are very, very happy to partner with digital disruptors like Doug and his team to be part of this movement. >> Doug, as Venkat mentioned, access to real time data is a requirement that is just simple truth these days. I'm just curious, compelling event wise was COVID and accelerator? 'Cause we all know of the supply chain challenges that we're all facing in one way or the other, was that part of the compelling event that had you guys go and say, we want to do DynamoDB plus Rockset? >> Yeah, that is a fantastic question. In fact, more so than you can imagine. So anytime you come into an industry and you're going to try to completely change or revolutionize the way it operates it takes a long time to get the message out. Sometimes years, I remember in insurance it took almost 10 years really to get that message out and get great adoption and then COVID came along. And when COVID came along, we all of a sudden had a situation where drivers and the foreman on the job site didn't want to exchange the paperwork. I heard one story of a driver taping the ticket for signature to the foreman on a broomstick and putting it out his windows so that he didn't get too close. It really was that dramatic. And again, this is the early days and no one really has any idea what's happening and we're all working from home. So we launched, we saw that as an opportunity to really help people solve that problem and understand more what this transformation would mean in the long term. So we launched internally what we called Project Lemonade obviously from, make lemonade out of lemons, that's the situation that we were in and we immediately made some enhancements to a mobile app and then launched that to the field. So that basically there's now a digital acceptance capability where the driver can just stay in the vehicle and the foreman can be anywhere, look at the material say it's acceptable for delivery and go from there. So yeah, it made a, it actually immediately caused many of our customers hundreds to begin, to want to push their data to the cloud for that reason just to take advantage of that one capability >> Project lemonade, sounds like it's made a lot of lemonade out of a lot of lemons. Can you comment Doug on kind of the larger trend of real time analytics and logistics? >> Yeah, obviously, and this is something I didn't think about much either not knowing anything about concrete other than it was in my driveway before I got here. And that it's a perishable product and you've got that basically no more than about an hour and a half from the time you mix it, put it in the drum and get it to the job site and pour it. And then the next one has to come behind it. And I remember I, the trend is that we can't really do that on paper anymore and stay on top of what has to be done we'll get into the field. So a foreman, I recall saying that when you're in the field waiting on delivery, that you have people standing around and preparing the site ready to make a pour that two minutes is an eternity. And so, working a real time is all always a controversial word because it means something different to anyone, but that gave it real, a real clarity to mean, what it really meant to have real time analytics and how we are doing and where are my vehicles and how is this job performing today? And I think that a lot of people are still trying to figure out how to do that. And fortunately, we found a great tool set that's allowing us to do that at scale. Thankfully, for Rockset primarily. >> Venkat talk about it from your perspective the larger trend of real time analytics not just in logistics, but in other key industries. >> Yeah. I think we're seeing this across the board. I think, whether, even we see a huge trend even within an enterprise different teams from the marketing team to the support teams to more and more business operations team to the security team, really moving more and more of their use cases from real time. So we see this, the industries that are the innovators and the pioneers here are the ones for whom real times that requirement like Doug and his team here or where, if it is all news, it's no news, it's useless, right? But I think even within, across all industries, whether it is, gaming whether it is, FinTech, Bino related companies, e-learning platforms, so across, ed tech and so many different platforms, there is always this need for business operations. Some, certain aspects certain teams within large organizations to, have to tell me how to win the game and not like, play Monday morning quarterback after the game is over. >> Right, Doug, let's go back at you, I'm curious with connects, have you been able to scale the platform since you integrated with Rockset? Talk to us about some of the outcomes that you've achieved so far? >> Yeah, we have, and of course we knew and we made our database selection with dynamo that it really doesn't have a top end in terms of how much information that we can throw at it. But that's very, very challenging when it comes to using that information from reporting. But we've found the same thing as we've scaled the analytics side with Rockset indexing and searching of that database. So the scale in terms of the number of customers and the amount of data we've been able to take on has been, not been a problem. And honestly, for the first time in my career, I can say that we've always had to add people every time we add a certain number of customers. And that has absolutely not been the case with this platform. >> Well, and I imagine the team that you do have is far more, sorry Venkat, far more strategic and able to focus on bigger projects. >> It, is, and, you've amazed at, I mean Venkat hit on a couple of points that it's in terms of the adoption of analytics. What we found is that we are as big a customer of this analytic engine as our customers are because our marketing team and our sales team are always coming to us. Well how many customers are doing this? How many partners are connected in this way? Which feature flags are turned on the platform? And the way this works is all data that we push into the platform is automatically just indexed and ready for reporting analytics. So we really it's no additional ad of work, to answer these questions, which is really been phenomenal. >> I think the thing I want to add here is the speed at which they were able to build a scalable solution and also how little, operational and administrative overhead that it has cost of their teams, right. I think, this is again, realtime analytics. If you go and ask hundred people, do you want fast analytics on realtime data or slow analytics on scale data, people, no one would say give me slow and scale. So, I think it goes back to again our fundamental pieces that you have to remove all the cost and complexity barriers for realtime analytics to be the new default, right? Today companies try to get away with batch and the pioneers and the innovators are forced to solve, I know, kind of like address some of these realtime analytics challenges. I think with the platforms like the realtime analytics platform, like Rockset, we want to completely flip it on its head. You can do everything in real time. And there may be some extreme situations where you're dealing with like, hundreds of petabytes of data and you just need an analyst to generate like, quarterly reports out of that, go ahead and use some really, really good batch base system but you should be able to get anything, and everything you want without additional cost or complexity, in real time. That is really the vision. That is what we are really enabling here. >> Venkat, I want to also get your perspective and Doug I'd like your perspective on this as well but that is the role of cloud native and serverless technologies in digital disruption. And what do you see there? >> Yeah, I think it's huge. I think, again and again, every customer, and we meet, Command Alkon and Doug and his team is a great example of this where they really want to spend as much time and energies and calories that they have to, help their business, right? Like what, are we accomplishing trying to accomplish as a business? How do we enable, how do we build better products? How do we grow revenue? How do we eliminate risk that is inherent in the business? And that is really where they want to spend all of their energy not trying to like, install some backend software, administer build IDL pipelines and so on and so forth. And so, doing serverless on the compute side of that things like AWS lambda does and what have you. And, it's a very important innovation but that isn't, complete the story or your data stack also have to become serverless. And, that is really the vision with Rockset that your entire realtime analytics stack can be operating and managing. It could be as simple as managing a serverless stack for your compute environments like your APS servers and what have you. And so I think that is going to be a that is for here to stay. This is a path towards simplicity and simplicity scales really, really well, right? Complexity will always be the killer that'll limit, how far you can use this solution and how many problems can you solve with that solution? So, simplicity is a very, very important aspect here. And serverless helps you, deliver that. >> And Doug your thoughts on cloud native and serverless in terms of digital disruption >> Great point, and there are two parts to the scalability part. The second one is the one that's more subtle unless you're in charge of the budget. And that is, with enough effort and enough money that you can make almost any technology scale whether it's multiple copies of it, it may take a long time to get there but you can get there with most technologies but what is least scalable, at least that I as I see that this industry is the people, everybody knows we have a talent shortage and these other ways of getting the real time analytics and scaling infrastructure for compute and database storage, it really takes a highly skilled set of resources. And the more your company grows, the more of those you need. And that is what we really can't find. And that's actually what drove our team in our last industry to even go this way we reached a point where our growth was limited by the people we could find. And so we really wanted to break out of that. So now we had the best of both scalable people because we don't have to scale them and scalable technology. >> Excellent. The best of both worlds. Isn't it great when those two things come together? Gentlemen, thank you so much for joining me on "theCUBE" today. Talking about what Rockset and Command Alkon are doing together better together what you're enabling from a supply chain digitization perspective. We appreciate your insights. >> Great. Thank you. >> Thanks, Lisa. Thanks for having us. >> My pleasure. For Doug Moore and Venkat Venkatramani, I'm Lisa Martin. Keep it right here for more coverage of "theCUBE", your leader in high tech event coverage. (upbeat music)

(gentle upbeat music) >> Hello, and welcome to theCUBE presentation of the AWS Startup Showcase Open Cloud Innovations. This is season two episode one of an ongoing series covering setting status from the AWS ecosystem. Talking about innovation, here it's open source for this theme. We do this every episode, we pick a theme and have a lot of fun talking to the leaders in the industry and the hottest startups. I'm your host John Furrier here with Lisa Martin in our Palo Alto studios. Lisa great series, great to see you again. >> Good to see you too. Great series, always such spirited conversations with very empowered and enlightened individuals. >> I love the episodic nature of these events, we get more stories out there than ever before. They're the hottest startups in the AWS ecosystem, which is dominating the cloud sector. And there's a lot of them really changing the game on cloud native and the enablement, the stories that are coming out here are pretty compelling, not just from startups they're actually penetrating the enterprise and the buyers are changing their architectures, and it's just really fun to catch the wave here. >> They are, and one of the things too about the open source community is these companies embracing that and how that's opening up their entry to your point into the enterprise. I was talking with several customers, companies who were talking about the 70% of their pipeline comes from the open source community. That's using the premium version of the technology. So, it's really been a very smart, strategic way into the enterprise. >> Yeah, and I love the format too. We get the keynote we're doing now, opening keynote, some great guests. We have Sir John on from AWS started program, he is the global startups lead. We got Swami coming on and then closing keynote with Deepak Singh. Who's really grown in the Amazon organization from containers now, compute services, which now span how modern applications are being built. And I think the big trend that we're seeing that these startups are riding on that big wave is cloud natives driving the modern architecture for software development, not just startups, but existing, large ISV and software companies are rearchitecting and the customers who buy their products and services in the cloud are rearchitecting too. So, it's a whole new growth wave coming in, the modern era of cloud some say, and it's exciting a small startup could be the next big name tomorrow. >> One of the things that kind of was a theme throughout the conversations that I had with these different guests was from a modern application security perspective is, security is key, but it's not just about shifting lab. It's about doing so empowering the developers. They don't have to be security experts. They need to have a developer brain and a security heart, and how those two organizations within companies can work better together, more collaboratively, but ultimately empowering those developers, which goes a long way. >> Well, for the folks who are watching this, the format is very simple. We have a keynote, editorial keynote speakers come in, and then we're going to have a bunch of companies who are going to present their story and their showcase. We've interviewed them, myself, you Dave Vallante and Dave Nicholson from theCUBE team. They're going to tell their stories and between the companies and the AWS heroes, 14 companies are represented and some of them new business models and Deepak Singh who leads the AWS team, he's going to have the closing keynote. He talks about the new changing business model in open source, not just the tech, which has a lot of tech, but how companies are being started around the new business models around open source. It's really, really amazing. >> I bet, and does he see any specific verticals that are taking off? >> Well, he's seeing the contribution from big companies like AWS and the Facebook's of the world and large companies, Netflix, Intuit, all contributing content to the open source and then startups forming around them. So Netflix does some great work. They donated to open source and next thing you know a small group of people get together entrepreneurs, they form a company and they create a platform around it with unification and scale. So, the cloud is enabling this new super application environment, superclouds as we call them, that's emerging and this new supercloud and super applications are scaling data-driven machine learning and AI that's the new formula for success. >> The new formula for success also has to have that velocity that developers expect, but also that the consumerization of tech has kind of driven all of us to expect things very quickly. >> Well, we're going to bring in Serge Shevchenko, AWS Global Startup program into the program. Serge is our partner. He is the leader at AWS who has been working on this program Serge, great to see you. Thanks for coming on. >> Yeah, likewise, John, thank you for having me very excited to be here. >> We've been working together on collaborating on this for over a year. Again, season two of this new innovative program, which is a combination of CUBE Media partnership, and AWS getting the stories out. And this has been a real success because there's a real hunger to discover content. And then in the marketplace, as these new solutions coming from startups are the next big thing coming. So, you're starting to see this going on. So I have to ask you, first and foremost, what's the AWS startup showcase about. Can you explain in your terms, your team's vision behind it, and why those startup focus? >> Yeah, absolutely. You know John, we curated the AWS Startup Showcase really to bring meaningful and oftentimes educational content to our customers and partners highlighting innovative solutions within these themes and ultimately to help customers find the best solutions for their use cases, which is a combination of AWS and our partners. And really from pre-seed to IPO, John, the world's most innovative startups build on AWS. From leadership downward, very intentional about cultivating vigorous AWS community and since 2019 at re:Invent at the launch of the AWS Global Startup program, we've helped hundreds of startups accelerate their growth through product development support, go to market and co-sell programs. >> So Serge question for you on the theme of today, John mentioned our showcases having themes. Today's theme is going to cover open source software. Talk to us about how Amazon thinks about opensource. >> Sure, absolutely. And I'll just touch on it briefly, but I'm very excited for the keynote at the end of today, that will be delivered by Deepak the VP of compute services at AWS. We here at Amazon believe in open source. In fact, Amazon contributes to open source in multiple ways, whether that's through directly contributing to third-party project, repos or significant code contributions to Kubernetes, Rust and other projects. And all the way down to leadership participation in organizations such as the CNCF. And supporting of dozens of ISV myself over the years, I've seen explosive growth when it comes to open source adoption. I mean, look at projects like Checkov, within 12 months of launching their open source project, they had about a million users. And another great example is Falco within, under a decade actually they've had about 37 million downloads and that's about 300% increase since it's become an incubating project in the CNCF. So, very exciting things that we're seeing here at AWS. >> So explosive growth, lot of content. What do you hope that our viewers and our guests are going to be able to get out of today? >> Yeah, great question, Lisa. I really hope that today's event will help customers understand why AWS is the best place for them to run open source, commercial and which partner solutions will help them along their journey. I think that today the lineup through the partner solutions and Deepak at the end with the ending keynote is going to present a very valuable narrative for customers and startups in selecting where and which projects to run on AWS. >> That's great stuff Serge would love to have you on and again, I want to just say really congratulate your team and we enjoy working with them. We think this showcase does a great service for the community. It's kind of open source in its own way if I can co contributing working on out there, but you're really getting the voices out at scale. We've got companies like Armory, Kubecost, Sysdig, Tidelift, Codefresh. I mean, these are some of the companies that are changing the game. We even had Patreon a customer and one of the partners sneak with security, all the big names in the startup scene. Plus AWS Deepak saying Swami is going to be on the AWS Heroes. I mean really at scale and this is really a great. So, thank you so much for participating and enabling all of this. >> No, thank you to theCUBE. You've been a great partner in this whole process, very excited for today. >> Thanks Serge really appreciate it. Lisa, what a great segment that was kicking off the event. We've got a great lineup coming up. We've got the keynote, final keynote fireside chat with Deepak Singh a big name at AWS, but Serge in the startup showcase really innovative. >> Very innovative and in a short time period, he talked about the launch of this at re:Invent 2019. They've helped hundreds of startups. We've had over 50 I think on the showcase in the last year or so John. So we really gotten to cover a lot of great customers, a lot of great stories, a lot of great content coming out of theCUBE. >> I love the openness of it. I love the scale, the storytelling. I love the collaboration, a great model, Lisa, great to work with you. We also Dave Vallante and Dave Nicholson interview. They're not here, but let's kick off the show. Let's get started with our next guest Swami. The leader at AWS Swami just got promoted to VP of the database, but also he ran machine learning and AI at AWS. He is a leader. He's the author of the original DynamoDB paper, which is celebrating its 10th year anniversary really impacted distributed computing and open source. Swami's introduced many opensource aspects of products within AWS and has been a leader in the engineering side for many, many years at AWS, from an intern to now an executive. Swami, great to see you. Thanks for coming on our AWS startup showcase. Thanks for spending the time with us. >> My pleasure, thanks again, John. Thanks for having me. >> I wanted to just, if you don't mind asking about the database market over the past 10 to 20 years cloud and application development as you see, has changed a lot. You've been involved in so many product launches over the years. Cloud and machine learning are the biggest waves happening to your point to what you're doing now. Software is under the covers it's powering it all infrastructure is code. Open source has been a big part of it and it continues to grow and change. Deepak Singh from AWS talks about the business model transformation of how like Netflix donates to the open source. Then a company starts around it and creates more growth. Machine learnings and all the open source conversations around automation as developers and builders, like software as cloud and machine learning become the key pistons in the engine. This is a big wave, what's your view on this? How how has cloud scale and data impacting the software market? >> I mean, that's a broad question. So I'm going to break it down to kind of give some of the back data. So now how we are thinking about it first, I'd say when it comes to the open source, I'll start off by saying first the longevity and by ability of open sources are very important to our customers and that is why we have been a significant contributor and supporter of these communities. I mean, there are several efforts in open source, even internally by actually open sourcing some of our key Amazon technologies like Firecracker or BottleRocket or our CDK to help advance the industry. For example, CDK itself provides some really powerful way to build and configure cloud services as well. And we also contribute to a lot of different open source projects that are existing ones, open telemetries and Linux, Java, Redis and Kubernetes, Grafana and Kafka and Robotics Operating System and Hadoop, Leucine and so forth. So, I think, I can go on and on, but even now I'd say the database and observability space say machine learning we have always started with embracing open source in a big material way. If you see, even in deep learning framework, we championed MX Linux and some of the core components and we open sourced our auto ML technology auto Glue on, and also be open sourced and collaborated with partners like Facebook Meta on Fighter showing some major components and there, and then we are open search Edge Compiler. So, I would say the number one thing is, I mean, we are actually are very, very excited to partner with broader community on problems that really mattered to the customers and actually ensure that they are able to get amazing benefit of this. >> And I see machine learning is a huge thing. If you look at how cloud group and when you had DynamoDB paper, when you wrote it, that that was the beginning of, I call the cloud surge. It was the beginning of not just being a resource versus building a data center, certainly a great alternative. Every startup did it. That's history phase one inning and a half, first half inning. Then it became a large scale. Machine learning feels like the same way now. You feel like you're seeing a lot of people using it. A lot of people are playing around with it. It's evolving. It's been around as a science, but combined with cloud scale, this is a big thing. What should people who are in the enterprise think about how should they think about machine learning? How has some of your top customers thought about machine learning as they refactor their applications? What are some of the things that you can share from your experience and journey here? >> I mean, one of the key things I'd say just to set some context on scale and numbers. More than one and a half million customers use our database analytics or ML services end-to-end. Part of which machine learning services and capabilities are easily used by more than a hundred thousand customers at a really good scale. However, I still think in Amazon, we tend to use the phrase, "It's day one in the age of internet," even though it's an, or the phrase, "Now, but it's a golden one," but I would say in the world of machine learning, yes it's day one but I also think we just woke up and we haven't even had a cup of coffee yet. That's really that early, so. And, but when you it's interesting, you've