>> 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.

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great questions, Dave. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

>> Welcome back to SuperCloud2. This is Dave Vellante, and we're here with Jack Greenfield. He's the Vice President of Enterprise Architecture and the Chief Architect for the global technology platform at Walmart. Jack, I want to thank you for coming on the program. Really appreciate your time. >> Glad to be here, Dave. Thanks for inviting me and appreciate the opportunity to chat with you. >> Yeah, it's our pleasure. Now we call what you've built a SuperCloud. That's our term, not yours, but how would you describe the Walmart Cloud Native Platform? >> So WCNP, as the acronym goes, is essentially an implementation of Kubernetes for the Walmart ecosystem. And what that means is that we've taken Kubernetes off the shelf as open source, and we have integrated it with a number of foundational services that provide other aspects of our computational environment. So Kubernetes off the shelf doesn't do everything. It does a lot. In particular the orchestration of containers, but it delegates through API a lot of key functions. So for example, secret management, traffic management, there's a need for telemetry and observability at a scale beyond what you get from raw Kubernetes. That is to say, harvesting the metrics that are coming out of Kubernetes and processing them, storing them in time series databases, dashboarding them, and so on. There's also an angle to Kubernetes that gets a lot of attention in the daily DevOps routine, that's not really part of the open source deliverable itself, and that is the DevOps sort of CICD pipeline-oriented lifecycle. And that is something else that we've added and integrated nicely. And then one more piece of this picture is that within a Kubernetes cluster, there's a function that is critical to allowing services to discover each other and integrate with each other securely and with proper configuration provided by the concept of a service mesh. So Istio, Linkerd, these are examples of service mesh technologies. And we have gone ahead and integrated actually those two. There's more than those two, but we've integrated those two with Kubernetes. So the net effect is that when a developer within Walmart is going to build an application, they don't have to think about all those other capabilities where they come from or how they're provided. Those are already present, and the way the CICD pipelines are set up, it's already sort of in the picture, and there are configuration points that they can take advantage of in the primary YAML and a couple of other pieces of config that we supply where they can tune it. But at the end of the day, it offloads an awful lot of work for them, having to stand up and operate those services, fail them over properly, and make them robust. All of that's provided for. >> Yeah, you know, developers often complain they spend too much time wrangling and doing things that aren't productive. So I wonder if you could talk about the high level business goals of the initiative in terms of the hardcore benefits. Was the real impetus to tap into best of breed cloud services? Were you trying to cut costs? Maybe gain negotiating leverage with the cloud guys? Resiliency, you know, I know was a major theme. Maybe you could give us a sense of kind of the anatomy of the decision making process that went in. >> Sure, and in the course of answering your question, I think I'm going to introduce the concept of our triplet architecture which we haven't yet touched on in the interview here. First off, just to sort of wrap up the motivation for WCNP itself which is kind of orthogonal to the triplet architecture. It can exist with or without it. Currently does exist with it, which is key, and I'll get to that in a moment. The key drivers, business drivers for WCNP were developer productivity by offloading the kinds of concerns that we've just discussed. Number two, improving resiliency, that is to say reducing opportunity for human error. One of the challenges you tend to run into in a large enterprise is what we call snowflakes, lots of gratuitously different workloads, projects, configurations to the extent that by developing and using WCNP and continuing to evolve it as we have, we end up with cookie cutter like consistency across our workloads which is super valuable when it comes to building tools or building services to automate operations that would otherwise be manual. When everything is pretty much done the same way, that becomes much simpler. Another key motivation for WCNP was the ability to abstract from the underlying cloud provider. And this is going to lead to a discussion of our triplet architecture. At the end of the day, when one works directly with an underlying cloud provider, one ends up taking a lot of dependencies on that particular cloud provider. Those dependencies can be valuable. For example, there are best of breed services like say Cloud Spanner offered by Google or say Cosmos DB offered by Microsoft that one wants to use and one is willing to take the dependency on the cloud provider to get that functionality because it's unique and valuable. On the other hand, one doesn't want to take dependencies on a cloud provider that don't add a lot of value. And with Kubernetes, we have the opportunity, and this is a large part of how Kubernetes was designed and why it is the way it is, we have the opportunity to sort of abstract from the underlying cloud provider for stateless workloads on compute. And so what this lets us do is build container-based applications that can run without change on different