compared it to where cloud was like 10, 12 years ago. That's early days when I used to talk to engineering leaders who are running their own data center and then we talked about cloud and various disruptive technologies. I still used to get a sense about like why cloud and basic and whatnot at that time, Whereas now with machine learning though almost every CIO, CEO, all of them never asked me why machine learning. Instead, the number one question, I get is, how do I get started with it? What are the best use cases? which is great, and this is where I always tell them one of the learnings that we actually learned in Amazon. So again, a few years ago, probably seven or eight years ago, and Amazon itself realized as a company, the impact of what machine learning could do in terms of changing how we actually run our business and what it means to provide better customer experience optimize our supply chain and so far we realized that the we need to help our builders learn machine learning and the help even our business leaders understand the power of machine learning. So we did two things. One, we actually, from a bottom-up level, we built what I call as machine learning university, which is run in my team. It's literally stocked with professors and teachers who offer curriculum to builders so that they get educated on machine learning. And now from a top-down level we also, in our yearly planning process, we call it the operational planning process where we write Amazon style narratives six pages and then answer FAQ's. We asked everyone to answer one question around, like how do you plan to leverage machine learning in your business? And typically when someone says, I really don't play into our, it does not apply. It's usually it doesn't go well. So we kind of politely encourage them to do better and come back with a better answer. This kind of dynamic on top-down and bottom-up, changed the conversation and we started seeing more and more measurable growth. And these are some of the things you're starting to see more and more among our customers too. They see the business benefit, but this is where to address the talent gap. We also made machine learning university curriculum actually now open source and freely available. And we launched SageMaker Studio Lab, which is a no cost, no set up SageMaker notebook service for educating learner profiles and all the students as well. And we are excited to also announce AIMLE scholarship for underrepresented students as well. So, so much more we can do well. >> Well, congratulations on the DynamoDB paper. That's the 10 year anniversary, which is a revolutionary product, changed the game that did change the world and that a huge impact. And now as machine learning goes to the next level, the next intern out there is at school with machine learning. They're going to be writing that next paper, your advice to them real quick. >> My biggest advice is, always, I encourage all the builders to always dream big, and don't be hesitant to speak your mind as long as you have the right conviction saying you're addressing a real customer problem. So when you feel like you have an amazing solution to address a customer problem, take the time to articulate your thoughts better, and then feel free to speak up and communicate to the folks you're working with. And I'm sure any company that nurtures good talent and knows how to hire and develop the best they will be willing to listen and then you will be able to have an amazing impact in the industry. >> Swami, great to know you're CUBE alumni love our conversations from intern on the paper of DynamoDB to the technical leader at AWS and database analyst machine learning, congratulations on all your success and continue innovating on behalf of the customers and the industry. Thanks for spending the time here on theCUBE and our program, appreciate it. >> Thanks again, John. Really appreciate it. >> Okay, now let's kick off our program. That ends the keynote track here on the AWS startup showcase. Season two, episode one, enjoy the program and don't miss the closing keynote with Deepak Singh. He goes into great detail on the changing business models, all the exciting open source innovation. (gentle bright music)

(upbeat music) >> Okay, welcome back everyone to theCUBE's coverage of AWS re:Invent 2021. This is theCUBE. I'm John Furrier, my host Lisa Martin here, with some keynote guests who are on the big stage here at re:Invent, Linda Jojo, Chief Digital Officer at United Airlines. Thanks for coming on. >> Hey, great to be here. Thanks for having me. >> So up on the big stage, big transformation story in front of 27,000 people, on the virginity, >> Linda: That many? >> that's the number, >> It's a big room >> pretty small for Amazon web services, nearly 60,000, but you know, pandemic and all but great presentation. What was the, what was the transformation story for United? >> Well, I think there's two parts of the story. One is just how fast everything happened, you know. February of 2020, we're having a kickoff meeting with AWS about how we're going to really transform the airline and a month later the world shut down. And so it changed, we went from thinking about the future to really just trying to make it through the next few weeks. But as soon as that happened, we knew that we had to take advantage of the crisis and think about everything from what can we do with our onboard products, we've changed out a lot of things about our airplanes, we've doubled down on sustainability. We're really focused on the diversity of our workforce, but also we really said, what can we do about transforming our technology? And that's where AWS came in because one of the silver linings for our tech team was that we didn't always have a plane in the air. And so when that happens, we had no time to make a change and back it out, if it doesn't work or heaven forbid have an outage. We a little bit longer. So we got aggressive and we made a lot of changes and made a lot of move to AWS Cloud. >> Talk to me a little bit about the cultural shift involved. I mean, you talked about, you know, everybody was just scrambling. >> Yeah. So quickly, there was this instant, what do we do? How do we pivot? How do we survive mode? But from a cultural perspective, it sounds like you took, you leveraged the situation to be able to make a lot of improvements across the United, but culturally that's, that's challenging to get all those folks on board at the same time. How did you facilitate that? >> Well, you know what, the story I'm going to tell isn't all just about me. It's about the incredible team that we have, but you know, folks got focused and Amazon talks about having a two pizza team about how if your team should be no bigger than what can be fed by two pizzas, and that really keeps the decision-making streamlined and fast. For us since we were now all working from home, we called it a one screen team. And so the idea was no more than a number of people that could fit on that video call was the idea. So that was the number of people that we had on our teams. We branded them even call them scrappy teams, which was really kind of fun. And those are the groups that just kind of got their job done. And you know, the first part of their job was every week or every day it seemed like we were getting new rules from the U.S government about what countries you couldn't fly to. And it was chaotic. It was confusing for customers and frankly, our, that one screen team, they were up like every night making modifications to who could check in online and who couldn't. And we said when it's time to open back up, we can't, we got to do this better. And so that group came up with something we now call the Travel-Ready Center. Which is really pretty incredible. What you can do now is first of all, when you book your flight, we'll tell you what you need to fly. You need this type of a COVID test, this many days in advance. This is what fully vaccinated means in the country you're going to. And so this was the kind of vaccine card we need to see. You upload it all. We use Amazon SageMaker and we have machine learning models that actually now will within seven seconds validate that you're ready to fly. And what that means is just like always, you can get your boarding pass before you get to the airport. Now, if you guys travel a lot, I hope you still do, >> Yeah. what that means is that you can actually bypass the lobby of the airport and all the document checking that's going on because your travel ready. So customers love it. Gate agents love it too, because gate agents, the rules are changing so fast. They don't, you know, and they work the flight to Tel Aviv one day and the flight to Paris the next and the rules are different. And maybe in between, they changed. So having the software actually figured that out is what helps. >> So very dynamic and new innovations popped out of this pandemic. What else did Amazon help you with? Was there other Amazon innovations that you guys gravitated to SageMaker was one, what were some of the other? >> Yeah. You know, honestly, the team uses a lot of the tools and a lot of different ways. I would say the other big one was DynamoDB, and some of the things that we did to actually migrate some of our core systems to Amazon and actually, you know, instead of making phoning home to data centers all the time, we're now going right to the Cloud and getting some, some really great performance out of that. >> And, and, and the travel thing that you guys did that was came out of the innovation from the teams. >> Yeah. >> is there any other, other examples that popped out from you guys? >> Yeah. Well, I think another one is something that we call Agent on Demand. Agent on Demand is where you used it when you had to talk to an agent in the airport, you'd go get in line somewhere. And sometimes it was a long line, right? Because there's only two people there. And so the first thing we did was we made sure the technologies they used worked on a phone or an iPad. So now we weren't limited by the number of, of stations at the gate. The next thing we is that we made it QR code enabled. And now what customers can do is they can scan the QR code and they get a live agent, like a FaceTime call on their, on their phone. They can do it from anywhere from their seat at the gate or in line for a coffee, and they can solve their problem right there. And those agents, by the way, now maybe there's a snowstorm going on in Chicago, but the agents are in Houston where it's sunny. And so we can actually leverage the fact that those agents are there to help our customers. >> So you've got the user experience, you did some innovation. How about the operational things, I noticed when I traveled the United, the packaging's different ,the greetings are different. I get why all these operational impacts happened to the whole supply chain.(laughing) >> Yeah. Well, you know, the technology's great, but what makes you remember United are the people that you're going to interact with. And so we really focused on service for our, for our employees. And how do we give them information in the palm of their hand to, to treat you in a very personal way. We know that you flew last week and where you went. We know that you just made a million miles. And so we can give that information to our flight attendant and they can provide a really great experience. >> That experience is key. These days. One of the things that's been in short supply, during the pandemic is patience. And obviously you guys have to be very cognizant of that with some of the things that have happened across all the airlines and passengers not having the patience that they normally would have. >> Oh yeah. That is a real kudos to our flight attendants. And what we did with them, you know, wearing a mask is required on the aircraft and, you know, some folks don't like to be told what to do anywhere, right? And so people don't like that. Our flight attendants learned how to deescalate the situation and deal with it on the ground. So it's very simple. If you're not wearing a mask, flight attendant asked you nicely, you still don't put your mask on. They just give you a little card that says, by the way, if you don't put your mask on, this is going to be your last United flight. And the vast majority of customers put their masks on. So we have not seen some of that level of stress that's happened on some of other, other airlines. >> That's key. Cause it's been pretty rampant. But the fact that you're, you're making things much more accessible. And in real time, I think another thing we learned during the pandemic is that real time is no longer a nice to have. It's essential. We have this expectation as consumers, whether we're flying or we're buying something from an online retailer that we're going to be able to get whatever we want in the palm of our hand. >> Yeah. Well, you know what we like to say, we're very proud of our mobile app. We're very proud of it. But we like to say that are not comparing our mobile app to another airline mobile app. You're, you're comparing it to the last app you probably used. And that might've been the Amazon app. So we have to be as good as the Amazon app, but we have a lot of legacy technology behind it. And so we have really focused on that. >> Good, I want to ask you cause you're a Chief Digital Officer, because this comes up in a lot of our CUBE conversations and around the digital side is that obviously with the virtual now hybrid things, new innovations have happened. So I have to ask you what's changed for the better that's going to be around and what might not be around that you've learned from the pandemic, because these new things are emerging. New standards, new protocols, new digital experiences. What have you learned that's going to stay around and what kind of went away? >> Yeah. >> Well, I think nothing tells you about how important your customers are if you're standing in the middle of O'Hare and not seeing any. And that's what happened in April of 2020, when we actually, there was a day that year, that month that we had more pilots than passengers. It was just, you know, so you realize it's really all about the customer. And what we have to do is make sure that customers choose us. There might be less reasons to fly to certain places all the time, but when you do fly, we want you to pick United. And so it's got to be more than just where we fly. It's got to be the experiences you have with the people. And we have to use the technology to make it easier. I mean, Touchless, wasn't really a thing. QR codes are back. I mean, they were gone, right. And we have QR codes on everything now. Cause you want to get through that airport without having to touch anything, and you do that with your mobile app. >> Yeah. Great innovations. >> It is a great innovation. That contact list is key. You talk about QR cuts coming back. And just some of the things that we've, that we've, some of the silver linings and frankly there have been some the last 22 months or so, but being able to have that experience, that's tailored to me as a consumer. >> Right. I don't need to know what's under the hood enabling it. I just know I want to be able to make transactions or find whatever I need to in the palm of my hand, 24/7. >> Yeah. And you know, for airlines, it usually comes back to something went wrong and frankly, there's always something that going quite right. There's a, there's a weather delay somewhere or maybe your bag didn't get on the same flight you did. And so we want to give you transparency in that and control over what you can do. And so how make it, make it easier to rebook, make you understand what the situation is, be very transparent about it. And we even have something called Connection Saver. And what we do with that is we actually use real time data analytics. And what we do is we say, there's a person that's arriving late. And then we say with real-time weather, real-time connection data. We say, can we hold that flight for Lisa? And we, and we, yeah.(laughing) The worst thing is when that door closed, you run all the way through the airport and they closed the door. Right? We don't want to do, gate agents don't like doing that either. And so we use calculations that say, you know, the wind is blowing in the right direction. The pilots can make up the time. There isn't anybody on the other side, that's going to miss a connection. And so about 2000 times a day, we hold a connection for our customer. >> That's key. If you missed, sometimes just stay overnight. If you miss that connection. >> Especially on the last flight of the day we'll be, we'll be very generous because that doesn't do anybody any good. >> Well, great, great story. I love the keynote, Cloud has changed. I have to ask you this year at re:Invent, what's your observation on the Cloud as the cloud continues to expand, as Adam is talking about, how do you guys see the Cloud evolving for United? >> Well, you know, I, I think what's really impressive here is everybody is coming from every industry. It's not one or two industries that are here, are early adopters in the industry. It really is what you have to do to survive. But I probably would be remiss not to say that, which was really great was that there were two women on the, on the keynote stage and two men. So we were at 50 50 now there are 51% women in the world, but we'll take it. And I, in all seriousness, I do think that there is, there's a lot more diversity here and I think that's good. Not just for AWS. That's good for everybody. >> I couldn't agree more. That was one of the first things I noticed this morning when you took the keynote stage was a strong female leader before you even started telling the story. And that's something from an optics perspective. I know that Amazon is really keen on, but it's nice to hear from your perspective as well that there's, there's that diversity. There's also that thought diversity when you have different perspectives come into play because there's so many dynamics going on these days. But I have to ask you one question. We know we talked to, we, we, we talk about every company, these days being a data company, being a digital company needing to be, to be competitive. >> Right. Do you think of United, should, should we be thinking about United as a digital first company? >> Well, we, we, we connect people, right? And so we are physically moving people from one destination to another and they really want to get there. So we're not going to always be digital, but I would tell you that I often speak with our Chief Customer Officer and our Chief Operating Officer. And it's really hard for us to talk about anything without talking about technology or how it impacts the operation or how it impacts our customer. It's really, really meshing together for sure. >> Great stuff, Linda, thanks for coming on theCUBE. Really appreciate it. United Airlines, Chief Digital Officer on the main stage here at re:Invent and now on theCUBE. I'm John Furrier, Lisa Martin. You're watching theCUBE, the tech leader in event coverage. Thanks for watching. (upbeat music)

(gentle music) >> Hello everyone, and welcome back to day two of AWS re:Invent 2021, theCUBE's continuous coverage. My name is Dave Vellante, I'm here with my co-host, David Nicholson. We've got two sets. We had two remote sets prior to the show. We're running all kinds of activities and we've got AWS executives, partners, ecosystem technologists, Scott Weber is here as the director and an AWS partner, ambassador from PwC. Scott, good to see you. >> Nice to meet you guys. Thanks for letting me be here. >> Well, so your expertise is around application modernization. It's a hot theme these days. If you're a company with a lot of legacy debt, you've got a big complex application portfolio. I would think, especially with the forced match to digital over the last year and a half, two years. Now is really a time when you're probably too late to really start thinking about rationalizing your portfolio. What are you seeing in this space? >> Definitely, we're seeing the customers that have reached that point. I view modernization as sort of the second wave of cloud that's coming. So you had your first wave, the early adopters that lifted and shifted into the cloud. We still have people looking at getting into the cloud, but for those that went early, now, they're saying, "How do I get more out of the cloud? How do I get closer to cloud native?" And that's what we're starting to see around this modernization move is, I want to start to utilize those higher level services from AWS and the cloud providers. I want to get a better return, I want to stop worrying about running infrastructure and hardware. >> So when you think about, I go back all the way back to Y2K, that was like a boondoggle for IT to spend a bunch of doh and do some cool stuff. And then of course the .com crashed, but today it's different. It's really about the business impact the business outcome that you can drive in transforming your digital business. So how do you as a technology agnostic consultant help a company understand what they should leave alone or sunset? What they should aggressively migrate? What's the process that you use to do that? >> In some ways we go back, we can reuse sort of those 6Rs that maybe got a customer to the cloud, or as they're on that cloud journey, right? And you really want to focus on where can you optimize ROI. And you're going to come across those things that are going to be like, look, maybe it's a vendor COTS solution. There's not a lot we can do there. You're just going to have to continue down that path. Unless we can look to move that to a SaaS service. Maybe the vendor has gone to a SaaS offering. Or we get into looking at they've done development in house, but that development is still monolithic running on virtual machines, either in the data center or in AWS, but it's a critical system to that business. It's maybe it's become fragile. How can we now modernize that? Because that's where there's going to be a great return on investment for that customer, and it's also going to allow business agility for those customers. As we can get them to microservices and Lambda and function as a service, the blast radius for changes become smaller, allows the customer to move faster than what they're doing. So it's the rationalization becomes what's driving the business forward? What's critical to the business? But what's holding them back as well? So that the customers can start to move faster. >> So it's a formula of okay, what's the business value of those applications essentially? You can kind of rank that, but then it's a formula there's a cost equation. That's pretty straightforward to figure out the s is and the 2b but then there's a speed. Like an ongoing time to value from a developer standpoint and then I guess there's risk. Have you got your core jewels? Maybe you don't want to touch those yet. Is that kind of your algorithm? >> It is and on that sort of cost and value piece, that's where we can really see some interesting things happen, where as we get customers away from licensed OSS proprietary databases, that return on investment can be huge. So we've helped customers migrate from running .net applications on top of a typical Microsoft Windows stack and SQL server stack. All the way to taking those workloads, all the way, either to Linux containers or all the way to serverless if we're going to take all the steps to rewrite, you can drive 60, 70, 80% of the cost of operating at that platform out of it, then you start this flywheel effect of reinvesting that money back into the next project to help the customer move forward. >> And it's quick follow up, but I know you want to jump in. >> Yeah, yeah. >> Why wouldn't a customer, that's a Microsoft customer just run that on Azure? Why AWS? >> I mean, that's a good question and that sort of gets into a lot of philosophical, like discussion we talk about for a long time. The fact of the matter is the majority of your Windows workloads still run on top of AWS today. I would argue AWS has some pretty superior things in their underlying architecture, they're nitro architectures and things like that. But I think it's also choice. And, the whole move of .net to Linux, Microsoft started that they put the ability to, you can run SQL server on top of Linux. Well, if I run SQL server on top of Linux, I take out 20% of my costs right there. They put the support in for .net core to be able to run on Linux or on containers, but that's to help the developers move faster, that's to help us get to microservices. So that cloud provider choice, I think is becomes a bigger discussion, but a lot of people are choosing AWS because they're not just doing Microsoft workloads . Again, we could get very deep into like, trade-offs on why one over the other, but customers are choosing AWS for a lot of these words. >> Diversity and better cloud, better infrastructure. >> Yeah, and philosophical is an interesting way to look at it when it becomes a hostage negotiation. I'm not sure there was a lot of philosophy involved when server and SQL 2008 were being end of support life. And people were told, move it to Azure and we'll take care of you. Don't move it to Azure, you're on your own. But something on the subject of ROI. ROI is typically measured over time. How do you rectify and address the sort of CIO dilemma, which is that if ROI is being delivered fantastically in four years, but the average tenure of a CIO is 2.7 years, how do you address that? What is the sweet spot for timeframes that you're seeing for people to actually implement when you consider as was mentioned today, the keynote that somewhere around 15% of IT spend is in cloud today, which leaves 85% of it on premises. So what do we do about that? >> Yeah, that's a great question. So, I think, I like to get small wins. So find a very big pain point for that customer. How can we start to get them some small wins and start that flywheel effect going of like you saved money here, now, can we reinvest and start to show some wins, but we've engaged in projects where we've completely rewritten a whole application stack that was the core service for a business in a year and a half, and we took them from a run rate of somewhere between 40 and $60,000 a month. Had they been running that in AWS, they were running it in a data center today. So that was our estimate to less than $5,000 a month to run that application on a serverless platform inside of AWS. >> So when you talk about modernizing an application environment, that's typically not thought of as low hanging fruit. So does that mean that all the low hanging fruit has been consumed? Are all the net new things that are developed in a cloud native format, have they already been done? Is this the only frontier for opportunity now? >> No, it's not the only frontier. I mean, there's a lot of customers that are still just trying to get into the cloud. >> Lots of applications out there? >> Yeah, and you look at things like mainframe as well. That's I think a coming area where customers are finally starting to say, "Enough with the mainframe, we saw it in the keynote today of a new sort of service offering around helping customers rationalize how to do, to start to do things with the mainframe." So, but sometimes you can get those easy wins. Like we find a scalability issue. And we can inject scalability and pull back costs very rapidly. 'Cause you run in that scenario, there provision for max capacity that may happen 10% of the year. Now they're vastly overpaying. So we can still get some easy wins with slight tweaks to the platform while we help them rationalize those longer built times. I think the other thing we're starting to see is a shift in CIOs that are coming more from a software background too. That aren't from the pure infrastructure background and as we see those software dBase CIO start to come in. They're starting to understand the game that can be had of making the investment in the software and those upgrades to the software. >> And their tenure is elongating 'cause, CIO career is over was the joke. Now you're losing CIO, is cause they're going onto a bigger and better. They getting more options. I mean, they're becoming rockstars again. I want to ask you just as a side about that mainframe compatible runtime that they announced 'cause it sounds like you've got some experience in converting mainframe. >> Yeah. >> 'Cause I've always been a skeptic. We've seen this movie before where people have to freeze code, they've got to freeze code for 18 months. It takes 24 months, but now it's cloud, Adam Selipsky said, we can cut migration time, which is critical here by two-thirds 'cause that's the key. If you can reduce the time of which you have to freeze the code or maybe not even freeze the code. Again, I'm a skeptic, but what are you seeing with practical experience? >> So at PwC, we're seeing a lot of customers, start down this path and the ROI is pretty amazing when once you get in and you really start to dig in of what it can be if to go down this path. And there's a lot of tools out there, there's a gentleman on our team that's a real genius with this and he's helped multiple customers go down this path. There's tools that can start to do code conversion for you. I mean, we all get a little skeptical on those things cause we never know what the machine is going to try to make the code look like, but it's the starting point. But there is more. >> Like a prewash? >> Yeah, (Dave laughs) there's more and more design patterns coming out to help us down those pathways. But it goes back to agility for the business cause a lot of these customers running mainframes today are looking at a six month release cycle if they want to make any changes to their environment. If we can get them into an agile mindset to a microservice, they can get to two weeks or less for release cycles. So it's a big win for the company overall. Yes, there's a risk, but I think you can take, you can try to de-risk it as much as you can, you don't take the core, the absolute core critical piece of that mainframe. You start to pick away around the edges and you get comfortable with what you're doing. >> And going back to the concept of ROI, specifically in the mainframe space, there have been some not so subtle nudges from the marketplace that changed the dynamics associated with staying on your mainframe. Because if I tell you that the tax to stay on your mainframe is going to triple or quadruple over the next several years, that changes the balance. So you have the old guard in the software business who will remain nameless, jacking up the prices because they feel like, you know what, "What are you going to do? What are you going to do other than write me a cheque?" And the answer is, "Well move," right?. >> Yep, it's reached a point like the companies are moving. And what I think companies start to see too is, when we talk about purpose-driven databases, Adam was talking about that in the keynote today too. And we've seen that with customers when we've done builds, what's the right database for this data? And now you can start to get things moving even faster. And you unleash new ways of thinking. And I mean, some of the vendors are doing things like that and the companies aren't happy about it. >> Well, yes, but look, you're talking about Oracle in particular. (group chattering) That's one of them, but Oracle invests in its database and it's two different theories. Adam, today's the right tool for the right job, API and primitives and Oracle takes the kind of Swiss army knife approach. But they do invest if you have hard core mission critical, recovery is everything. There's a risk factor involved there, but if you want to go fast and you're a developer, you're not going to necessarily knock on Oracle's door, you're going to go to get an AWS. But it gets to my question, having done a lot of TCO analysis, it used to be labor, was always two-thirds of the cost. Now with automation, especially in Oracle environments, software license costs are the dominant component and it's maybe less true for SQL server, certainly true for Db2. I remember the early days of the flash, we used to tell customers, install flash. You're going to be able to consolidate, reduce your Oracle licenses when they come up. So that was a preferred strategy, but what are you seeing in terms of the ability? First of all is that a correct premise that software licenses is still a big component or an increasingly large component, and how do you unshackle from that? >> Yeah, so definitely software licensing costs for the OSS and for the databases are huge. I mean, there's numbers out there that like for SQL server enterprise, if you can get somebody off the SQL server enterprise and get them to an open solution like Aurora Postgres or something like that, it's a 90% ROI, and the numbers are similar for Oracle. And I talked to a lot of customers are like, "But we don't know Postgres," but it's not really that different. It's still data modeling. And when you get to these managed services platforms like RDS and Aurora, you free up those DBS to do the higher value things. The ROI of a DBA is not managing memory and desk and babysitting the servers, it's helping the developers build better data models. And those sorts of things that are higher value. So it is a big thing and we're seeing customers saying like, "Help us reduce this licensing cost," and help us be more efficient because the open platforms now, especially in the relational database area, are on par in a lot of ways with the Oracles and the SQL servers. So then you start to say, "Well, what am I gaining by paying and being sort of held hostage to these numbers?" So we definitely see customers making this transition. >> I mean, the point about Postgres is a good one because you're going to get enterprise class recoverability but even EDB would say okay, don't start with your mission critical core, pick around the edges just what he's saying over and over time, you're going to become more cloud native and get to the point, can you get to that point where everything's cloud native, everything is a service, maybe not a 100%, but a large part of your application portfolio can get there, right? >> Yeah, you're going to find those, that goes back to doing that application tiering and evaluation and ROI. So, we have a case study that we did with Constellation Brands, where they really needed a B2B type ordering portal solution. And they looked at sort of the typical vendors in a packaged solution if you will, a cottage type solution. And we proposed doing a full custom solution, soup to nuts and building it natively in AWS. And it was built completely on top of platform services. There was no servers in that environment and we were done. We were using AWS Fargate to run their containers on top of, we were using RDS Postgres, we were using Lambda and in some places we were using DynamoDB for holding inflate orders. And so the whole environment is deployable from one cloud formation template. So it completely changed how we even went through the testing of the thing. 'Cause you ran the same cloud formation template to deploy to a different environment. And you knew you were getting the same exact thing. And so they went from, they no longer had to worry about securing underlying compute, secure the containers, run on top of Fargate, use a platform service for your databases, and it was a beautiful solution for them. >> Yeah, you got to taste of that and your eyes open up and say, "Wow, what's possible?" >> Yeah, its a game changer. >> We heard that from NASDAQ this morning. An amazing story. She said, our first Amazon bill was 20 bucks. I bet it's higher now, but first hits free kind of thing. But the point is when people talk about the AWS bill, et cetera, no question, you should try to optimize that. But at the end of the day, it's about the business value Scott, isn't it? >> Scott: Yeah, it is. >> Hey, thanks so much for coming to theCUBE. It was great perspectives, >> No, thank you guys. I appreciate having you guys on. >> Thank you very much. >> Keep it right there, Dave Nicholson and I will be right back. You're watching theCUBE's coverage of AWS re:Invent 2021. (gentle music)

(Techno music plays in intro) >> We're here with theCUBE covering Commvault Connections 21. And we're going to look at the data protection space and how cloud computing has advanced the way we think about backup, recovery and protecting our most critical data. Ranga Rajagopalan who is the Vice President of products at Commvault, and Stephen Orban who's the General Manager of AWS Marketplace & Control Services. Gents! Welcome to theCUBE. Good to see you. >> Thank you, always a pleasure to see you Dave. >> Dave, thanks for having us. Great to be here. >> You're very welcome. Stephen, let's start with you. Look, the cloud has become a staple of digital infrastructure. I don't know where we'd be right now without being able to access enterprise services, IT services remotely, Um, but specifically, how are customers looking at backup and recovery in the cloud? Is it a kind of a replacement for existing strategies? Is it another layer of protection? How are they thinking about that? >> Yeah. Great question, Dave. And again, thanks. Thanks for having me. And I think, you know, look. If you look back to 15 years ago, when the founders of AWS had the hypothesis that many enterprises, governments, and developers were going to want access to on demand, pay as you go, IT resources in the cloud. None of us would have been able to predict that it would have matured and, um, you know become the staple that it has today over the last 15 years. But the reality is that a lot of these are enterprise customers. Many of whom have been doing their own IT infrastructure for the last 10, 20 or or multiple decades do have to kind of figure out how they deal with it. The change management of moving to the cloud, and while a lot of our customers will initially come to us because they're looking to save money or costs. Almost all of them decide to stay and go big because of the speed at which they're able to innovate on behalf of their customers. And when it comes to storage and backup, that just plays right into where they're headed and there's a variety of different techniques that customers use. Whether it be, you know, a lift and shift for a particular set of applications. Or a data center or where it, where they do very much look at how can they replace the backup and recovery that they have on premises in the cloud using solutions like what we're partnering with Commvault to do. Or completely re-imagining their architecture for net new developments that they can really move quickly for, for their customers and, and completely developing something brand new, where it is really a, um, you know a brand new replacement and innovation for, for, for what they've done in the past. >> Great. Thank you, Stephen. Ranga, I want to ask you about the D word, digital. Look, if you're not a digital business today, you're basically out of business. So my question to you Ranga is, is how have you seen customers change the way they think about data protection during what I call the forced March to digital over the last 18, 19 months? Are customers thinking about data protection differently today? >> Definitely Dave, and and thank you for having me and Stephen pleasure to join you on this CUBE interview. First, going back to Stephen's comments, can't agree more. Almost every business that we talk with today has a cloud first strategy, a cloud transmission mandate. And, you know, the reality is back to your digital comment. There are many different paths to the hybrid micro cloud. And different customers. You know, there are different parts of the journey. So as Stephen was saying, most often customers, at least from a data protection perspective. Start the conversation their thinking, hey, I have all these tapes, can I start using cloud as my air gap, long-term retention target. And before they realize they start moving their workloads into the cloud, and none of the backup and recovery facilities are going to change. So you need to continue protecting the cloud, which is where the cloud meta data protection comes in. And then they start innovating around DR Can I use cloud as my DR sites so that, you know, I don't need to meet in another site. So this is all around us, cloud transmissions, all around us. And, and the real essence of this partnership between AWS and Commvault is essentially to drive, and simplify all the paths to the cloud Regardless of whether you're going to use it as a storage target or, you know, your production data center or your DR. Disaster Recovery site. >> Yeah. So really, it's about providing that optionality for customers. I talked to a lot of customers and said, hey, our business resilience strategy was really too focused on DR. I've talked to all the customers at the other end of the spectrum said, we didn't even have a DR strategy. Now we're using the cloud for that. So it's a, it's really all over the map and you want that optionality. So Stephen, >> (Ranga cuts in) >> Go ahead, please. >> And sorry. Ransomware plays a big role in many of these considerations as well, right? Like, it's unfortunately not a question of whether you're going to be hit by ransomware. It's almost become like, what do you do when you're hit by ransomware? And the ability to use the cloud scale to immediately bring up the resources. Use the cloud backers has become a very popular choice simply because of the speed with which you can bring the business back to normal operations. The agility and the power that cloud brings to the table. >> Yeah. Ransomware is scary. You don't, you don't even need a high school degree diploma to be a ransomware-ist. You could just go on the dark web and buy ransomware as a service and do bad things. And hopefully you'll end up in jail. Stephen, we know about the success of the AWS Marketplace. You guys are partnering here. I'm interested in how that partnership, you know, kind of where it started and how it's evolving. >> Yeah. And happy to highlight on that. So look, when we, when we started AWS or when the founders of AWS started AWS, as I said, 15 years ago. We realized very early on that while we were going to be able to provide a number of tools for customers to have on demand access to compute storage, networking databases, that many particularly, enterprise and government government customers still use a wide range of tools and solutions from hundreds, if not in some cases, thousands of different partners. I mean, I talked to enterprises who who literally used thousands of of different vendors to help them deliver those solutions for their customers. So almost 10 years ago, we're almost at our 10 year anniversary for AWS Marketplace. We launched the first instantiation of AWS Marketplace, which allowed builders and customers to find, try, buy, and then deploy third-party software solutions running on Amazon Machine Instances, also known as AMI's. Natively, right in their AWS and cloud accounts to compliment what they were doing in the cloud. And over the last, nearly 10 years, we've evolved quite a bit. To the point where we support software in multiple different packaging types. Whether it be Amazon Machine Instances, containers, machine learning models, and of course, SAS and the rise of software as a service, so customers don't have to manage the software themselves. But we also support a data products through the AWS data exchange and professional services for customers who want to get services to help them integrate the software into their environments. And we now do that across a wide range of procurement options. So what used to be pay as you go Amazon Machine Instances now includes multiple different ways to contract directly. The customer can do that directly with the vendor, with their channel partner or using kind of our, our public e-commerce capabilities. And we're super excited, um, over the last couple of months, we've been partnering with Commvault to get their industry leading backup and recovery solutions listed on AWS Marketplace. Which is available for our collective customers now. So not only do they have access to Commvault's awesome solutions to help them protect against ransomware, as we talked about and, and to manage their backup and recovery environments. But they can find and deploy that directly in one click right into their AWS accounts and consolidate their, their billing relationship right on the AWS invoice. And it's been awesome to work with with Ranga and the, and the product teams at Commvault to really expose those capabilities where Commvault's using a lot of different AWS services to, to provide a really great native experience for our collective customers as they migrate to the cloud. >> Yeah. The Marketplace has been amazing. We've watched it evolve over the past decade and it's just, it's a key characteristic of cloud. Everybody has a cloud today, right? Ah, we're a cloud too, but Marketplace is unique in, in, in that it's the power of the ecosystem versus the resources of one. And Ranga, I wonder if from your perspective, if you could talk about the partnership with AWS from your view, and and specifically you've got some hard news. Would, if you could, talk about that as well. >> Absolutely. So the partnership has been extending for more than 12 years, right? So AWS and Commvault have been bringing together solutions that help customers solve the data management challenges and everything that we've been doing has been driven by the customer demand that we see, right. Customers are moving their workloads to the cloud. They are finding new ways of deploying the workloads and protecting them. You know, earlier we introduced cloud native integration with the EBS AVI's which has driven almost 70% performance improvements in backup and restore. When you look at huge customers like Coca-Cola, who have standardized on AWS and Commvault, that is the scale that they want to operate on. They manage around one through 3,000 snapshots, 1200 easy, two instances across six regions, but with just one resource dedicated for the data management strategy, right? So that's where the real built-in integration comes into play. And we've been extending it to make use of the cloud efficiencies like power management and auto-scale, and so on. Another aspect is our commitment to a radically simple customer experience. And that's, you know, I'm sure Stephen would agree. It's a big mantra at AWS as well. That's really, together, the customer demand that's brought us together to introduce combo into the AWS Marketplace, exactly the way Stephen described it. Now the hot announcement is calmer, backup and recovery is available in AWS Marketplace. So the exact four steps that Stephen mentioned: find, try, buy, and deploy everything simplified to the Marketplace so that our AWS customers can start using our more backup software in less than 20 minutes. A 60 day trial version is included in the product through Marketplace. And, you know, it's a single click buy. We use the cloud formation templates to deploy. So it becomes a super simple approach to protect the AWS workloads. And we protect a lot of them starting from EC2, RDS DynamoDB, DocumentDB, you know, the, the containers, the list just keeps going on. So it becomes a very natural extension for our customers to make it super simple, to start using Commvault data protection for the AWS workloads. >> Well, the Commvault stack is very robust. You have an extremely mature stack. I want to, I'm curious as to how this sort of came about? I mean, it had to be customer driven, I'm sure. When your customers say, hey, we're moving to the cloud, we had a lot of workloads in the cloud. We're a Commvault customer, that intersection between Commvault and AWS customer. So, so again, I presume this was customer driven, but maybe you can give us a little insight and add some color to that, Ranga. >> Every everything, you know, in this collaboration has been customer driven. We were earlier talking about the multiple paths to cloud and a very good example, and Stephen might probably add more color from his own experience at Dow Jones, but I I'll, I'll bring it to reference Parsons. Who's, you know, civil engineering leader. They started with the cloud first mandate saying, we need to start moving all our backups to the cloud, but we averted that bad actors might find it easy to go and access the backups. AWS and Commvault came together with AWS security features and Commvault brought in its own authorization controls. And now we are moved more than 14 petabytes of backup data into the cloud, and it's sort of as that, not even the backup administrators can go and patch the backups without multiple levels of authorization, right? So the customer needs, whether it is from a security perspective, performance perspective, or in this case from a simplicity perspective is really what is driving us and, and the need came exactly like that. There are many customers who have now standardized on AWS, they want to find everything related to this Marketplace. They want to use their existing, you know, the AWS contracts and also bring data strategy as part of that. So that, that's the real driver behind this. Stephen and I were hoping that we could actually announce some of the customers that have actively started using it. You know, many notable customers have been behind this innovation. And Stephen I don't know if you wanted to add more to that. >> I would just, I would just add Dave, you know, like if I look back before I joined AWS seven years ago, I was the CIO at Dow Jones. And I was leading a, a fairly big cloud migration there over a number of years. And one of the impetuses for us moving to the cloud in the first place was when Hurricane Sandy hit, we had a real disaster recovery scenario in one of our New Jersey data centers. And we had to act pretty quickly. Commvault was, was part of that solution. And I remember very clearly, even back then, back in 2013, there being options available to help us accelerate our move to the cloud. And, and just to reiterate some of the stuff that Ranga was talking about, you know, Commvault's done a great job over the last, more than a decade. Taking features from things like EBS, and S3, and TC2 and some of our networking capabilities and embedding them directly into their services so that customers are able to, you know, more quickly move their backup and recovery workloads to the cloud. So each and every one of those features was, is a result of, I'm sure, Commvault working backwards from their customer needs just as we do at AWS. And we're super excited to take that to the next level, to give customers the option to then also buy that right on their AWS invoice on AWS Marketplace. >> Yeah. I mean, we're going to have to leave it there. Stephen you've mentioned this several times, there's sort of the early days of AWS. We went back then we were talking about gigabytes and terabytes, and now we're talking about petabytes and beyond. Guys thanks so much. We really appreciate your time and sharing the news with us. >> Dave, thanks for having us. >> All right, keep it right there more from Commvault Connections 21, you're watching theCUBE.