cloud provider infrastructure. So the same applications can run on WCNP over Azure, WCNP over GCP, or WCNP over the Walmart private cloud. And we have a private cloud. Our private cloud is OpenStack based and it gives us some significant cost advantages as well as control advantages. So to your point, in terms of business motivation, there's a key cost driver here, which is that we can use our own private cloud when it's advantageous and then use the public cloud provider capabilities when we need to. A key place with this comes into play is with elasticity. So while the private cloud is much more cost effective for us to run and use, it isn't as elastic as what the cloud providers offer, right? We don't have essentially unlimited scale. We have large scale, but the public cloud providers are elastic in the extreme which is a very powerful capability. So what we're able to do is burst, and we use this term bursting workloads into the public cloud from the private cloud to take advantage of the elasticity they offer and then fall back into the private cloud when the traffic load diminishes to the point where we don't need that elastic capability, elastic capacity at low cost. And this is a very important paradigm that I think is going to be very commonplace ultimately as the industry evolves. Private cloud is easier to operate and less expensive, and yet the public cloud provider capabilities are difficult to match. >> And the triplet, the tri is your on-prem private cloud and the two public clouds that you mentioned, is that right? >> That is correct. And we actually have an architecture in which we operate all three of those cloud platforms in close proximity with one another in three different major regions in the US. So we have east, west, and central. And in each of those regions, we have all three cloud providers. And the way it's configured, those data centers are within 10 milliseconds of each other, meaning that it's of negligible cost to interact between them. And this allows us to be fairly agnostic to where a particular workload is running. >> Does a human make that decision, Jack or is there some intelligence in the system that determines that? >> That's a really great question, Dave. And it's a great question because we're at the cusp of that transition. So currently humans make that decision. Humans choose to deploy workloads into a particular region and a particular provider within that region. That said, we're actively developing patterns and practices that will allow us to automate the placement of the workloads for a variety of criteria. For example, if in a particular region, a particular provider is heavily overloaded and is unable to provide the level of service that's expected through our SLAs, we could choose to fail workloads over from that cloud provider to a different one within the same region. But that's manual today. We do that, but people do it. Okay, we'd like to get to where that happens automatically. In the same way, we'd like to be able to automate the failovers, both for high availability and sort of the heavier disaster recovery model between, within a region between providers and even within a provider between the availability zones that are there, but also between regions for the sort of heavier disaster recovery or maintenance driven realignment of workload placement. Today, that's all manual. So we have people moving workloads from region A to region B or data center A to data center B. It's clean because of the abstraction. The workloads don't have to know or care, but there are latency considerations that come into play, and the humans have to be cognizant of those. And automating that can help ensure that we get the best performance and the best reliability. >> But you're developing the dataset to actually, I would imagine, be able to make those decisions in an automated fashion over time anyway. Is that a fair assumption? >> It is, and that's what we're actively developing right now. So if you were to look at us today, we have these nice abstractions and APIs in place, but people run that machine, if you will, moving toward a world where that machine is fully automated. >> What exactly are you abstracting? Is it sort of the deployment model or, you know, are you able to abstract, I'm just making this up like Azure functions and GCP functions so that you can sort of run them, you know, with a consistent experience. What exactly are you abstracting and how difficult was it to achieve that objective technically? >> that's a good question. What we're abstracting is the Kubernetes node construct. That is to say a cluster of Kubernetes nodes which are typically VMs, although they can run bare metal in certain contexts, is something that typically to stand up requires knowledge of the underlying cloud provider. So for example, with GCP, you would use GKE to set up a Kubernetes cluster, and in Azure, you'd use AKS. We are actually abstracting that aspect of things so that the developers standing up applications don't have to know what the underlying cluster management provider is. They don't have to know if it's GCP, AKS or our own Walmart private cloud. Now, in terms of functions like Azure functions that you've mentioned there, we haven't done that yet. That's another piece that we have sort of on our radar screen that, we'd like to get to is serverless approach, and the Knative work from Google and the Azure functions, those are things that we see good opportunity to use for a whole variety of use cases. But right now we're not doing much with that. We're strictly container based right now, and we do have some VMs that are running in sort of more of a traditional model. So our stateful workloads are primarily