(mid tempo digital music) >> Welcome to today's session of theCUBE' presentation of the AWS startup showcase. This is New Breakthroughs and DevOps, Data Analytics, and Cloud Management Tools. The segment is featuring Rockset and we're going to be talking about data analytics. I'm your host, Lisa Martin, and today I'm joined by one of our alumni, Venkat Venkataramani, the co-founder and CEO of Rockset, and Carl Sjogreen, the co-founder and CPO of Seesaw Learning. We're going to be talking about the fast path to real-time analytics at Seesaw. Guys, Thanks so much for joining me today. >> Thanks for having us >> Thank you for having us. >> Carl, let's go ahead and start with you. Give us an overview of Seesaw. >> Yeah, so Seesaw is a platform that brings educators, students, and families together to create engaging and learning experiences. We're really focused on elementary aged students, and have a suite of creative tools and engaging learning activities that helps get their learning and ideas out into the world and share that with family members. >> And this is used by over 10 million teachers and students and family members across 75% of the schools in the US and 150 countries. So you've got a great big global presence. >> Yeah, it's really an honor to serve so many teachers and students and families. >> I can imagine even more so now with the remote learning being such a huge focus for millions and millions across the country. Carl, let's go ahead and get the backstory. Let's talk about data. You've a ton of data on how your product is being used across millions of data points. Talk to me about the data goals that you set prior to using Rockset. >> Yeah, so, as you can imagine with that many users interacting with Seesaw, we have all sorts of information about how the product is being used, which schools, which districts, what those usage patterns look like. And before we started working with Rockset, a lot of data infrastructure was really custom built and cobbled together a bit over the years. We had a bunch of batch jobs processing data, we were using some tools, like Athena, to make that data visible to our internal customers. But we had a very sort disorganized data infrastructure that really as we've grown, we realized was getting in the way of helping our sales and marketing and support and customer success teams, really service our customers in the way that we wanted to past. >> So operationalizing that data to better serve internal users like sales and marketing, as well as your customers. Give me a picture, Carl, of those key technology challenges that you knew you needed to solve. >> Yeah, well, at the simplest level, just understanding, how an individual school or district is using Seesaw, where they're seeing success, where they need help, is a critical question for our customer support teams and frankly for our school and district partners. a lot of what they're asking us for is data about how Seesaw is being used in their school, so that they can help target interventions, They can understand where there is an opportunity to double down on where they are seeing success. >> Now, before you found Rockset, you did consider a more traditional data warehouse approach, but decided against it. Talk to me about the decision why was a traditional data warehouse not the right approach? >> Well, one of the key drivers is that, we are heavy users of DynamoDB. That's our main data store and has been tremendous aid in our scaling. Last year we scaled with the transition to remote learning, most of our metrics by, 10X and Dynamo didn't skip a beat, it was fantastic in that environment. But when we started really thinking about how to build a data infrastructure on top of it, using a sort of traditional data warehouse, a traditional ETL pipeline, it wasn't going to require a fair amount of work for us to really build that out on our own on top of Dynamo. And one of the key advantages of Rockset was that it was basically plug and play for our Dynamo instance. We turned Rockset on, connected it to our DynamoDB and were able within hours to start querying that data in ways that we hadn't before. >> Venkat let's bring you into the conversation. Let's talk about the problems that you're solving for Seesaw and also the complimentary relationship that you have with DynamoDB. >> Definitely, I think, Seesaw, big fan of the product. We have two kids in elementary school that are active users, so it's a pleasure to partner with Seesaw here. If you really think about what they're asking for, what Carl's vision was for their data stack. The way we look at is business observability. They have many customers and they want to make sure that they're doing the right thing and servicing them better. And all of their data is in a very scalable, large scale, no SEQUEL store like DynamoDB. So it makes it very easy for you to build applications, but it's very, very hard to do analytics on it. Rockset had comes with all batteries included, including real-time data connectors, with Amazon DynamoDB. And so literally you can just point Rockset at any of your Dynamo tables, even though it's a no SEQUEL store, Rockset will in real time replicate the data and automatically convert them into fast SEQUEL tables for you to do analytics on. And so within one to two seconds of data getting modified or new data arriving in DynamoDB from your application, within one to two seconds, it's available for query processing in Rockset with full feature SEQUEL. And not just that, I think another very important aspect that was very important for Seesaw is not just that they wanted me to do batch analytics. They wanted their analytics to be interactive because a lot of the time we just say something is wrong. It's good to know that, but oftentimes you have a lot more followup questions. Why is it wrong? When did it go wrong? Is it a particular release that we did? Is it something specific to the school district? Are they trying to use some part of the product more than other parts of the product and struggling with it? Or anything like that. It's really, I think it comes down to Seesaw's and Carl's vision of what that data stack should serve and how we can use that to better serve the customers. And Rockset's indexing technology, and whatnot allows you to not only get real-time in terms of data freshness, but also the interactivity that comes in ad-hoc drilling down and slicing and dicing kind of analytics that is just our bread and butter . And so that is really how I see not only us partnering with Seesaw and allowing them to get the business observerbility they care about, but also compliment Dynamo transactional databases that are massively scalable, born in the cloud, like DynamoDB. >> Carl talked to me about that complimentary relationship that Venkat just walked us through and how that is really critical to what you're trying to deliver at Seesaw. >> Yeah, well, just to reiterate what Venkat said, I think we have so much data that any question you ask about it, immediately leads to five other questions about it. We have a very seasonal business as one example. Obviously in the summertime when kids aren't in school, we have very different usage patterns, then during this time right now is our critical back to school season versus a steady state, maybe in the middle of the school year. And so really understanding how data is trending over time, how it compares year over year, what might be driving those things, is something that frankly we just haven't had the tools to really dig into. There's a lot about that, that we are still beginning to understand and dig into more. And so this iterative exploration of data is incredibly powerful to expose to our product team, our sales and marketing teams to really understand where Seesaw's working and where we still have work do with our customers. And that's so critical to us doing a good job for schools in districts. >> And how long have you been using Rockset, Carl? >> It's about six months now, maybe a little bit longer. >> Okay, so during the pandemic. So talk to me a little bit about in the last 18 months, where we saw the massive overnight transition to remote learning and there's still a lot of places that are in that or a hybrid environment. How critical was it to have Rockset to fuel real-time analytics interactivity, particularly in a very challenging last 18 month time period? >> The last 18 months have been hard for everyone, but I think have hit teachers and schools maybe harder than anyone, they have been struggling with. And then, overnight transition to remote learning challenges of returning to the classroom hybrid learning, teachers and schools are being asked to stretch in ways they have never been stretched before. And so, our real focus last year was in doing whatever we could to help them manage those transitions. And data around student attendance in a remote learning situation, data around which kids were completing lessons and which kids weren't, was really critical data to provide to our customers. And a lot of our data infrastructure had to be built out to support answering those questions in this really crazy time for schools. >> I want to talk about the data set, but I'd like to go back to Venkat 'cause what's interesting about this story is Seesaw is a customer of Rockset, Venkat, is a customer of Seesaw. Talk to me Venkat about how this has been helpful in the remote learning that your kids have been going through the last year and a half. >> Absolutely. I have two sons, nine and ten year olds, and they are in fourth and fifth grade now. And I still remember when I told them that Seesaw is considering using Rockset for the analytics, they were thrilled, they were overjoyed because finally they understood what I do for a living. (chuckling) And so that was really amazing. I think, it was a fantastic dual because for the first time I actually understood what kids do at school. I think every week at the end of the week, we would use Seesaw to just go look at, "Hey, well, let's see what you did last week." And we would see not only what the prompts and what the children were doing in the classroom, but also the comments from the educators, and then they comment back. And then we were like, "Hey, this is not how you speak to an educators." So it was really amazing to actually go through that, and so we are very, very big fans of the product, we really look forward to using it, whether it is remote learning or not, we try to use it as a family, me, my wife and the kids, as much as possible. And it's a very constant topic of conversation, every week when we are working with the kids and seeing how we can help them. >> So from an observability perspective, it sounds like it's giving parents and teachers that visibility that really without it, you don't get. >> That's absolutely correct . I think the product itself is about making connections, giving people more visibility into things that are constantly happening, but you're not in the know. Like, before Seesaw, I used to ask the kids, "How was school today? "what happened in the class?" And they'll say, "It was okay." It would be a very short answer, it wouldn't really have the depth that we are able to get from Seesaw. So, absolutely. And so it's only right that, that level of observability and that level of... Is also available for their business teams, the support teams so that they can also service all the organizations that Seesaw's working with, not only the parents and the educators and the students that are actually using the product. >> Carl, let's talk about that data stack And then I'm going to open the can on some of those impacts that it's making to your internal folks. We talked about DynamoDB, but give me an visual audio, visual picture of the data stack. >> Yeah. So, we use DynamoDB as our database of record. We're now in the process of centralizing all of our analytics into Rockset. So that rather than having different BaaS jobs in different systems, querying that data in different ways, trying to really set Rockset up as the source of truth for analytics on top of Dynamo. And then on top of Rockset, exposing that data, both to internal customers for that interactive iterative SEQUEL style queries, but also bridging that data into the other systems our business users use. So Salesforce, for example, is a big internal tool and have that data now piped into Salesforce so that a sales rep can run a report on a prospect to reach out to, or a customer that needs help getting started with Seesaw. And it's all plumbed through the Rockset infrastructure. >> From an outcome standpoint, So I mentioned sales and marketing getting that visibility, being able to act on real time data, how has it impacted sales in the last year and a half? six months rather since , it's now since months using it. >> Well, I don't know if I can draw a direct line between those things, but it's been a very busy year for Seesaw, as schools have transitioned to remote learning. And our business is really largely driven by teachers discovering our free product, finding it valuable in their classroom, and then asking their school or district leadership to purchase a school wide subscription. It's a very bottoms up sales motion. And so data on where teachers are starting to use Seesaw is the key input into our sales and marketing discussions with schools and districts. And so understanding that data quickly in real time is a key part of our sales strategy and a key part of how we grow at Seesaw over time. >> And it sounds like Rockset is empowering those users, the sales and marketing folks to really fine tune their interactions with existing customers, prospective customers. And I imagine you on the product side in terms of tuning the product. What are some of the things Carl that you've learned in the last six months that have helped you make better decisions on what you want Seesaw to deliver in the future? >> Well, one of the things that I think has been really interesting is how usage patterns have changed between the classroom and remote learning. We saw per student usage of Seesaw increased dramatically over the past year, and really understanding what that means for how the product needs to evolve to better meet teacher needs, to help organize that information, since it's now a lot more of it, really helped motivate our product roadmap over the last year. We launched a new progress dashboard that helps teachers get an added glance view of what's happening in their classroom. That was really in direct response to the changing usage patterns, that we were able to understand with better insights into data. >> And those insights allow you to pivot and iterate on the product. Venkat I want to just go back to the AWS relationship for a second. You both talked about the complimentary nature of Rockset and DynamoDB. Here we are at the AWS Startup Showcase. Venkat just give the audience a little overview of the partnership that you guys have with AWS. >> Rockset fully runs on AWS, so we are customer of AWS. We are also a partner. There are lots of amazing cloud data products that AWS has, including DynamoDB or AWS Kinesis. And so one with which we have built in integrations. So if you're managing data in AWS, we compliment and we can provide, very, very fast interactive real-time analytics on all of your datasets. So the partnership has been wonderful, we're very excited to be in the Startup Showcase. And so I hope this continuous for years to come. >> Let's talk about the synergies between a Rockset and Seesaw for a second. I know we talked about the huge value of real time analytics, especially in today's world, where we've learned many things in the last year and a half, including that real-time analytics is no longer a nice to have for a lot of industries, 'cause I think Carl as you said, if you can't get access to the data, then there's questions we can't ask. Or we can't iterate on operations, if we wait seconds for every query to load, then there's questions we can't ask. Talk to me Venkat, about how Rockset is benefiting from what you're learning from Seesaw's usage of the technology? >> Absolutely. I mean, if you go to the first part of the question on why do businesses really go after real time. What is the drive here? You might have heard the phrase, the world is going from batch to real-time. What does it really mean? What's the driving factor there? Our take on it is, I think it's about accelerating growth. Seesaw's product being amazing and it'll continue to grow, it'll continue to be a very, very important product in the world. With or without Rockset, that will be true. The way we look at once they have real-time business observability, is that inherent growth that they have, they can reach more people, they can put their product in the hands of more and more people, they can iterate faster. And at the end of the day, it is really about having this very interesting platform, very interesting architecture to really make a lot more data driven decisions and iterate much more quickly. And so in batch analytics, if you were able to make, let's say five decisions a quarter, in real time analytics you can make five decisions a day. So that's how we look at it. So that is really, I think, what is the underpinnings of why the world is going from batch to real time. And what have we learned from having a Seesaw as a customer? I think Seesaw has probably one of the largest DynamoDB installations that we have looked at. I think, we're talking about billions and billions of records, even though they have tens of millions of active users. And so I think it has been an incredible partnership working with them closely, and they have had a tremendous amount of input on our product roadmap and some of that like role-based access control and other things have already being a part of the product, thanks to the continuous feedback we get from their team. So we're delighted about this partnership and I am sure there's more input that they have, that we cannot wait to incorporate in our roadmap. >> I imagine Venkat as well, you as the parent user and your kids, you probably have some input that goes to the Seesaw side. So this seems like a very synergistic relationship. Carl, a couple more questions for you. I'd love to know how in this... Here we are kind of back to school timeframe, We've got a lot of students coming back, they're still remote learning. What are some of the things that you're excited about for this next school year that do you think Rockset is really going to fuel or power for Seesaw? >> Yeah, well, I think schools are navigating yet another transition now, from a world of remote learning to a world of back to the classroom. But back to the classroom feels very different than it does at any other back to school timeframe. Many of our users are in first or second grade. We serve early elementary age ranges and some of those students have never been in a classroom before. They are entering second grade and never having been at school. And that's hard. That's a hard transition for teachers in schools to make. And so as a partner to those schools, we want to do everything we can to help them manage that transition, in general and with Seesaw in particular. And the more we can understand how they're using Seesaw, where they're struggling with Seesaw, as part of that transition, the more we can be a good partner to them and help them really get the most value out of Seesaw, in this new world that we're living in, which is sort of like normal, and in many ways not. We are still not back to normal as far as schools are concerned. >> I'm sure though, the partnership that you provide to the teachers and the students can be a game changer in these, and still navigating some very uncertain times. Carl, last question for you. I want you to point folks to where they can go to learn more about Seesaw, and how for all those parents watching, they might be able to use this with their families. >> Yeah, well, seesaw.me is our website, and you can go to seesaw.me and learn more about Seesaw, and if any of this sounds interesting, ask your teacher, if they're not using Seesaw, to give it a look. >> Seesaw.me, excellent. Venkat, same question for you. Where do you want folks to go to learn more about Rockset and its capabilities? >> Rockset.com is our website. There is a free trial for... $300 worth of free trial credits. It's a self service platform, you don't need to talk to anybody, all the pricing and everything is out there. So, if real-time analytics and modernizing your data stack is on your roadmap, go give it a spin. >> Excellent guys. Thanks so much for joining me today, talking about real-time analytics, how it's really empowering both the data companies and the users to be able to navigate in challenging waters. Venkat, thank you, Carl, thank you for joining us. >> Thanks everyone. >> Thanks Lisa. >> For my guests, this has been our coverage of the AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics and Cloud Management Tools. I am Lisa Martin. Thanks for watching. (mid tempo music)

>> Narrator: From around the globe, It's theCUBE! Covering Fortinet Security Summit, brought to you by Fortinet. >> And welcome to the cube coverage here at the PGA champion-- Fortinet championship, where we're going to be here for Napa valley coverage of Fortinet's, the championships security summit, going on Fortinet, sponsoring the PGA, but a great guest Merritt Baer, who's the principal in the office of the CISO at Amazon web services. Great to see you. Thanks for coming on. >> Merritt: Thank you for having me. It's good to be here. >> So Fortinet, uh, big brand now, sponsoring the PGA. Pretty impressive that they're getting out there with the golf. It's very enterprise focused, a lot of action. A lot of customers here. >> Merritt: It seems like it, for sure. >> Bold move. Amazon, Amazon web services has become the gold standard in terms of cloud computing, seeing DevOps people refactoring. You've seen the rise of companies like Snowflake building on Amazon. People are moving not only to the cloud, but they're refactoring their business and security is top of mind for everyone. And obviously cybersecurity threats that Fortinet helps cover, you guys are partnering with them, is huge. What is your state of the union for cyber? What's the current situation with the threat landscape? Obviously there's no perimeter in the cloud. More end points are coming on board. The Edge is here. 5G, wavelength with outpost, a lot happening. >> That was a long question, but I'll, I'll try. So I think, you know, as always business in innovation is the driver. And security needs to be woven into that. And so I think increasingly we're seeing security not be a no shop, but be an enabler. And especially in cloud, when we're talking about the way that you do DevOps with security, I know folks don't like the term DevSecOps, but you know, to be able to do agile methodology and be able to do the short sprints that are really agile and, and innovative where you can-- So instead of nine months or whatever, nine week timelines, we're talking about short sprints that allow you to elastically scale up and down and be able to innovate really creatively. And to do that, you need to weave in your security because there's no like, okay, you pass go, you collect $200. Security is not an after the fact. So I think as part of that, of course the perimeter is dead, long live the perimeter, right? It does matter. And we can talk about that a little bit. You know, the term zero trust is really hot right now. We can dig into that if that's of interest. But I think part of this is just the business is kind of growing up. And as you alluded to we're at the start of what I think is an S curve that is just at the beginning. >> You know, I was really looking forward to Reinforced this year. It was got canceled last year, but the first inaugural event was in Boston. I remember covering that. This year it was virtual, but the keynote Steven gave was interesting, security hubs at the center of it. And I want to ask you, because I need you to share your view on how security's changed with the cloud, because there's now new things that are there to take advantage of if you're a business or an enterprise, yeah on premises, there's a standard operating procedure. You have the perimeter, et cetera. That's not there anymore, but with the cloud, there's a new, there's new ways to protect and security hub is one. What are some of the new things that cloud enables for security? >> Well, so just to clarify, like perimeters exist logically just like they do physically. So, you know, a VPC for example, would be a logical perimeter and that is very relevant, or a VPN. Now we're talking about a lot of remote work during COVID, for example. But one of the things that I think folks are really interested with Security Hub is just having that broad visibility and one of the beauties of cloud is that, you get this tactile sense of your estate and you can reason about it. So for example, when you're looking at identity and access management, you can look at something like access analyzer that will under the hood be running on a tool that our, our group came up with that is like reasoning about the permissions, because you're talking about software layers, you're talking about computer layer reasoning about security. And so another example is in inspector. We have a tool that will tell you without sending a single packet over the network, what your network reach ability is. There's just like this ability to do infrastructure as code that then allows you to do security as code. And then that allows for ephemeral and immutable infrastructures so that you could, for example, get back to a known good state. That being said, you know, you kill a, your web server gets popped and you kill it and you spin up a new one. You haven't solved your problem, right? You need to have some kind of awareness of networking and how principals work. But at the same time, there's a lot of beauties about cloud that you inherit from a security perspective to be able to work in those top layers. And that's of course the premise of cloud. >> Yeah, infrastructure as code, you mentioned that, it's awesome. And the program ability of it with, with server-less functions, you're starting to see new ways now to spin up resources. How is that changing the paradigm and creating opportunities for better security? Is it, is it more microservices? Is it, is, are there new things that people can do differently now that they didn't have a year ago or two years ago? Because you're starting to see things like server-less functions are very popular. >> So yes, and yes, I think that it is augmenting the way that we're doing business, but it's especially augmenting the way we do security in terms of automation. So server-less, under the hood, whether it's CloudWatch events or config rules, they are all a Lambda function. So that's the same thing that powers your Alexa at home. These are server-less functions and they're really simple. You can program them, you can find them on GitHub, but they are-- one way to really scale your enterprise is to have a lot of automation in place so that you put those decisions in ahead of time. So your gray area of human decision making is scaled down. So you've got, you know, what you know to be allowable, what you know to be not allowable. And then you increasingly kind of whittled down that center into things that really are novel, truly novel or high stakes or both. But the focus on automation is a little bit of a trope for us. We at Amazon like to talk about mechanisms, good intentions are not enough. If it's not someone's job, it's a hope and hope is not a plan, you know, but creating the actual, you know, computerized version of making it be done iteratively. And I think that is the key to scaling a security chain because as we all know, things can't be manual for long, or you won't be able to grow. >> I love the AWS reference. Mechanisms, one way doors, raising the bar. These are all kind of internal Amazon, but I got to ask you about the Edge. Okay. There's a lot of action going on with 5G and wavelength. Okay, and what's interesting is if the Edge becomes so much more robust, how do you guys see that security from a security posture standpoint? What should people be thinking about? Because certainly it's just a distributed Edge point. What's the security posture, How should we be thinking about Edge? >> You know, Edge is a kind of catch all, right, we're talking about Internet of Things. We're talking about points of contact. And a lot of times I think we focus so much on the confidentiality and integrity, but the availability is hugely important when we're talking about security. So one of the things that excites me is that we have so many points of contact and so many availability points at the Edge that actually, so for example, in DynamoDB, the more times you put a call on it, the more available it is because it's fresher, you've already been refreshing it, there are so many elements of this, and our core compute platform, EC2, all runs on Nitro, which is our, our custom hardware. And it's really fascinating, the availability benefits there. Like the best patching is a patching you don't have to do. And there are so many elements that are just so core to that Greengrass, you know, which is running on FreeRTOS, which has an open source software, for example, is, you know, one element of zero trust in play. And there are so many ways that we can talk about this in different incarnations. And of course that speaks to like the breadth and depth of the industries that use cloud. We're talking about automotive, we're talking about manufacturing and agriculture, and there are so many interesting use cases for the ways that we will use IOT. >> Yeah. It's interesting, you mentioned Nitro. we also got Annapurna acquisition years ago. You got latency at the Edge. You can handle low latency, high volume compute with the data. That's pretty powerful. It's a paradigm shift. That's a new dynamic. It's pretty compelling, these new architectures, most people are scratching their heads going, "okay, how do I do this, like what do I do?" >> No, you're right. So it is a security inheritance that we are extremely calculated about our hardware supply chain. And we build our own custom hardware. We build our own custom Silicon. Like, this is not a question. And you're right in that one of the things, one of the north stars that we have is that the security properties of our engineering infrastructure are built in. So there just is no button for it to be insecure. You know, like that is deliberate. And there are elements of the ways that nature works from it running, you know, with zero downtime, being able to be patched running. There are so many elements of it that are inherently security benefits that folks inherit as a product. >> Right. Well, we're here at the security summit. What are you excited for today? What's the conversations you're having here at the Fortinet security summit. >> Well, it's awesome to just meet folks and connect outside. It's beautiful outside today. I'm going to be giving a talk on securing the cloud journey and kind of that growth and moving to infrastructure as code and security as code. I'm excited about the opportunity to learn a little bit more about how folks are managing their hybrid environments, because of course, you know, I think sometimes folks perceive AWS as being like this city on a hill where we get it all right. We struggle with the same things. We empathize with the same security work. And we work on that, you know, as a principal in the office of the CISO, I spend a lot of my time on how we do security and then a lot of my time talking to customers and that empathy back and forth is really crucial. >> Yeah. And you've got to be on the bleeding edge and have the empathy. I can't help but notice your AWS crypto shirt. Tell me about the crypto, what's going on there. NFT's coming out, is there a S3 bucket at NFT now, I mean. (both laughing) >> Cryptography never goes out of style. >> I know, I'm just, I couldn't help-- We'll go back to the pyramids on that one. Yeah, no, this is not a, an advertisement for cryptocurrency. It is, I'm a fangirl of the AWS crypto team. And as a result of wearing their shirts, occasionally they send me more shirts. And I can't argue with that. >> Well, love, love, love the crypto. I'm big fan of crypto, I think crypto is awesome. Defi is amazing. New applications are going to come out. We think it's going to be pretty compelling, again, let's get today right. (laughing) >> Well, I don't think it's about like, so cryptocurrency is just like one small iteration of what we're really talking about, which is the idea that math resolves, and the idea that you can have value in your resolution that the math should resolve. And I think that is a fundamental principle and end-to-end encryption, I believe is a universal human right. >> Merritt, thank you for coming on the cube. Great, great to have you on. Thanks for sharing that awesome insight. Thanks for coming on. >> Merritt: Thank you. >> Appreciate it. Okay. CUBE coverage here in Napa valley, our remote set for Fortinet's security cybersecurity summit here as part of their PGA golf Pro-Am tournament happening here in Napa valley. I'm John Furrier. Thanks for watching.

(bright intro music) >> Welcome to this "Cube Conversation". I'm your host, Lisa Martin. This is part of our third AWS Start-up Showcase. And I'm pleased to welcome two gentlemen from Rockset, Venkat Venkataramani is here, the CEO and co-founder and Dhruba Borthakur, CTO and co-founder. Gentlemen, welcome to the program. >> Thanks for having us. >> Thank you. >> Excited to learn more about Rockset, Venkat, talk to me about Rockset and how it's putting real-time analytics within the reach of every company. >> If you see the confluent IPO, if you see where the world is going in terms of analytics, I know, we look at this, real-time analytics is like the lost frontier. Everybody wants fast queries on fresh data. Nobody wants to say, "I don't need that. You know, give me slow queries on stale data," right? I think if you see what data warehouses and data lakes have done, especially in the cloud, they've really, really made batch analytics extremely accessible, but real-time analytics still seems too clumsy, too complex, and too expensive for most people. And we are on a mission to make, you know, real-time analytics, make it very, very easy and affordable for everybody to be able to take advantage of that. So that's our, that's what we do. >> But you're right, nobody wants a stale data or slower queries. And it seems like one of the things that we learned, Venkat, sticking with you in the last 18 months of a very strange world that we're living in, is that real-time is no longer a nice to have. It's really a differentiator and table stakes for businesses in every industry. How do you make it more affordable and accessible to businesses in so many different industries? >> I think that's a great question. I think there are, at a very high level, there are two categories of use cases we see. I think there is one full category of use cases where business teams and business units are demanding almost like business observability. You know, if you think about one domain that actually understood real-time and made everything work in real-time is the DevOps world, you know, metrics and monitoring coming out of like, you know, all these machines and because they really want to know as soon as something goes wrong, immediately, I want to, you know, be able to dive in and click and see what happens. But now businesses are demanding the same thing, right? Like a CEO wants to know, "Are we on track to hit our quarterly estimates or not? And tell me now what's happening," because you know, the larger the company, the more complex that have any operations dashboards are. And, you know, if you don't give them real-time visibility, the window of opportunity to do something about it disappears. And so they are really, businesses is really demanding that. And so that is one big use case we have. And the other strange thing we're also seeing is that customers are demanding real-time even from the products they are using. So you could be using a SaaS product for sales automation, support automation, marketing automation. Now I don't want to use a product if it doesn't have real-time analytics baked into the product itself. And so all these software companies, you know, providing a SaaS service to their cloud customers and clients, they are also looking to actually, you know, their proof of value really comes from the analytics that they can show within the product. And if that is not interactive and real-time, then they are also going to be left behind. So it's really a huge differentiator whether you're building a software product or your running a business, the real-time observability gives you a window of opportunity to actually do something about, you know, when something goes wrong, you can actually act on it very, very quickly. >> Right, which is absolutely critical. Dhruba, I want to get your take on this. As the CTO and co-founder as I introduced you, what were some of the gaps in the market back in 2016 that you saw that really necessitated the development of this technology? >> Yeah, for real-time analytics, the difference compared to what it was earlier is that all your things used to be a lot of batch processes. Again, the reason being because there was something called MapReduce, and that was a scanning system that was kind of a invention from Google, which talked about processing big data sets. And it was about scanning, scanning large data sets to give answers. Whereas for real-time analytics, the new trend is that how can you index these big datasets so that you can answer queries really fast? So this is what Rockset does as well, is that we have capabilities to index humongous amounts of data cheaply, efficiently, and economically feasible for our customers. And that's why query is the leverage the index to give fast (indistinct). This is one of the big changes. The other change obviously is that it has moved to the cloud, right? A lot of analytics have moved to the cloud. So Rockset is built natively for the cloud, which is why we can scale up, scale down resources when queries come and we can provide a great (indistinct) for people as data latency, and as far as query latencies comes on, both of these things. So these two trends, I think, are kind of the power behind moving, making people use more real-time analytics. >> Right, and as Venkat was talking about how it's an absolute differentiator for businesses, you know, last year we saw this really, this quick, all these quick pivots to survive and ultimately thrive. And we're seeing the businesses now coming out of this, that we're able to do that, and we're able to pivot to digital, to be successful and to out-compete those who maybe were not as fast. I saw that recently, Venkat, you guys had a new product release a few weeks ago, major product release, that is making real-time analytics on streaming data sources like Apache Kafka, Amazon Kinesis, Amazon DynamoDB, and data lakes a lot more accessible and affordable. Breakdown that launch for me, and how is it doing the accessibility and affordability that you talked about before? >> Extremely good question. So we're really excited about what we call SQL-based roll-ups, is what we call that release. So what does that do? So if you think about real-time analytics and even teeing off the previous question you asked on what is the gap in the market? The gap in the market is really, all that houses and lakes are built for batch. You know, they're really good at letting people accumulate huge volumes of data, and once a week, analyst asking a question, generating a report, and everybody's looking at it. And with real-time, the data never stops coming. The queries never stop coming. So how do you, if I want real-time metrics on all this huge volumes of data coming in, now if I drain it into a huge data lake and then I'm doing analytics on that, it gets very expensive and very complex very quickly. And so the new release that we had is called SQL-based roll-ups, where simply using SQL, you can define any real-time metric that you want to track across any dimensions you care about. It could be geo demographic and other dimensions you care about that and Rockset will automatically maintain all those real-time metrics for you in real-time in a highly accurate fashion. So you never have to doubt whether the metrics are valid and it will be accurate up to the second. And the best part is you don't have to learn a new language. You can actually use SQL to define those metrics and Rockset will automatically maintain that and scale that for you in the cloud. And that, I think, reduces the barrier. So like if somebody wants to build a real-time, you know, track something for their business in real-time, you know, you have to duct tape together multiple, disparate components and systems that were never meant to work with each other. Now you have a real-time database built for the cloud that is fully, you know, supports full feature SQL. So you can do this in a matter of minutes, which would probably take you days or weeks with alternate technologies. >> That's a dramatic X reduction in time there. I want to mention the Snowflake IPO since you guys mentioned the Confluent IPO. You say that Rockset does for real-time, what Snowflake did for batch. Dhruba, I want to get your perspective on that. Tell me about that. What do you mean by that? >> Yeah, so like we see this trend in the market where lot of analytics, which are very batch, they get a lot of value if they've moved more real-time, right? Like Venkat mentioned, when analytics powers, actual products, which need to use analytics into their, to make the product better. So Rockset very much plays in this area. So Rockset is the only solution. I shouldn't say solution. It's a database, it's a real-time database, which powers these kind of analytic systems. If you don't use Rockset, then you might be using maybe a warehouse or something, but you cannot get real-time because there is always a latency of putting data into the warehouse. It could be minutes, it could be hours. And then also you don't get too many people making concurrent queries on the warehouse. So this is another difference for real-time analytics because it powers applications, the query volume could be large. So that's why you need a real-time database and not a real-time warehouse or any other technologies for this. And this trend has really caught up because most people have either, are pretty much into this journey. You asked me this previous question about what has changed since 2016 as well. And this is a journey that most enterprises we see are already embarking upon. >> One thing too, that we're seeing is that more and more applications are becoming data intensive applications, right? We think of whether it's Instagram or DoorDash or whatnot, or even our banking app, we expect to have the information updated immediately. How do you help, Dhruba, sticking with you, how do you help businesses build and power those data intensive applications that the consumers are demanding? >> That's a great question. And we have booked, me and Venkat, we have seen these data applications at large scale when we were at Facebook earlier. We were both parts of the Facebook team. So we saw how real-time was really important for building that kind of a business, that was social media. But now we are taking the same kind of back ends, which can scale to like huge volumes of data to the enterprises as well. Venkat, do you have anything to add? >> Yeah, I think when you're trying to go from batch to real-time, you're 100% spot on that, a static report, a static dashboard actually becomes an application, becomes a data application, and it has to be interactive. So you're not just showing a newspaper where you just get to read. You want to click and deep dive, do slice and dice the data to not only understand what happened, but why it happened and come up with hypotheses to figure out what I want to do with it. So the interactivity is important and the real-timeliness now it becomes important. So the way we think about it is like, once you go into real-time analytics, you know, the data never stops coming. That's obvious. Data freshness is important. But the queries never stop coming also because one, when your dashboards and metrics are getting up to date real-time, you really want alerts and anomaly detection to be automatically built in. And so you don't even have to look at the graphs once a week. When something is off, the system will come and tap on your shoulder and say, "Hey, something is going on." And so that really is a real-time application at that point, because it's constantly looking at the data and querying on your behalf and only alerting you when something, actually, is interesting happening that you might need to look at. So yeah, the whole movement towards data applications and data intensive apps is a huge use case for us. I think most of our customers, I would say, are building a data application in one shape or form or another. >> And if I think of use cases like cutthroat customer 360, you know, as customers and consumers of whatever product or solution we're talking about, we expect that these brands know who we are, know what we've done with them, what we've bought, what to show me next is what I expect whether again, it's my bank or it's Instagram or something else. So that personalization approach is absolutely critical, and I imagine another big game changer, differentiator for the customers that use Rockset. What do you guys think about that? >> Absolutely, personalized recommendation is a huge use case. We see this all where we have, you know, Ritual is one of the customers. We have a case study on that, I think. They want to personalize. They generate offline recommendations for anything that the user is buying, but they want to use behavioral data from the product to personalize that experience and combine the two before they serve anything on the checkout lane, right? We also see in B2B companies, real-time analytics and data applications becoming a very important thing. And we have another customer, Command Alkon, who, you know, they have a supply chain platform for heavy construction and 80% of concrete in North America flows through their platform, for example. And what they want to know in real-time is reporting on how many concrete trucks are arriving at a big construction site, which ones are late and whatnot. And the real-time, you know, analytics needs to be accurate and needs to be, you know, up to the second, you know, don't tell me what trucks were, you know, coming like an hour ago. No, I need this right now. And so even in a B2B platform, we see that very similar trend trend where real-time reporting, real-time search, real-time indexing is actually a very, very important piece to the puzzle. And not just for B to C examples that you said, and the Instagram comment is also very appropriate because a hedge fund customer came to us and said, "I have kind of a dashboards built on top of like Snowflake. They're taking two to five seconds and I have certain parts of my dashboards, but I am actually having 50/60 visualizations. You do the math, it takes many minutes to load. And so they said, "Hey, you have some indexing deck. Can you make this faster?" Three weeks later, the queries that would take two to five seconds on a traditional warehouse or a cloud data warehouse came back in 18 milliseconds with Rockset. And so it is so fast that they said, you know, "If my internal dashboards are not as fast as Instagram, no one in my company uses it." These are their words. And so they are really, you know, the speed is really, really important. The scale is really, really important. Data freshness is important. If you combine all of these things and also make it simple for people to access with SQL-based, that's really the real unique value prop that we have a Rockset, which is what our customers love. >> You brought up something interesting, Venkat, that kind of made me think of the employee experience. You know, we always think of the customer 360. The customer experience with the employee experience, in my opinion, is inextricably linked. The employees have to have access to what they need to deliver and help these great customer relationships. And as you were saying, you know, the employees are expecting databases to be as fast as they see on Instagram, when they're, you know, surfing on their free time. Then adoption, I imagine, gets better, obviously, than the benefit from the end user and customers' perspective is that speed. Talk to me a little bit about how Rockset, and I would like to get both of your opinions here, is a facilitator of that employee productivity for your customers. >> This is a great question. In fact, the same hedge fund, you know, customer, I pushed them to go and measure how many times do people even look at all the data that you produce? (laughs) How many analysts and investors actually use your dashboards and ask them to go investigate at that. And one of the things that they eventually showed me was there was a huge uptake and their dashboards went from two to three second kind of like, you know, lags to 18 milliseconds. They almost got the daily active user for their own internal dashboards to be almost going from five people to the entire company, you know, so I think you're absolutely spot on. So it really goes back to, you know, really leveraging the data and actually doing something about it. Like, you know, if I ask a question and it's going to, you know, system is going to take 20 minutes to answer that, you know, I will probably not ask as many questions as I want to. When it becomes interactive and very, very fast, and all of a sudden, I not only start with a question and, you know, I can ask a follow-up question and then another follow-up question and make it really drive that to, you know, a conclusion and I can actually act upon it. And this really accelerates. So even if you kind of like, look at the macro, you hear these phrases, the world is going from batch to real-time, and in my opinion, when I look at this, people want to, you know, accelerate their growth. People want to make faster decisions. People want to get to, what can I do about this and get actionable insights. And that is not really going to come from systems that take 20 minutes to give a response. It's going to really come from systems that are interactive and real-time, and that's really the need for acceleration is what's really driving this movement from batch to real-time. And we're very happy to facilitate that and accelerate that moment. >> And it really drives the opportunity for your customers to monetize more and more data so that they can actually act on it, as you said, in real-time and do something about it, whether it's a positive experience or it is, you know, remediating a challenge. Last question guys, since we're almost out of time here, but I want to understand, talk to me about the Rockset-AWS partnership and what the value is for your customers. >> Okay, yeah. I'll get to that in a second, but I wanted to add something to your previous question. I think my opinion for all the customers that we see is that real-time analytics is addictive. Once they get used to it, they can go back to the old stuff. So this is what we have found with all our customers. So, yeah, for the AWS question, I think maybe Venkat can answer that better than me. >> Yeah, I mean, we love partnering with AWS. I think, they are the world's leader when it comes to public clouds. We have a lot of joint happy customers that are all AWS customers. Rockset is entirely built on top of AWS, and we love that. And there is a lot of integrations that Rockset natively comes with. So if you're already managing your data in AWS, you know, there are no data transfer costs or anything like that involved for you to also, you know, index that data in Rockset and actually build real-time applications and stream the data to Rockset. So I think the partnership goes in very, very deep in terms of like, we are an AWS customer, we are a partner and we, you know, our go-to market teams work with them. And so, yeah, we're very, very happy, you know, like, AWS fanboys here, yeah. >> Excellent, it sounds like a very great synergistic collaborative relationship, and I love, Dhruba, what you said. This is like, this is a great quote. "Real-time analytics is addictive." That sounds to me like a good addiction (all subtly laugh) for businesses and every industry to take out. Guys, it's been a pleasure talking to you. Thank you for joining me, talking to the audience about Rockset, what differentiates you, and how you're helping customers really improve their customer productivity, their employee productivity, and beyond. We appreciate your time. >> Thanks, Lisa. >> Thank you, thanks a lot. >> For my guests, I'm Lisa Martin. You're watching this "Cube Conversation". (bright ending music)

(bouncy music) >> Welcome to this Cube Conversation, I'm Lisa Martin. I'm joined by Erez Berkner, the CEO and co-founder of lumigo. Erez, welcome to the program. >> Hey Lisa, thank you for having me. Glad to be here. >> Excellent, we're going to have a great conversation. We're going to be talking about the growing trend of using cloud native and serverless. But before we do, Erez, give our audience an overview of lumigo. >> Excellent, so lumigo is an observability platform. Basically allowing developer, architects, the technology person in the organization to understand what's going on with his modern cloud, with his serverless, with his cloud native application. So at the end of the day, lumigo as assess platform, allow you to know what's happening, get visibility, and be able to get to the root cause of issues, many times before they actually hit your production. >> I saw on your website, in terms of speed, getting up and running quickly, in four minutes with four clicks. Tell me how developers do this that quickly. >> Yeah, that's actually great point. Because in general, when we talk about the modern cloud, people are really fed up with deploying agents, long processes of servers, and more and more we see the trend towards APIs, toward code libraries. At the end of the day, at the heart of lumigo, we built a very strong automation engine based on APIs, based on lomdalier integration. And this allows a developer to basically connect lumigo via the APIs in couple of clicks. Doesn't require code changes, deployment of agents, deployment of services. And this is why it's so fast, because it's lightweight. And that's a trend of managed services, of serverless, and lumigo is another stone in that wall. >> Excellent, lightweight, key there. Define serverless, what is considered serverless? >> Mmm, ooh, don't get me involved in dispute of those definitions. But I can share my view, but this is a.. Anyone, I would say, have his own definition. But the main concept with serverless is at the end of the day, really, like it says, serverless. You don't deploy a server. You don't rent a server, you don't manage a server, you don't deploy an operating system, you don't patch a server. You don't take care of scalability, of high visibility. Basically, all the chores of managing, of maintaining a server, basically go away. Now, they don't really go away. Somebody else is dealing with them. So there is a server, but it's not your server to manage. And that someone is a cloud provider, is Amazon, is Microsoft, is Google, it's IBM. And this is how I view serverless. Basically, a managed service that doesn't require to deploy or manage a server, and you use it via APIs. And if you think about that, in the past when serverless started, 2015, serverless was function as a service, Lambda, AWS started that. But today, in 2021, serverless, yeah, it's function as a service, it's Lambda, but it's also storage as a service, like S3, and data as a service, like Snowflake, like DynamoDB. And queue as a service like SNS, like EventBridge, like Kenesis. And even Stripe, payment a as service, and Twilio, and SendGrid. So all these API based services, that you just consume, and they're like Lego pieces that you connect together and you just connect and you go, and you start working and they up and running, this is how I define serverless today. And that's basically allowing you to run any application today with zero servers. >> That's a great definition, that nice and clean, and I think the Lego bricks really kind of clicked in my mind when you talked about that. Let's talk now about for business critical production applications, what are you seeing in terms of adoption of serverless for those cases? >> That's a great question, because I think that we are in a critical point of time, in cloud native, in modern cloud, in serverless market. And I think it's an evolution. You know, when we started, again, back in 2015, serverless was just one or two services. But we got to a critical mass of services, including DynamoDB and S3 and Lambda and EventBridge and all the other services, that step function, that basically allow you to build your application based on serverless. And this critical point of the architecture of serverless being mature enough, being wide enough, to allow you to do what you want, to have the confidence running serverless in production, to know that you have the tooling that you used to have in the past to monitor, to debug, to secure, to understand cost, all of this are really coming together this year. We actually see this year, and a bit of end of last year, but this is what's driving a trend in the industry. I think it's still not known enough to many of the organizations, or not wide enough, or not public enough. But our customers are focused on cloud native and serverless. And we've seen a dramatic change in the last six months. And the main change is organizations that used to play around with serverless, that used to do non-business critical usage of serverless, because it's easy, because it makes sense, because it's fast, all of a sudden they got the confidence to do that with their business critical application in production. And this is a shift that we're seeing. And that goes many times with the technology maturity. You start, you play around with something, it makes sense, it makes sense, you get confidence, and boom! This become more and more mainstream technology. And we're at the verge of that. >> In terms of a catalyst for that confidence, do you think that the events, the world events of the last 12 months and this acceleration of digital transformation, has that played any part in the maturation of the technology that's giving customers the confidence to adopt serverless? >> Yeah, I think it's fascinating, what we're seeing. Because I think the last event really push a organization to innovate. Because of different reason, because they don't have the head count, so they need to reduce the maintenance that they do, they need to reduce the developer head count, the DevOps head count, they need to reduce costs. Serverless is running only when it need to run, so you pay only for what you use. So this is another method that our customer, for example, reduce their cost. So I think beyond the maturity of the architecture, the push forward for optimization, for lower usage or lower usage of engineering force, really pushed serverless forward. And this paradigm, once it worked for one team, it's viral. It's viral with in organization and the cross-organization. So this team managed to reduce 50% of the cost, and 70% of the developers that need to maintain the production. Let's duplicate that. And let's do that four times, and five times, and 10 times. And this is the point in time that we are. So that's a trend and I think it's very much impacted by the world economics. >> Interesting, that trend of virality. Let's dig into, you mentioned a couple of benefits. I heard reduction in total cost of ownership, or costs. Talk to me about the lumigo solution, the technology, and what some of those key benefits are that it is consistently delivering to your customers. >> So I think the basic is that serverless makes a lot of sense, economical, maintenance. That's why the cloud providers are putting so much effort and power in delivering more and more serverless maturity. One of the challenges that we see for almost any organization adopting the new technology, it goes back to we understand the values, but at the end of the day I need to make sure that if something goes wrong in production, I will know about it and I will know how to react and fix it in a matter of minutes. 