VM based, but for serverless, that's an opportunity for us to take some of these stateless workloads and turn them into cloud functions. >> Well, and that's another cost lever that you can pull down the road that's going to drop right to the bottom line. Do you see a day or maybe you're doing it today, but I'd be surprised, but where you build applications that actually span multiple clouds or is there, in your view, always going to be a direct one-to-one mapping between where an application runs and the specific cloud platform? >> That's a really great question. Well, yes and no. So today, application development teams choose a cloud provider to deploy to and a location to deploy to, and they have to get involved in moving an application like we talked about today. That said, the bursting capability that I mentioned previously is something that is a step in the direction of automatic migration. That is to say we're migrating workload to different locations automatically. Currently, the prototypes we've been developing and that we think are going to eventually make their way into production are leveraging Istio to assess the load incoming on a particular cluster and start shedding that load into a different location. Right now, the configuration of that is still manual, but there's another opportunity for automation there. And I think a key piece of this is that down the road, well, that's a, sort of a small step in the direction of an application being multi provider. We expect to see really an abstraction of the fact that there is a triplet even. So the workloads are moving around according to whatever the control plane decides is necessary based on a whole variety of inputs. And at that point, you will have true multi-cloud applications, applications that are distributed across the different providers and in a way that application developers don't have to think about. >> So Walmart's been a leader, Jack, in using data for competitive advantages for decades. It's kind of been a poster child for that. You've got a mountain of IP in the form of data, tools, applications best practices that until the cloud came out was all On Prem. But I'm really interested in this idea of building a Walmart ecosystem, which obviously you have. Do you see a day or maybe you're even doing it today where you take what we call the Walmart SuperCloud, WCNP in your words, and point or turn that toward an external world or your ecosystem, you know, supporting those partners or customers that could drive new revenue streams, you know directly from the platform? >> Great question, Steve. So there's really two things to say here. The first is that with respect to data, our data workloads are primarily VM basis. I've mentioned before some VMware, some straight open stack. But the key here is that WCNP and Kubernetes are very powerful for stateless workloads, but for stateful workloads tend to be still climbing a bit of a growth curve in the industry. So our data workloads are not primarily based on WCNP. They're VM based. Now that said, there is opportunity to make some progress there, and we are looking at ways to move things into containers that are currently running in VMs which are stateful. The other question you asked is related to how we expose data to third parties and also functionality. Right now we do have in-house, for our own use, a very robust data architecture, and we have followed the sort of domain-oriented data architecture guidance from Martin Fowler. And we have data lakes in which we collect data from all the transactional systems and which we can then use and do use to build models which are then used in our applications. But right now we're not exposing the data directly to customers as a product. That's an interesting direction that's been talked about and may happen at some point, but right now that's internal. What we are exposing to customers is applications. So we're offering our global integrated fulfillment capabilities, our order picking and curbside pickup capabilities, and our cloud powered checkout capabilities to third parties. And this means we're standing up our own internal applications as externally facing SaaS applications which can serve our partners' customers. >> Yeah, of course, Martin Fowler really first introduced to the world Zhamak Dehghani's data mesh concept and this whole idea of data products and domain oriented thinking. Zhamak Dehghani, by the way, is a speaker at our event as well. Last question I had is edge, and how you think about the edge? You know, the stores are an edge. Are you putting resources there that sort of mirror this this triplet model? Or is it better to consolidate things in the cloud? I know there are trade-offs in terms of latency. How are you thinking about that? >> All really good questions. It's a challenging area as you can imagine because edges are subject to disconnection, right? Or reduced connection. So we do place the same architecture at the edge. So WCNP runs at the edge, and an application that's designed to run at WCNP can run at the edge. That said, there are a number of very specific considerations that come up when running at the edge, such as the possibility of disconnection or degraded connectivity. And so one of the challenges we have faced and have grappled with and done a good job of I think is dealing with the fact that applications go offline and come back online and have to reconnect and resynchronize, the sort of online offline capability is something that can be quite challenging. And we have a couple of application architectures that sort of form the two core sets of patterns that we use. One is an offline/online synchronization architecture where we discover that we've come back online, and we understand the differences between the online dataset and the offline dataset and how they have to be reconciled. The other is a message-based architecture. And here in our health and wellness domain, we've developed applications that are queue based. So they're essentially business processes that consist of multiple steps where each step has its own queue. And what that allows us to do is devote whatever bandwidth we do have to those pieces of the process that are most latency sensitive and allow the queue lengths to increase in parts of the process that are not latency sensitive, knowing that they will eventually catch up when the bandwidth is restored. And to put that in a little bit of context, we have fiber lengths to all of our locations, and we have I'll just use a round number, 10-ish thousand locations. It's larger than that, but that's the ballpark, and we have fiber to all of them, but when the fiber is disconnected, and it does get disconnected on a regular basis. In fact, I forget the exact number, but some several dozen locations get disconnected daily just by virtue of the fact that there's construction going on and things are happening in the real world. When the disconnection happens, we're able to fall back to 5G and to Starlink. Starlink is preferred. It's a higher bandwidth. 5G if that fails. But in each of those cases, the bandwidth drops significantly. And so the applications have to be intelligent about throttling back the traffic that isn't essential, so that it can push the essential traffic in those lower bandwidth scenarios. >> So much technology to support this amazing business which started in the early 1960s. Jack, unfortunately, we're out of time. I would love to have you back or some members of your team and drill into how you're using open source, but really thank you so much for explaining the approach that you've taken and participating in SuperCloud2. >> You're very welcome, Dave, and we're happy to come back and talk about other aspects of what we do. For example, we could talk more about the data lakes and the data mesh that we have in place. We could talk more about the directions we might go with serverless. So please look us up again. Happy to chat. >> I'm going to take you up on that, Jack. All right. This is Dave Vellante for John Furrier and the Cube community. Keep it right there for more action from SuperCloud2. (upbeat music)

>>Well, hello everybody at John Wallace here on the Cube, and glad to have you along here for day two of our coverage here at AWS Reinvent 22. We're up in the global startup program, which is part of AWS's Startup Showcase, and I've got Kevin Farley with me. He is the director of Strategic Alliances with Maria Day db. And Kevin, good to see you this morning. Good to see you, John. Thanks for joining us. Thank >>You. >>Appreciate it. Yeah. First off, tell us about Maria db. Sure. Obviously data's your thing. Yep. But to share that with some folks at home who might not be familiar with your offering. >>Yeah. So Maria DB's been around as a corporate entity for 10 plus years, and we have a massive customer base. You know, there's a billion downloads from Docker Hub, 75% of the Fortune 500. We have an enormous sea of really happy users. But what we realize is that all of these users are really thinking about what do we, what does it mean to transform it? What does cloud modernization mean? And how do we build a strategy on something we really love to drive it into the cloud and take it to the future. So what we launched about two years ago, two and a half years ago, is Skye. It's our database as a service. It leverages all the best elements, what we provide on the enterprise platform. It marries to the AWS cloud, and it really provides the best of both worlds for our >>Customers. So in your thought then, what, what problem is that solving? >>I think what you see in the overall database market is that many people have been using what we would call legacy technology. There's been lots of sort of stratification and mixes of different database solutions. All of them come with some promise, and all of 'em come with a lot of compromise. So I think what the market is really looking for is something that can take what they know and love, can bring it to the cloud and can survive the port drive the performance and scale. That completely changes the landscape, especially as you think about what modern data needs look like, right? What people did 10 years ago with the exponential scale of data no longer works. And what they need is something that not only can really deliver against their core business values and their core business deliverables, but gets 'em to the future. How do we drive something new? How do we innovate? How do we change the game? And I think what we built with AWS really delivers what we call cloud scale. It's taking something that is the best technology, and I as a V can build, marrying it to, you know, Kubernetes layer, marrying it to global availability, thinking about having true global high availability across all of your environments and really delivering that to customers through an integrated partnership. >>Could we see this coming? I mean, because you know data, right? I mean, yeah, we, we, everybody talked about the tsunami of growth, you know, >>Back 10 >>Sure. 11 years ago. But, but maybe the headlights didn't go far enough or, or, but, but you could see that there was going to be crunch time. >>There's no doubt. And I think that this has been a, there's, there's been these sort of pocket solutions, right? So if you think at the entire no sequel world, right? People said, oh, I need scale, I can get it, but what do I have to give up