'Cause that's my service, that's my business. And I know how to do it in a server world, where there's one server or three servers, and everything running in the same server. I have the tools for that. And I want to go serverless, I want to go cloud native, but all of a sudden there are dozens of services that I consume via APIs and they're a part of a bigger picture of my application. So I'm lacking many times the confidence, the tools, the awareness of, something goes wrong, I'll know about it, and I'll be able to fix it. And this is where lumigo comes in. So we built lumigo from the ground up to be very much focused on the modern cloud, on serverless. And that means two main things that we provide for our customers. One is, I would say one thing. We provide confidence. You can use serverless in production, and you can rest assured that if something goes wrong, you will be the one alerting and we'll give you all the information to debug it. And we do it by two main things. One is the visibility that we create. Because we're connected to the environment, we alert on things that are relevant to serverless. It's not about CPU, it's not about a iO. It's about concurrency limits, it's about cold start, it's about time outs, it's about reaching duration limits. These are the things that we know to alert you about. It's very specific to the serverless services. And it's not a generic metric, it's serverless metric. So that's number one, visibility, getting alert whenever something is about to go wrong. But what do you do then? Let's say I have one million invocations a day, and one of them is actually, I have a trigger, something went wrong. And this is where lumigo allow the developers to debug. Basically, you click on a specific issue, and lumigo tell you the entire story of what happened, from the very beginning, an API gateway triggering a Lambda, right into DynamoDB, triggering an Lambda, it tell you the entire story end to end of what happened with that specific request, with inputs, with outputs, with environment variables. All the things the developer need in order to debug, to find the root cause, and then fix it in matter of minutes. And that's the game-changer that allow those organizations to run serverless with confidence. >> You talk about confidence, it's a word that I hear often when I'm talking with customers of vendors. It's not something to be underestimated. It's incredibly important that technology provide that confidence, especially given the events of the last year and a half that we've seen where suddenly folks couldn't get into data centers, for example. Talk to me a little bit about some of the customers. I saw from your website some great brand names, but talk to me about a customer that you think really not only has that confidence that lumigo is delivering, but is really changing their business and their approach to modern monitoring with lumigo. >> Yeah, so there are several interesting. I'll choose maybe one of the more interesting cases, a company called Medtronic. It's one of the largest medical device companies in the U.S. And it's very interesting because they have an IoT backend. Basically they have medical devices around the world that send IoT information back to their cloud. And they get metrics, they run machine learning on that. And they took a strategic decision to run the system with serverless. Because it can scale automatically, because it can deploy one more million devices and they don't need to change anything, and many, many other benefits of serverless. And we met them back in 20, end of 2019. They were looking for exactly a solution that allows them to get issues and drill down to analyze those issues. And they were just in the beginning. The early days they had 20 million invocations, requests per month. They knew they were going to scale, they knew that when they scale, they cannot correlate logs, and try to understand what happened manually. They need a professional tool. And this is where they started using lumigo. And today, a year and a half after, they reached one billion invocations a month. Again, the same concept, IoT devices, medical devices, sending metrics and information for the backend for processing. And today, lumigo is monitoring everything in that environment. And alert them from, you're about to have a problem, or you have an application error, or you have high latency, you have spike of cost, all of that are covered by lumigo. And the developers, once they get this to slot, to play the duty, you're just able to click on it, and drill down and see, one by one, requests that created the trigger that alert. And they can understand, again, the inputs, the output, the logs, the return values, everything. I call it debugging heaven. Because it's always there, it's always post-mortem, you don't need to do anything. At the same time you get the visibility and you can fix it, because this is their production, this is their business critical application. >> Debugging heaven, I love that. That's for developers, that is probably a Nirvana state. I want to wrap up Erez, just giving our folks in the audience an overview of the relationship that lumigo has with AWS. >> AWS is one of our strongest partner. I think there's a great synergy working with AWS. We've been partners for the last three years. And I think the reason for the... You know, we're still... AWS has thousands, tens of thousands of partners. I think that this partnership is specifically strong because there is a win-win relationship over here. On the one hand side lumigo is very much invested on Amazon. Our customers are mostly Amazon customers, and we are solving, providing confidence for those customers to run serverless in production, and answering a need of a customer. And this is also the win for Amazon. Amazon is basically have a great, great technology of serverless. But the lack of visibility, the lack of confidence, is hindering the adoption. And Amazon decided to work with lumigo, saying, we'll develop the core, we'll develop the services, we'll develop the serverless architecture, and you can use lumigo for monitoring, for debugging, for everything that you need in order to run that in production. And that's been very, very strong relationship that just grows as we develop together. And it's been on working together with customers, introducing customers, but also on the technology level. For the audience who sees Amazon announcement on serverless, many times lumigo is a design partner. It's part of the announcement, of lumigo was a design partner and the launch partner, and support the new feature out of the box. This is because we want to get the support as soon as possible, as soon as new features are released. So that's where we are today. >> Sounds like a very collaborative and symbiotic relationship. Erez, thank you for joining me on the program today, talking to us about some of the trends in serverless, some of the things that are catalyzing adoption, that visibility, that confidence, that lumigo delivers to its customers. We thank you for your time. >> Excellent, thank you very much Lisa. Have a good day. >> You too! For Erez Berkner, I'm Lisa Martin. Thanks for watching this Cube Conversation. (bouncy music)

(upbeat music) >> Okay, we're back. We're here in the main stage in Cloud City. I'm John Furrier and Dave Vellante. Normally, we're over there on theCUBE set, but here we've got a special presentation. We'll talk about Totogi and the new CEO of Totogi is Danielle, who is also the CEO of TelcoDR, Digital Revolution. Great to see you. And of course, Robin Langley, we interviewed you in theCUBE, CTO of Totogi. This is a main stage conversation because this is the big news. >> Yeah. >> You guys launched there with a hundred million dollar investment. We covered that news a couple weeks ago and you as the CEO. What's the story. Tell us what is happening with Totogi? Why such a big focus? What's the big push? >> Yeah, I'm really excited about Totogi because I really think this team is working to build public cloud tools for Telco the right way. It's everything I've been talking about. I talked about it yesterday in my keynote and this is really the execution of that vision. So, I'm super excited about that. A couple of days ago, Rob and I were talking about the charging system, but there's another product that Totogi introduced to the world and that's the webscale BSS system. So I think we're going to talk about that today. It's going to be great. >> Let's get into actually the charging system, which was great processing here. What is this focus? What is BSS about with cloud? How does the public cloud innovation change the game with this? >> Well, a little bit like charging. I mean, there are maybe, you know, a hundred plus BSS systems out there, why does the world need yet another BSS? And I think one thing is we're coupling up with public cloud, which gives it that webscale element. Right? We can have a platform. Never do another upgrade again, which I think is really exciting. But I think the really key thing that we're working on is we're building on top of an open API standard. And a lot of vendors talk about their APIs, why is this different? These are standards developed by TM forum, right? It's an independent body in our industry. They've been working on these, sorry, open APIs, and all the different vendors signed a manifesto that say, "I pledge. I pledge to support the open API", but if you look at the leaderboard and everyone is Sub10, Sub5, right? And so it's kind of like, going through the actions and not falling, you know, saying it, but not following it up and we're doing it. >> Wow, so... >> Yeah. >> Dave: Robin, you guys just popped up on the leaderboard. You went from a standing start to, I think more than 10. >> Yeah. >> I don't think that's ever been done before, has it? >> No, so we were out there. We published 12 APIs and we've got a quote from, you know, TM forums saying, essentially I've never seen anyone move so fast and to publish. And it's our intent to publish, you know, 50 plus, all of their APIs by the end of the year. >> So, how were you able to do that? I mean, like, were you holding them back? Just kind of dumping them on one day? This is the nature of the new business, isn't it? >> Yeah, absolutely and then you think about BSS. It's just, you know, been known for years to be a spaghetti of, you know, applications, you know, disparate data, data being duplicated, systems not talking to each other, lots of different interface types. And it was crying out to be just, you know, sold properly in the cloud. And the public cloud is perfect for this. You know, we can build a model and start, rather than looking at the applications first, you know, let's look at the model, the unified model and build on those open APIs and then start to, you know, allow people to come in and create an ecosystem of applications all using that same model. >> If you don't mind me asking you, if you can explain. 'Cause we talked before we weren't on camera, but we talked about the cloud and you were explaining to me how this is perfect for the challenges that you guys are trying to solve. What about the public cloud dynamic or innovation component that you guys are leveraging? Take us through a little bit on that, because I think that's a big story here that's under the covers is... >> Yeah. >> What you're capable of doing here. Do you mind explaining? >> Yeah, no, absolutely. So the cloud gives us this true scalability across everything. You know, we can scale to billions of records. So we can hook in, you know, to suck in data from, you know, our on-premise systems anywhere. We have, you know, a product called Devflow, so we used to do that. And it can really allow us to bring that data in, scale-out, use standard term cloud innovations, like Lambda functions and AWS, you know, DynamoDB, and present that, you know, through that open API. So we can use, you know graphQL, you know, present that with rest on top. And so you can then build on top of that. You can take any low code, no code application building tool you like, put that on top and then start building your own ecosystem. You can build inventory systems, CRM, anything you like. >> Well one thing that's really interesting about these projects is they usually take months, years to deploy, right? And what we're doing is we're providing, almost BSS as a service, right? It's an API layer that anyone can go to. Maybe you need to use it for five minutes, five months, five years, right? With the open standard and your own developers can learn how to use this text stack and code to it doesn't require us. And so we're really trying to get away from being an SI, you know, systems integrator or heavy services revenue, and instead build the product that enables the telcos to use their own people, to build the applications that they, they know what they want, and so, here you go. >> It's a platform. >> Yeah. >> It's a platform. >> So, how do you connect to systems on the ground? Like what's the modern approach to doing that? >> Yeah, go for it. >> Yeah so, telcos have, you know, a huge amount of data on premise. They have difficulties you can get to it. So, as I mentioned before, we had this Devflows product and it has connectors. We have like 30 plus connectors to all the standard sort of, billing systems, CRM systems, you know, we can hook into things like Salesforce. And we can create either, you know, couple of a real-time interface in there, or we can start to suck data into the cloud and then make it available. So, if they want to start with a nice, easy step and just build slowly, we can just hook in and pull that information out. If there may be, you know, an attribute that you want to, you know, use in some of that application, you can easily get to it. And then, you know, over time you start to build your data into the cloud and then you've got the scale, you know, and all the innovations of that brings with it. >> So is Devflow an on-ramp, if you will, for the public cloud, is that the way you were thinking about it? >> Yeah. >> Yeah. Yeah, I mean, I call it the slurper. (group chuckles) Right. I mean, these telcos have, like Robin was saying, spaghetti systems that have been, you know, customized and connected and integrated. I mean, it is a jungle out there of data. They're not going to be able to move this in one step. We just think of like a pile of spaghetti, like the whole bowl. >> Overcooked spaghetti. >> Right overcooked, the whole bowl comes out and it's really hard to just pull out one noodle and the rest is there and what are you going to do? And so the slurper, right, Devflows, allows you to select which data you want to pull out. It could be one time, you could have it sync. You don't have to do the whole thing and it doesn't disrupt the production environment that's on-premise. But now you're starting to move your data into the public cloud and then like Robin was saying, you can throw it up against quick sites. You can throw it up against different Amazon services. You can create new applications. And so it's not this like, you know, big bang kind of approach. You can start to do it in pieces and I think that's what the industry needs. >> I'm talking about this the other day, when we're talk about charging. What a lot of vendors will do is they'll put a wrapper around it, containerize it and then shove it into the public cloud and say, "Okay". >> Check mark. >> Yeah a checkbox. And it affects how they price, if they price the same way. But we talked a lot about pricing the other day, really pricing like cloud, consumption pricing. How are you pricing in this case? >> Same with the charging system. The BSS system is paid by the use, paid by the API call. So, really excited to introduce yet, again, a free tier. We think we're doing 500 million API calls per month for free. We think this is great for a smaller telco where like, you're experimenting and just getting to know the system and before you like, go all in and buy. And I think that API pricing is going to go right at the heart of some of these vendors that love to charge by the subscriber or a perpetual license agreement, right? They're not quite moving as a service. And so, yeah. >> Are you saying, they're going to be disruptive in the pricing in terms of lower cost or more, consumable. >> And I think it's also an easier on ramp, right? It's easier to start paying by the use and experimenting. And it's really easy, just like I was talking about with charging, where you're going to get the same great product that you would sell to a tier one at a price that you can afford. And now those smaller two or three guys aren't having to make a trade off between great technology, but I'm paying through the nose or sacrifice on the tech, but I can afford it. And so, I think you're going to see this ecosystem of people starting to learn how to code and think in this way. Telcos have already decided that they want to adopt the TM forum, open APIs. They're on all the RFPs. Do you support it? Everyone says they support it, but we don't see anyone really doing it. They're not on the leaderboard. >> And there's transparency, because you're pricing by API call, right? Versus the spaghetti, you guys call it, the hairball of what am I paying for? >> Right, you're getting, all of this. It's by the subscriber. It's millions and millions of dollars. Oh, and you know, you're going to need to buy a bunch of consulting revenue to make it all work and talk to each other. Pay up, right? And that's what we're living in today. And I'm taking us to the, you know, public cloud future by the API. >> This is the big cloud revolution. It's unbundling has been a really big part of the consumption of technology paid by the usage, get in, get some value, get some data, understand what it is, double down on it, iterate. >> Put it up with different services that are available that we don't have, but Amazon uses, right? They have call centers up there, they have ML that you may want to use like, start using it, start coding, start learning about the AWS tech stack. >> So is it available now? >> Yeah. >> Yeah. No, it's available now. We've already published the swagger for the BSS APIs. So, you know, they can come on board, they can go to access to all the API straight away and start using it. They can load up their favorite REST clients and then start developing. >> So you got a dozen APIs today. Where are we headed? What can we expect? >> All by the end of the year. There's over 50 APIs. You know, the number one guy on the board is at like 22, 21, 22 APIs covered. We'll be 50 plus by the end of the year. And we're just going to blow doors. >> The API economy has come to telco. >> Yeah, I mean, it's really BSS' Lego pieces, right. Assembling these different components and really opening it up. And I think there's been a lot of power by the vendors to keep it locked down, keep it close. Yes, we have an API, but you got to use our people to do it. Here's the hundreds of thousands or millions of dollars that you're going to pay us and keep us in business, and fat and happy, and I'm coming right in on the low end. Right, dropping that price, opening it up. I think telcos are going to love it. >> Well, Mike, you said too, you'll allow the smaller telcos to have the same, actually, better capabilities than the larger telcos, right? Maybe the stack's not as mature or whatever, but they'll get there and they'll get there with a simpler, easier to understand pricing model and way, way faster. >> Yeah. >> All right and that's where the disruption comes. >> And I Think this is where AWS has really done well as a hyper scaler against their competition, is that they've really gotten to market very quickly with their services. Maybe they're not perfect, but they ship 'em. And they get them out there and they get people using them. They use them internally and they get them out. And I think this is where maybe some of the other hyperscalers, they hold them back and they wait until they're a little bit more mature. And AWS is one because they've been fast. And I want to sort of copy that feat. >> I think your idea of subscriber love in your keynote, and I think applies here because Amazon web services has done such a great job of working backwards from the customer. So they'd ship it fast on used cases that they know have been proven through customer interactions. >> Yep. >> They don't just make up new features. And then they iterate. They go, "Okay". >> Start simple, grow on that, learn from the market. What are people using? What are they not using? Iterate, iterate, iterate. >> Okay, so with that in mind, working backwards from your customer, how do you see the feature set evolving for this functionality? How do you see it evolving as a product? >> Yeah, I mean, I think all of the BSS systems today have been designed with manual people on the other side of the screen, right? And we've seen chat bots take off, we've seen, you know, using chat as support. I think we need to start getting into more automation right? Which is really going to change up telco, right? They have thousands of customer support agents and you're like, "Dude, I just want a SIM, that's all I need". >> Yeah. >> Just like, where do I push a button and send an Uber to my house and drop it off or eSim. And so, speeding up business, empowering the subscriber. We know how to interact, we just went through COVID where we learned about different apps that overnight, you can like order all of your groceries and order all of your food and there it is, and it was contactless and... >> It's funny, you said future of work, which we love that term, "work". Workloads, work force, you got all these kind of new dynamics going on with cloud enablement and the changes is radical. And the value is there. There's value opportunities. >> I mean like, you know, where are the ARVR applications, right? Where your agent pops. I saw the demo. There's a strife in Austin and they're going to kill me 'cause I can't remember their name. But they had a little on your mobile phone, a little holographic customer support. Like, "How can I help you"? Right. And I'm like, "Where's that", like, imagine you're like, ATT, you're not like on the phone for like an hour and a half trying to like, figure out what's wrong. And it's like, you know, it knows what's wrong. It understands my needs and so, no one's working on that. We're still working on, keyboards. >> Right, that and chat bot is a great example because it's all AI, and where's the best AI? It's in the cloud because that's where the data is. That's where the best of modeling has been. (chuckles) >> I think your point, it's the scale of data. >> Absolutely. >> And machine learning and AI needs a lot of data points to get really good. I mean, I'm old, I'm 50. I graduated in 1993. I took an AI class from Niels Nielsen, like the godfather of AI, right? Okay, like that AI, even 10 years ago AI, it's just moving so quickly and it's now super affordable. >> Well, I really want to thank you guys for coming up and sharing that knowledge and insight, congratulations on the product and open APIs. Love open API's open source with some new revolution. Danielle and Robin. Thank you so much. >> Thanks so much. >> Thank you. >> Thank you. >> Congratulations. Thank you everyone for coming. (crowd applauding) (people whooping) Okay, back to you in the studio at Cloud City.