asset compliance? So I have to change the way I think about what data is and how I, I can govern it. So there's been these things that deliver on half the promise, but there's never been something that comes together and really drives what we deliver through CIQ is something called expand. So distributed SQL really tied to the SQL Query language, having that asset data. So having everything you need without the compromise built on the cloud allows you to scale out and allows you to think about, I can actually do exponential layers of, of data, data modeling, data querying, complete read, write, driving that forward. And I think it gives us a whole nother dynamic that we can deliver on in a way that hasn't been before. And I think that's kind of the holy grail of what people are looking for is how am I building modern applications and how do I have a database in the cloud that's really gonna support >>It? You know, you talk about distributed, you know, sequel and, and I mean, there's a little mystery behind it, isn't there? Or at least maybe not mystery. There's a little, I guess, confusion or, or just misunderstanding. I mean, I, how, nail that down a little bit. I >>Would say the best way to say it, honestly, this is the great thing, is it people believe it's too good to be true. And I think what we see over and over >>Again, you know, what they say about that. >>But this is the great part is, you know, you know, we've just had two taste studies recently with aws, with HIT labs and Certified power, both on expand, both proof in the pudding. They did the POCs, they're like, oh my God, this works. If you watch the keynote yesterday, you know, Adam had a slide that was, you know, as big as the entire room and it highlighted Samsung and they said, you know, we're doing 80,000 requests per second. So the, you know, the story there is that AWS is able as, as an entity with their scale and their breadth to handle that kind of workload. But guess what that is? That's MariaDB expand underneath there driving all of that utilization. So it's already there, it's already married, it's already in the cloud, and now we're taking it to a completely different level with a fully managed database solution. Right? >>How impressive is that? Right? I mean, you would think that somebody out there who, I mean that that volume, that kind of capacity is, is mind blowing. >>I mean, to your kind of previous point, it's like one of those things, do I see what's coming and it's here, right? You know, it's, is it actually ever gonna be possible? And now we're showing that it really is on a daily basis for some of the biggest brands in the world. We're also seeing companies moving off not only transitioning from, you know, MariaDB or myse, but all of the big licensed, you know, conversions as well. So you think about Oracle DBS Bank is one of our biggest customers, one of the largest Oracle conversions in the world onto MariaDB. And now thinking about what is the promise of connecting that to the cloud? How do you take things that you're currently doing, OnPrem delivering a hybrid model that also then starts to say, Hey, here's my path to cloud modernization. Skye gives me that bridge. And then you take it one layer farther and you think about multi-cloud, right? That's one of the things that's critical that ISVs can really only deliver in a meaningful way, is how can we have a solution for a customer that we can take to any availability zone. We can have performance, proximity, cost, proximity. We're always able to have that total data dexterity across any environment we need and we can build on that for the future. >>So if, if we're talking about cloud database and there's so many good things going forward here. You're talking about easy use and scalability and all that. But as with ever have you talked about this, there's some push and there's some pull. Yeah. So, so what's the, what's the other side that's still, you know, you that you think has to be >>Addressed? And I think that's a great question. So there's, we see that there's poll, right? We've seen these deals, this pipeline growth, this, there's great adoption. But what I think we're still not at the point of massive hockey stick adoption is that customers still don't fully understand the capabilities distributed SQL and the power they can actually deliver. So the more we drive case studies, the more we drive POCs, the more we prove the model, I think you're gonna see just a massive adoption scale. And I also think customers are tired of doing lots of different things in lots of different pockets. So neither one of the key elements of Sky SQL is we can do both transactional and analytical data out of the same database driven by the same proxy. So what, instead of having DBAs and developers try to figure out, okay, I'm gonna pull from this database here. >>Yeah. That there, it's, it's this big spaghetti wire concept that is super expensive and super time intensive. So the ability to write modern applications and pull data from both pockets and really be able to have that as a seamless entity and deliver that to customers is massive. I mean, another part of the keynote yesterday was a new deliverable, like kind of no etl. Adam talked about Aurora and Redshift and the massive complexity of what used to exist for getting data back and forth. You also have to pay for two different databases. It's super expensive. So I think the idea that you can take the real focus of AWS and US is customer value. How do you deliver that next thing that changes the game? Always utilizes AWS delivers on that promise, but then takes a net new technology that really starts to think about how do we bring things together? How do we make it more simple? How do we make it more powerful? And how do we deliver more customer value as we go forward? >>But you know, if, if I'm, I'm still an on-prim guy, just pretend I'm not saying I am. Just pretend I just for the sake of the discussion here, it's like I just can't let it go. Yeah. Right. I, I still, you know, there's control, there's the known versus the unknown. The uncertain. Yeah. So twist my arm just a little bit more and get me over the hum. >>Well, first of all, you don't have to, right? And there's gonna be some industries and some verticals that will always have elements of their business that will be OnPrem. Guess what? We make the best based in the world. It can be MariaDB, but there's those that then say, these, these elements of our business are gonna be far more effective moving to the cloud. So we give you Skye, there's a natural symbiotic bridge between everything we do and how we deliver it. Where you can be hybrid and it's great. You can adopt the cloud as your business needs grow. And you can have multi-cloud. This is that, that idea that you can, can have your cake and eat it too, right? You can literally have all these elements of your business met without these big pressure to say, you gotta throw that away. You gotta move to this. It's really, how do you kind of gracefully adopt the cloud in a way that makes sense for your business? Where are you trying to drive your business? Is it time to value, right? Is it governance? Is it is there's different elements of what matters the most to individual businesses. You know, we wanna address those and we can address >>Those. So you're saying you don't have to dive >>In, you don't have to dive >>In. You, you can, you can go ankle deep, knee deep, whatever you wanna >>Do. Absolutely. And you know, some of the largest MariaDB users still have massive, massive on-prem implementations. And that's okay. But there's elements that are starting to fall behind. There's cost savings, there's things that they need to do in the cloud that they can't do. OnPrem. And that's where expand Skye really says, okay, here is your platform. Grow as you want to, migrate as you want to. And we're there every step along the way. We, we also provide a whole Sky DBA team. Some guys just say, I wanna get outta the database world at all. This is, this is expensive, it's costly and it's difficult to be an expert. So you can bring in our DBA team and they'll man and run, they'll, they'll run your entire environment. They'll optimize it, you know, they'll troubleshoot it, they'll bug fix, they'll do everything for you. So you can just say, I just wanna focus on building phenomenal applications for my customers. And the database game as we knew it is not something that I know I want to invest in anymore. Right. I wanna make that transition >>That makes that really, yeah. You know, I mean really attractive to a lot of people because you are, you talk about a lot of headache there. Yeah. So let's talk about AWS before Sure. I let you go just about that relationship. Okay. You've talked about the platform that it provides you and, and obviously the benefits, but just talk about how you've worked with AWS over the years Yep. And, and how you see that relationship allowing you to expand your services, no pun intended. >>For sure. So, I mean, I would start with the way we even contemplated architecture. You know, we worked with the satisfactory team. We made sure that the things that we built were optimized in their environment. You know, I think it was a lot of collaboration on how does this combined entity really make the most value for our customers? How does it make the most sense for our developers as we build it out? Then we work in the, in the global startup team. So the strategic element of who we are, not all startups are created equal, right? We have, right, we have 75% of the Fortune 100, we've got over a billion downloads. So, you know, we come in with promise. And the reason this partnership is so valuable and the reason there's so much investment going forward is cuz what really, what do the cloud guys care about? >>The very, very most, they want all of these mission critical, big workloads that are on prem to land in their cloud. What do we have a massive, massive TAM sitting out there, these customers that could go to aws. So we both see, like if we can deliver incredible value to that customer base, these big workloads will end up in aws. They'll use other AWS services. And as we scale and grow, you know, we have that platform that's already built for it. So I think that when you go back to like the tenants, the core principles of aws, the one that always stands out, the one that we always kind of lean back on is, are we delivering customer value? Is this the best thing for the customer? Because we do have some competition just like many other, other partners do, right? So there is Aurora and there is rds and there is times when that's a great service for a customer. But when people are really thinking about where do I need my database to go? Where do I really need to be set for the future growth? Where am I gonna get the kind of ROI I need going forward? That's where you can go, Hey, sky sql, expand distributed sql. This is the best game in town. It's built on aws and collectively, you know, we're gonna present that to a customer. I'm >>Sold. Done. >>I love it. Right? >>Maria db, check 'em out, they're on the show floor. Great traffic. I know at at the, at the booth. They're here at AWS Reinvent. So check 'em out. Maria db. Thanks >>Kevin. Hey, thanks John. Appreciate your >>Time. Appreciate Great. That was great. Right back with more, you're watching the cube, the leader in high tech coverage.