(gentle music) >> Okay, thank you Adam. Thank you everyone for joining us on the main stage here, folks watching, appreciate it. I'm John Furrier, Dave Vellante co-hosts of theCube. We're here in the main stage to talk to the two main players of Totogi, Danielle Royston, CEO as of today, the big news. Congratulations. >> Danielle: Yeah. Thank you. >> And Robin Langdon the CTO, Totogi. >> Robin: Thanks. So big news, CEO news today and $100 million investment. Every wants to know where's all the action? Why is this so popular right now? (Danielle chuckles) What's going on? Give us the quick update. >> Yeah, I met the Totogi guys and they have this great product I was really excited about. They're focused purely on telco software and bringing, coupling that with the Public Cloud, which is everything that I talk about, what I've been about for so long. And I really wanted to give them enough funding so they could focus on building great products. A lot of times, telcos, startups, you know they try to get a quick win. They kind of chase the big guys and I really wanted to make sure they were focused on building a great product. #2, I really wanted to show the industry, they had the funding they needed to be a real player. This wasn't like $5 million or a couple million dollars, so that was really important. And then #3, I want to make sure that we could hire great talent and you need money for compensation. And so $100 million it is. >> $100 million is a lot of fresh fat financing as they say. I got to ask you, what's different? Because I've been researching on the refactoring aspect of with the Cloud, obviously public cloud with AWS, a big deal. What's different about the charging aspect of this? >> Yeah I mean, charging hasn't been exciting, maybe ever. I mean, it's kind of like this really sort of sleepy area, but I think what the Totogi guys are doing is they're really coupling the idea of charging and network data to bring hyper-personalization to subscribers. And I think that's where it changes from being a charging engine to become an engagement engine. Telcos know more about us than Google, which is kind of crazy to think about it. They know when we wake up, they know what apps we use. If we call or text, if we game or stream and it's time to start using that data to drive a better experience to us. And I think to Totogi is enabling that. I'm super excited to do that. >> So Robin, I wonder if you could talk about that a little bit. I mean, maybe we get into the plumbing and I don't want to go too deep in it, but I think it's important because we've seen this movie before where people take their on-prem stacks, they wrap it in containers and they shove it into the Public Cloud and they say, "Hey, we're cloud too." If reading a press release, you guys are taking advantage of things like Amazon Nitro of course, but also Graviton and Graviton2 and eventually 3, which is the underlying capabilities, give you a cloud native advantage. Can you explain that a little bit? >> Yeah, absolutely. I mean, we wanted to build this in the Cloud using all of those great cloud innovations. So Graviton2, DynamoDB and using their infrastructure, just allowing us to be able to scale out. These all available to us to use and essentially free for us to use. And it's great, so as you say, we're not shoehorning something in that's decade's old technology, wrapping it in some kind of container and pushing it in. Which is just then, you just can't use any of those great innovations. >> And you've selected DynamoDB as the database. Okay, that's fine. We don't have to get so much into why, but maybe you could explain the advantage because I saw some benchmark numbers which were, like an order of magnitude greater than the competition, like share with us, why? How you were able to get there? And maybe share those numbers. >> Yeah, no, we do. So we just launched our benchmark. So, a million transactions per second. So we just blew away everyone else out there. And that's really because we could take advantage of all that great AWS technology in there and the database side we're using DynamoDB, where we had a huge debate about using what kind of database to go and use? There's a lot of people out there probably get very religious about the kind of database technology that you should be using. And whether it should it be SQL in-memory object database type technology, but really a single table design, gives you that true scalability. You can just horizontally scale that easily, across the whole planet. >> You know, Danielle. Again, I said that we've seen this movie before. There are a lot of parallels in telco with the enterprise. And if you look at enterprise SAS pricing, a lot of it is very static, kind of lock you in, per seat pricing, kind of an old model. And you're seeing a lot of the modern SAS companies who are emerging with a consumption pricing models. How are you guys thinking about pricing? >> Yeah, I don't know of any other company in telco that's starting to price by usage. And that is a very standard offering with the cloud providers, right? Google we know, Amazon, all those guys have a price by the API, price by the transaction. So we're really excited to offer that to telcos. They've been asking for it for awhile, right? Pay for what you need, when you need it, by the use. And so we're really excited to offer that, but I think what's really cool is the idea of a free tier, right? And so I think it's smaller telcos have a trade-off to make, whether, am I going to buy the best technology and pay through the nose and maybe at an unaffordable level, or do I compromise and buy something more affordable, but not as great. And what's so great about Totogi, it's the same product just priced for what you need. And so I think a CSP it'll, below 250,000 subscribers should be able to use the Totogi absolutely for free. And that is, and it's the same product that the big guy would get. So it's not a junior version or scaled back. And so I think that's really exciting. I think we're the only ones that do it. So here we go. >> Love the freemium model. So Robin, maybe you could explain why that's so much, so important in the charging space, because you've got a lot of different options that you want to configure for the consumer. >> Yeah. >> Maybe you could talk about sort of how the old world does that, the old guard and how long it takes and how you're approaching this. >> Yeah so it's, I mean traditionally, charging design, there's as you say, there's lots of different pricing leavers you want to be able to move and change to charge different people. And these systems, even if they say they're configurable, if they normally turn into an IT project where it takes weeks, months, even years to build out the system, you know, marketing can't just go in there and configure the dials and push out your new plans and tariffs. They have to go and create a requirement specification. They hand it down to IT. Those guys go and create a big change project. And by the time they're finished, the market's moved on. They're on to their next plan, their next tariff to go and build. So we wanted to create something that was truly configurable from a marketing standpoint. You know, user-friendly, they can go in there, configure it and be live in minutes, not even days or weeks. >> No, IT necessary. >> Robin: No IT necessary. >> So you know, I've been thinking about, John and I talk about this all the time, It's that there's a data play here. And what I think you're doing is actually building a data product. I think there's a new metric emerging in the industry, which is how long does it take me to go from idea to monetization with a data product. And that's what this is. This is a data product >> Yeah. >> for your customers. >> Absolutely, what Robin was talking about is totally the way the industry works. It's weeks before you have an idea and get it out to the market. And like Robin was mentioning, the market's changed by the time you get it out there, the data's stale. And so we researched every single plan in the world from every single CSP. There is about 30,000 plans in the world, right? The bigger you are, the more plans you have. On average, a tier one telco has 40 to 50 plans. And so how many offers, I mean think about, that's how many phones to buy, plans to buy. And so we're like, let's get some insight on the plans. Let's drive it into a standardization, right? Let's make them, which ones work, which ones don't. And that's, I think you're right. I think it's a data play and putting the power back into the marketer's hands and not with IT. >> So there's a lot of data on-prem. Explain why I can't do this with my on-prem data. >> Oh, well today that, I mean, sorry if you want to jump in. Feel free to jump in, right. But today, the products are designed in a way where they're, perpetually licensed, by the subscriber, rigid systems, not API based. I mean, there might be an API, but you got to pay through the nose to use it or you got to use the provider's people to code against it. They're inflexible. They were written when voice was the primary revenue driver, not data, right? And so they've been shoehorned, right? Like Robin was saying, shoehorned to be able to move into the world that we are now. I mean, when the iPhone came about that introduced apps and data went through the roof and the systems were written for voice, not written for data. >> And that's a good point, if you think about the telco industry, it seems like it could be a glacier that just needs to just break and just like, just get modern because we all have phones. We have apps. We can delete them. And the billing plans, like either nonexistent or it has to be all free. >> Well I mean, I'll ask you. Do you know what your billing plan is? Do you know how much data you use on a monthly basis? No one knows. >> I have no clue. >> A lot. >> No one. And so what you do is you buy unlimited. >> Dave: Right. >> You overpay. And so what we're seeing in the plans is that if you actually knew how much you used, you would be able to maybe pay less, which I remember the telcos are not excited to hear that message, but it's a win for the subscriber. And if you could >> I mean it's only >> accurately predict that. >> get lower and lower. I having a conversation last night at dinner with industry analysts, we're talking about a vehicle e-commerce, commerce in your car as you're driving. You can get that kind of with a 5G. The trend is transactions everywhere, ad-hoc, ephemeral... >> Yeah. >> The new apps are going to demand this kind of subscriber billing. >> Yeah >> Do people get this? Are you guys the only ones kind of like on this? >> No I think people have been talking about it for years. I think there's vendors out there that have been trying to offer this idea of like, build your own plan and all that other stuff but I think it's more than just minutes, text and data. It's starting to really understand what subscribers are using, right? Are you a football fan? Are you a golf fan? Are you a shopper? Are you a concert goer? And couple that with how you use your phone and putting out offers that are really exciting to subscribers so that we love our telco. Like we should be loving our telco. And I don't... I don't know that people talk >> They saved us >> about loving their telco. >> from the pandemic >> They saved us during the pandemic. The internet didn't crash, we got our zoom meetings. We got everything going on. What's the technical issue on solving these problems? Is it just legacy? Is it just mindset? Robin, what's your take on that? >> I'll keep talking as long as Robin will let me. (Daniel laughing) >> So the big technical issues, you're trying to build in this flexibility so that you can have, we don't know what people are going to configure in the future. It's minutes and text messages are given away for free. They're unlimited. Data is where it's at, about charging for apps and about using all that data in the network the telcos have, which is extremely valuable and there's a wealth of information in there that can be used to be monetized and push that out. And they need a charging system on top that can manage that and we have the flexibility that you don't have to go off and then start creating programs and IT projects that are going to do that. >> Well it's funny Danielle, you say that the telcos might not like that, right? 'Cause you might pay less. But in fact, that is the kind of on-prem mindset because when you have a fixed resource, you say, okay, don't use too much because we have to buy more. Or you overbuy to your point. The cloud mindset is, I'll try it. I'll try some more, I'll try some more. I'm aligning it with business value. Oh, I'm making money. Oh, great. I'm going to keep buying more. And it's very clear. It's transparent where the business value is. So my question is when you think about your charging engine and all this data conversation, is there more than just a charging engine in this platform? >> Well, I think just going back to what Robin was talking about. I think what Totogi is doing differently is by building it on the Public Cloud gives you virtually unlimited resources, right? In a couple of different directions, certainly hardware and capacity and scalability and all those other things, right? But also as Amazon is putting out more and more product, when you build it in this new way, you can take advantage of these new services very, very easily. And that is a different mindset. It's a different way to deploy applications. And I think that's what makes Totogi really different. You couldn't build Totogi on-premise because you need the infinite scalability. You need the machine learning, you need the AI of Amazon, which they have been investing in for decades, if they now charge you by the API call. And you get to use it like you were saying. Just give it a try, don't like it, stop. And it's just a completely different way of thinking, yeah. >> If I have to ask you a question about the Public Cloud, because the theme here in Cloud City is the Public Cloud is driving innovation, which is also includes disruption. And the new brands are coming in, old brands are either reinventing themselves or falling away. What is the Public Cloud truly enabling? Is it the scale? Is it the data? Is it the refactoring capability? What is the real driver of the Public Cloud innovation? >> I think the insight that CSPs are going to have is what Jamie Dimon had in banking. Like I think he was pretty famously saying, "I'm never going to use the Public Cloud. Our data is too precious, you know, regulations and all that stuff." But I think the insight they're going to have, and I hopefully, I do a keynote and I mentioned this, which is feature velocity. The ability to put out features in a day or two. Our feature velocity in telco is months. Months, months. >> Seriously? >> Yeah, sometimes years. It's just so slow between big iterations of new capability and to be able to put out new features in minutes or days and be able to outmaneuver your competition is unheard of. So the CSPs that starts to get this, it's going to be a real big get, and then they're going to start to.. (Danielle makes swishing sound) >> We just interviewed (Dave speaking indistinctly) a venture capitalist, Dave and I last month. And he's a big investor in Snowflake, on the big deals. He said that the new strategy that's working is you go to be agile with feature acceleration. We just talked about this at lunch and you get data. And you can dismantle the bad features quickly and double down >> Yup. >> on the winners. >> Ones that are working. So what used to be feature creep now is a benefit if you play it right? >> Danielle: It's feature experimentation. >> That's essentially what you- >> It's experimentation, right? And you're like, that one worked, this one didn't, kill that one, double down on this one, go faster and faster and so feature experimentation, which you can't do in telco, because every time we ask for a feature from your current vendor, it's hundreds of thousands, if not millions of dollars. So you don't experiment. And so yeah- >> You can make features disposable. >> Correct. And I think that we just discovered that on this stage just now. (group chuckling) >> Hey look at this. Digital revolution, DR. Telco DR. >> Yeah. >> Great to have you guys. >> This is super awesome. Thanks so much. >> You guys are amazing. Congratulations. And we're looking forward to the more innovation stories again, get out there, get the momentum. Great stuff. >> Danielle: It's going to be great. >> And awesome. >> Feature experimentation. >> Yeah. >> Hashtag. >> And Dave and I are going to head back over to our Cube set here, here on the main stage. We'll toss it back to the Adam in the studio. Adam, back to you and take it from here.

(bright upbeat music) >> Hello everyone, and welcome to the special exclusive CUBE Conversation, where we continue our coverage of the trends of the database market. With me is Nipun Agarwal, who's the vice president, MySQL HeatWave in advanced development at Oracle. Nipun, welcome. >> Thank you Dave. >> I love to have technical people on the Cube to educate, debate, inform, and we've extensively covered this market. We were all over the Snowflake IPO and at that time I remember, I challenged organizations bring your best people. Because I want to better understand what's happening at Database. After Oracle kind of won the Database wars 20 years ago, Database kind of got boring. And then it got really exciting with the big data movement, and all the, not only SQL stuff coming out, and Hadoop and blah, blah, blah. And now it's just exploding. You're seeing huge investments from many of your competitors, VCs are trying to get into the action. Meanwhile, as I've said many, many times, your chairman and head of technology, CTO, Larry Ellison, continues to invest to keep Oracle relevant. So it's really been fun to watch and I really appreciate you coming on. >> Sure thing. >> We have written extensively, we talked to a lot of Oracle customers. You get the leading mission critical database in the world. Everybody from Fortune 100, we evaluated what Gardner said about the operational databases. I think there's not a lot of question there. And we've written about that on WikiBound about you're converged databases, and the strategy there, and we're going to get into that. We've covered Autonomous Data Warehouse Exadata Cloud at Customer, and then we just want to really try to get into your area, which has been, kind of caught our attention recently. And I'm talking about the MySQL Database Service with HeatWave. I love the name, I laugh. It was an unveiled, I don't know, a few months ago. So Nipun, let's start the discussion today. Maybe you can update our viewers on what is HeatWave? What's the overall focus with Oracle? And how does it fit into the Cloud Database Service? >> Sure Dave. So HeatWave is a in-memory query accelerator for the MySQL Database Service for speeding up analytic queries as well as long running complex OLTP queries. And this is all done in the context of a single database which is the MySQL Database Service. Also, all existing MySQL applications or MySQL compatible tools and applications continue to work as is. So there is no change. And with this HeatWave, Oracle is delivering the only MySQL service which provides customers with a single unified platform for both analytic as well as transaction processing workloads. >> Okay, so, we've seen open source databases in the cloud growing very rapidly. I mentioned Snowflake, I think Google's BigQuery, get some mention, we'll talk, we'll maybe talk more about Redshift later on, but what I'm wondering, well let's talk about now, how does MySQL HeatWave service, how does that compare to MySQL-based services from other cloud vendors? I can get MySQL from others. In fact, I think we do. I think we run WikiBound on the LAMP stack. I think it's running on Amazon, but so how does your service compare? >> No other vendor, like, no other vendor offers this differentiated solution with an open source database namely, having a single database, which is optimized both for transactional processing and analytics, right? So the example is like MySQL. A lot of other cloud vendors provide MySQL service but MySQL has been optimized for transaction processing so when customs need to run analytics they need to move the data out of MySQL into some other database for any analytics, right? So we are the only vendor which is now offering this unified solution for both transactional processing analytics. That's the first point. Second thing is, most of the vendors out there have taken open source databases and they're basically hosting it in the cloud. Whereas HeatWave, has been designed from the ground up for the cloud, and it is a 100% compatible with MySQL applications. And the fact that we have designed it from the ground up for the cloud, maybe I'll spend 100s of person years of research and engineering means that we have a solution, which is very, very scalable, it's very optimized in terms of performance, and it is very inexpensive in terms of the cost. >> Are you saying, well, wait, are you saying that you essentially rewrote MySQL to create HeatWave but at the same time maintained compatibility with existing applications? >> Right. So we enhanced MySQL significantly and we wrote a whole bunch of new code which is brand new code optimized for the cloud in such a manner that yes, it is 100% compatible with all existing MySQL applications. >> What does it mean? And if I'm to optimize for the cloud, I mean, I hear that and I say, okay, it's taking advantage of cloud-native. I hear kind of the buzzwords, cloud-first, cloud-native. What does it specifically mean from a technical standpoint? >> Right. So first, let's talk about performance. What we have done is that we have looked at two aspects. We have worked with shapes like for instance, like, the compute shapes which provide the best performance for dollar, per dollar. So I'll give you a couple of examples. We have optimized for certain shifts. So, HeatWave is in-memory query accelerator. So the cost of the system is dominated by the cost. So we are working with chips which provide the cheapest cost per terabyte of memory. Secondly, we are using commodity cloud services in such a manner that it's in-optimized for both performance as well as performance per dollar. So, example is, we are not using any locally-attached SSDs. We use ObjectStore because it's very inexpensive. And then I guess at some point I will get into the details of the architecture. The system has been really, really designed for massive scalability. So as you add more compute, as you add more service, the system continues to scale almost perfectly linearly. So this is what I mean in terms of being optimized for the cloud. >> All right, great. >> And furthermore, (indistinct). >> Thank you. No, carry on. >> Over the next few months, you will see a bunch of other announcements where we're adding a whole bunch of machine learning and data driven-based automation which we believe is critical for the cloud. So optimized for performance, optimized for the cloud, and machine learning-based automation which we believe is critical for any good cloud-based service. >> All right, I want to come back and ask you more about the architecture, but you mentioned some of the others taking open source databases and shoving them into the cloud. Let's take the example of AWS. They have a series of specialized data stores and, for different workloads, Aurora is for OLTP I actually think it's based on MySQL Redshift which is based on ParAccel. And so, and I've asked Amazon about this, and their response is, actually kind of made sense to me. Look, we want the right tool for the right job, we want access to the primitives because when the market changes we can change faster as opposed to, if we put, if we start building bigger and bigger databases with more functionality, it's, we're not as agile. So that kind of made sense to me. I know we, again, we use a lot, we use, I think I said MySQL in Amazon we're using DynamoDB, works, that's cool. We're not huge. And I, we fully admit and we've researched this, when you start to get big that starts to get maybe expensive. But what do you think about that approach and why is your approach better? >> Right, we believe that there are multiple drawbacks of having different databases or different services, one, optimized for transactional processing and one for analytics and having to ETL between these different services. First of all, it's expensive because you have to manage different databases. Secondly, it's complex. From an application standpoint, applications need, now need to understand the semantics of two different databases. It's inefficient because you have to transfer data at some PRPC from one database to the other one. It's not secure because there is security aspects involved when your transferring data and also the identity of users in the two different databases is different. So it's, the approach which has been taken by Amazons and such, we believe, is more costly, complex, inefficient and not secure. Whereas with HeatWave, all the data resides in one database which is MySQL and it can run both transaction processing and analytics. So in addition to all the benefits I talked about, customers can also make their decisions in real time because there is no need to move the data. All the data resides in a single database. So as soon as you make any changes, those changes are visible to customers for queries right away, which is not the case when you have different siloed specialized databases. >> Okay, that, a lot of ways to skin a cat and that what you just said makes sense. By the way, we were saying before, companies have taken off the shelf or open source database has shoved them in the cloud. I have to give Amazon some props. They actually have done engineering to Aurora and Redshift. And they've got the engineering capabilities to do that. But you can see, for example, in Redshift the way they handle separating compute from storage it's maybe not as elegant as some of the other players like a Snowflake, for example, but they get there and they, maybe it's a little bit more brute force but so I don't want to just make it sound like they're just hosting off the shelf in the cloud. But is it fair to say that there's like a crossover point? So in other words, if I'm smaller and I'm not, like doing a bunch of big, like us, I mean, it's fine. It's easy, I spin it up. It's cheaper than having to host my own servers. So there's, presumably there's a sweet spot for that approach and a sweet spot for your approach. Is that fair or do you feel like you can cover a wider spectrum? >> We feel we can cover the entire spectrum, not wider, the entire spectrum. And we have benchmarks published which are actually available on GitHub for anyone to try. You will see that this approach you have taken with the MySQL Database Service in HeatWave, we are faster, we are cheaper without having to move the data. And the mileage or the amount of improvement you will get, surely vary. So if you have less data the amount of improvement you will get, maybe like say 100 times, right, or 500 times, but smaller data sizes. If you get to lots of data sizes this improvement amplifies to 1000 times or 10,000 times. And similarly for the cost, if the data size is smaller, the cost advantage you will have is less, maybe MySQL HeatWave is one third the cost. If the data size is larger, the cost advantage amplifies. So to your point, MySQL Database Service in HeatWave is going to be better for all sizes but the amount of mileage or the amount of benefit you will get increases as the size of the data increases. >> Okay, so you're saying you got better performance, better cost, better price performance. Let me just push back a little bit on this because I, having been around for awhile, I often see these performance and price comparisons. And what often happens is a vendor will take the latest and greatest, the one they just announced and they'll compare it to an N-1 or an N-2, running on old hardware. So, is, you're normalizing for that, is that the game you're playing here? I mean, how can you, give us confidence that this is easier kind of legitimate benchmarks in your GitHub repo. >> Absolutely. I'll give you a bunch of like, information. But let me preface this by saying that all of our scripts are available in the open source in the GitHub repo for anyone to try and we would welcome feedback otherwise. So we have taken, yes, the latest version of MySQL Database Service in HeatWave, we have optimized it, and we have run multiple benchmarks. For instance, TBC-H, TPC-DS, right? Because the amount of improvement a query will get depends upon the specific query, depends upon the predicates, it depends on the selectivity so we just wanted to use standard benchmarks. So it's not the case that if you're using certain classes of query, excuse me, benefit, get them more. So, standard benchmarks. Similarly, for the other vendors or other services like Redshift, we have run benchmarks on the latest shapes of Redshift the most optimized configuration which they recommend, running their scripts. So this is not something that, hey, we're just running out of the box. We have optimized Aurora, we have optimized (indistinct) to the best and possible extent we can based on their guidelines, based on their latest release, and that's what you're talking about in terms of the numbers. >> All right. Please continue. >> Now, for some other vendors, if we get to the benchmark section, we'll talk about, we are comparing with other services, let's say Snowflake. Well there, there are issues in terms of you can't legally run Snowflake numbers, right? So there, we have looked at some reports published by Gigaom report. and we are taking the numbers published by the Gigaom report for Snowflake, Google BigQuery and as you'll see maps numbers, right? So those, we have not won ourselves. But for AWS Redshift, as well as AWS Aurora, we have run the numbers and I believe these are the best numbers anyone can get. >> I saw that Gigaom report and I got to say, Gigaom, sometimes I'm like, eh, but I got to say that, I forget the guy's name, he knew what he was talking about. He did a good job, I thought. I was curious as to the workload. I always say, well, what's the workload. And, but I thought that report was pretty detailed. And Snowflake did not look great in that report. Oftentimes, and they've been marketing the heck out of it. I forget who sponsored it. It is, it was sponsored content. But, I did, I remember seeing that and thinking, hmm. So, I think maybe for Snowflake that sweet spot is not, maybe not that performance, maybe it's the simplicity and I think that's where they're making their mark. And most of their databases are small and a lot of read-only stuff. And so they've found a market there. But I want to come back to the architecture and really sort of understand how you've able, you've been able to get this range of both performance and cost you talked about. I thought I heard that you're optimizing the chips, you're using ObjectStore. You're, you've got an architecture that's not using SSD, it's using ObjectStore. So this, is their cashing there? I wonder if you could just give us some details of the architecture and tell us how you got to where you are. >> Right, so let me start off saying like, what are the kind of numbers we are talking about just to kind of be clear, like what the improvements are. So if you take the MySQL Database Service in HeatWave in Oracle Cloud and compare it with MySQL service in any other cloud, and if you look at smaller data sizes, say data sizes which are about half a terabyte or so, HeatWave is 400 times faster, 400 times faster. And as you get to... >> Sorry. Sorry to interrupt. What are you measuring there? Faster in terms of what? >> Latency. So we take TCP-H 22 queries, we run them on HeatWave, and we run the same queries on MySQL service on any other cloud, half a terabyte and the performance in terms of latency is 400 times faster in HeatWave. >> Thank you. Okay. >> If you go to a lot of other data sites, then the other data point of view, we're looking at say something like, 4 TB, there, we did two comparisons. One is with AWS Aurora, which is, as you said, they have taken MySQL. They have done a bunch of innovations over there and we are offering it as a premier service. So on 4 TB TPC-H, MySQL Database Service with HeatWave is 1100 times faster than Aurora. It is three times faster than the fastest shape of Redshift. So Redshift comes in different flavors some talking about dense compute too, right? And again, looking at the most recommended configuration from Redshift. So 1100 times faster that Aurora, three times faster than Redshift and at one third, the cost. So this where I just really want to point out that it is much faster and much cheaper. One third the cost. And then going back to the Gigaom report, there was a comparison done with Snowflake, Google BigQuery, Redshift, Azure Synapse. I wouldn't go into the numbers here but HeatWave was faster on both TPC-H as well as TPC-DS across all these products and cheaper compared to any of these products. So faster, cheaper on both the benchmarks across all these products. Now let's come to, like, what is the technology underneath? >> Great. >> So, basically there are three parts which you're going to see. One is, improve performance, very good scale, and improve a lower cost. So the first thing is that HeatWave has been optimized and, for the cloud. And when I say that, we talked about this a bit earlier. One is we are using the cheapest shapes which are available. We're using the cheapest services which are available without having to compromise the performance and then there is this machine learning-based automation. Now, underneath, in terms of the architecture of HeatWave there are basically, I would say, four key things. First is, HeatWave is an in-memory engine that a presentation which we have in memory is a hybrid columnar representation which is optimized for vector process. That's the first thing. And that's pretty table stakes these days for anyone who wants to do in-memory analytics except that it's hybrid columnar which is optimized for vector processing. So that's the first thing. The second thing which starts getting to be novel is that HeatWave has a massively parallel architecture which is enabled by a massively partitioned architecture. So we take the data, we read the data from MySQL into the memory of the HeatWave and we massively partition this data. So as we're reading the data, we're partitioning the data based on the workload, the sizes of these partitions is such that it fits in the cache of the underlying processor and then we're able to consume these partitions really, really fast. So that's the second bit which is like, massively parallel architecture enabled by massively partitioned architecture. Then the third thing is, that we have developed new state-of-art algorithms for distributed query processing. So for many of the workloads, we find that joints are the long pole in terms of the amount of time it takes. So we at Oracle have developed new algorithms for distributed joint processing and similarly for many other operators. And this is how we're being able to consume this data or process this data, which is in-memory really, really fast. And finally, and what we have, is that we have an eye for scalability and we have designed algorithms such that there's a lot of overlap between compute and communication, which means that as you're sending data across various nodes and there could be like, dozens of of nodes or 100s of nodes that they're able to overlap the computation time with the communication time and this is what gives us massive scalability in the cloud. >> Yeah, so, some hard core database techniques that you've brought to HeatWave, that's impressive. Thank you for that description. Let me ask you, just to go to quicker side. So, MySQL is open source, HeatWave is what? Is it like, open core? Is it open source? >> No, so, HeatWave is something which has been designed and optimized for the cloud. So it can't be open source. So any, it's not open service. >> It is a service. >> It is a service. That's correct. >> So it's a managed service that I pay Oracle to host for me. Okay. Got it. >> That's right. >> Okay, I wonder if you could talk about some of the use cases that you're seeing for HeatWave, any patterns that you're seeing with customers? >> Sure, so we've had the service, we had the HeatWave service in limited availability for almost 15 months and it's been about five months since we have gone G. And there's a very interesting trend of our customers we're seeing. The first one is, we are seeing many migrations from AWS specifically from Aurora. Similarly, we are seeing many migrations from Azure MySQL we're migrations from Google. And the number one reason customers are coming is because of ease of use. Because they have their databases currently siloed. As you were talking about some for optimized for transactional processing, some for analytics. Here, what customers find is that in a single database, they're able to get very good performance, they don't need to move the data around, they don't need to manage multiple databaes. So we are seeing many migrations from these services. And the number one reason is reduce complexity of ease of use. And the second one is, much better performance and reduced costs, right? So that's the first thing. We are very excited and delighted to see the number of migrations we're getting. The second thing which we're seeing is, initially, when we had the service announced, we were like, targeting really towards analytics. But now what are finding is, many of these customers, for instance, who have be running on Aurora, when they are moving from MySQL in HeatWave, they are finding that many of the OLTP queries as well, are seeing significant acceleration with the HeatWave. So now customers are moving their entire applications or, to HeatWave. So that's the second trend we're seeing. The third thing, and I think I kind of missed mentioning this earlier, one of the very key and unique value propositions we provide with the MySQL Database Service in HeatWave, is that we provide a mechanism where if customers have their data stored on premise they can still leverage the HeatWave service by enabling MySQL replication. So they can have their data on premise, they can replicate this data in the Oracle Cloud and then they can run analytics. So this deployment which we are calling the hybrid deployment is turning out to be very, very popular because there are customers, there are some customers who for various reasons, compliance or regulatory reasons cannot move the entire data to the cloud or migrate the data to the cloud completely. So this provides them a very good setup where they can continue to run their existing database and when it comes to getting benefits of HeatWave for query acceleration, they can set up this replication. >> And I can run that on anyone, any available server capacity or is there an appliance to facilitate that? >> No, this is just standard MySQL replication. So if a customer is running MySQL on premise they can just turn off this application. We have obviously enhanced it to support this inbound replication between on-premise and Oracle Cloud with something which can be enabled as long as the source and destination are both MySQL. >> Okay, so I want to come back to this sort of idea of the architecture a little bit. I mean, it's hard for me to go toe to toe with the, I'm not an engineer, but I'm going to try anyway. So you've talked about OLTP queries. I thought, I always thought HeatWave was optimized for analytics. But so, I want to push on this notion because people think of this the converged database, and what you're talking about here with HeatWave is sort of the Swiss army knife which is great 'cause you got a screwdriver and you got Phillips and a flathead and some scissors, maybe they're not as good. They're not as good necessarily as the purpose-built tool. But you're arguing that this is best of breed for OLTP and best of breed for analytics, both in terms of performance and cost. Am I getting that right or is this really a Swiss army knife where that flathead is really not as good as the big, long screwdriver that I have in my bag? >> Yes, so, you're getting it right but I did want to make a clarification. That HeatWave is definitely the accelerator for all your queries, all analytic queries and also for the long running complex transaction processing inquiries. So yes, HeatWave the uber query accelerator engine. However, when it comes to transaction processing in terms of your insert statements, delete statements, those are still all done and served by the MySQL database. So all, the transactions are still sent to the MySQL database and they're persistent there, it's the queries for which HeatWave is the accelerator. So what you said is correct. For all query acceleration, HeatWave is the engine. >> Makes sense. Okay, so if I'm a MySQL customer and I want to use HeatWave, what do I have to do? Do I have to make changes to my existing applications? You applied earlier that, no, it's just sort of plugs right in. But can you clarify that. >> Yes, there are absolutely no changes, which any MySQL or MySQL compatible application needs to make to take advantage of HeatWave. HeatWave is an in-memory accelerator and it's completely transparent to the application. So we have like, dozens and dozens of like, applications which have migrated to HeatWave, and they are seeing the same thing, similarly tools. So if you look at various tools which work for analytics like, Tableau, Looker, Oracle Analytics Cloud, all of them will work just seamlessly. And this is one of the reasons we had to do a lot of heavy lifting in the MySQL database itself. So the MySQL database engineering team was, has been very actively working on this. And one of the reasons is because we did the heavy lifting and we meet enhancements to the MySQL optimizer in the MySQL storage layer to do the integration of HeatWave in such a seamless manner. So there is absolutely no change which an application needs to make in order to leverage or benefit from HeatWave. >> You said earlier, Nipun, that you're seeing migrations from, I think you said Aurora and Google BigQuery, you might've said Redshift as well. Do you, what kind of tooling do you have to facilitate migrations? >> Right, now, there are multiple ways in which customers may want to do this, right? So the first tooling which we have is that customers, as I was talking about the replication or the inbound replication mechanism, customers can set up heat HeatWave in the Oracle Cloud and they can send the data, they can set up replication within their instances in their cloud and HeatWave. Second thing is we have various kinds of tools to like, facilitate the data migration in terms of like, fast ingestion sites. So there are a lot of such customers we are seeing who are kind of migrating and we have a plethora of like, tools and applications, in addition to like, setting up this inbound application, which is the most seamless way of getting customers started with HeatWave. >> So, I think you mentioned before, I have my notes, machine intelligence and machine learning. We've seen that with autonomous database it's a big, big deal obviously. How does HeatWave take advantage of machine intelligence and machine learning? >> Yeah, and I'm probably going to be talking more about this in the future, but what we have already is that HeatWave uses machine learning to intelligently automate many operations. So we know that when there's a service being offered in the cloud, our customers expect automation. And there're a lot of vendors and a lot of services which do a good job in automation. One of the places where we're going to be very unique is that HeatWave uses machine learning to automate many of these operations. And I'll give you one such example which is provisioning. Right now with HeatWave, when a customer wants to determine how many nodes are needed for running their workload, they don't need to make a guess. They invoke a provisioning advisor and this advisor uses machine learning to sample a very small percentage of the data. We're talking about, like, 0.1% sampling and it's able to predict the amount of memory with 95% accuracy, which this data is going to take. And based on that, it's able to make a prediction of how many servers are needed. So just a simple operation, the first step of provisioning, this is something which is done manually across, on any of the service, whereas at HeatWave, we have machine learning-based advisor. So this is an example of what we're doing. And in the future, we'll be offering many such innovations as a part of the MySQL Database and the HeatWave service. >> Well, I've got to say I was skeptic but I really appreciate it, you're, answering my questions. And, a lot of people when you made the acquisition and inherited MySQL, thought you were going to kill it because they thought it would be competitive to Oracle Database. I'm happy to see that you've invested and figured out a way to, hey, we can serve our community and continue to be the steward of MySQL. So Nipun, thanks very much for coming to the CUBE. Appreciate your time. >> Sure. Thank you so much for the time, Dave. I appreciate it. >> And thank you for watching everybody. This is Dave Vellante with another CUBE Conversation. We'll see you next time. (bright upbeat music)