>>Hi, I'm John Ferry with the Cube. We're here for a special exclusive conversation with David Geria, the CEO of Mongo MongoDB. Well established leading platform. It's been around for, I mean, decades. So continues to become the platform of choice for high performance data. This modern data stack that's emerging, a big part of the story here at a reinvent 2022 on top of an already performing a cloud with, you know, chips and silicon specialized instances, the world's gonna be getting faster, smaller, higher performance, lower cost specialized. Dave, thanks for taking the time with me today, >>John. It's great to be here. Thank you for having me. >>Do you see yourself as a ISV or you just go with that, because that's kind of a nomenclature >>When, when I think of the term isv, I think of the notion of someone building an end solution for customer to get something done. Or what we're building is essentially a developer data platform and we have thousands of ISVs who build software applications on our platform. So how could we be an isv? Because by definition I, you know, we enable people to do so many different things and you know, they can be the, you know, the largest companies of the world trying to transform their business or startups who are trying to disrupt either existing industries or create new ones. And so that's, and, and that's how our customers view MongoDB and, and the whole Atlas platform basically enables them to do some amazing things. The reason for that is, you know, you know, we believe that what we are enabling developers to do is be able to reduce the friction and the work required to build modern applications through the document model, which is really intuitive to the way developers think and code through the distributed nature of platforms. >>So, you know, things like charting no other company on the planet offers the capabilities we do to enable people to build the most highly performant and scalable applications. And also what we also do is enable people to, you know, run different types of workloads on our platform. So we have obviously transactional, we have search, we have time series, we enable people to do things like sophisticated device synchronization from Edge to the back end. We do graph, we do real time analytics. So being able to consolidate all that with developers on one elegant unified platform really makes, you know, it attractive for developers to build on long >>Db. You know, you guys are a feature partner of aws and I would speculate, I don't know if you can comment on this, but I would imagine that you probably produce a lot of revenue for Amazon because you really can't turn off EC two when you do a database work. So, you know, you kind of crank it all the time. You guys are a top partner. How long have you guys been a partner with aws? What's the relationship? >>The relationship's been strong, actually, Amazon spoke at one of our first user conferences in 2013. And since then we've been working together. We've been at reinvent since essentially 2015. And we've been a premier partner, an Emerald sponsor for the last Nu you know, I think four or five years. And so we're very committed to the relationship and I think there's some things that we have a lot, we have a lot of things in common. We care a lot about customers and for us, our customers, our developers, we care a lot about removing friction from their day to day work to move, be able to move fast and be able to, in order to seize new opportunities and respond to new threats. And so consequently, I think the partnership, obviously by nature of our, our common objectives has really come together. >>Talk about the journey of Mongo. I mean, you look back at the history, I, you go back the old lamp stack days, right? So you know, the day developer traction is just really kind of stuck at the none. I mean, it's, it's really well known. And I remember over the conversations, Dave Mongo doesn't scale. I mean, every year we heard something along those lines cuz it just kept scaling. I heard the same thing with AWS back in 2013 timeframe. You, oh, it's just, it's really not for a real prime time. It's, it's for hobbyists, not so much builders, maybe startup cloud, but that developer traction is translated. Can you take us through the journey of Mongo where it is now and, and kinda look back and, and, and take us through what's the state of the art now, >>Right? So just for those of you who, who, those, you know, those in your audience who don't know too much about Mon Be I'll just, you know, start with the background. The company was astounded by developers. It was basically the CTO and some key developers from Double Click who really saw the challenges and the limitations of the relational database architecture because they're trying to serve billions of ads per day and they constantly need to work on the constraints and relational database. And so they essentially decided, why don't we just build a database that we'd want to use? And that was a catalyst to starting MongoDB. The first thing they focused on was, rather than having a tabler data structure, they focused on a document data structure. Why documents? Because there's much more natural and intuitive to work with data and documents in terms of you can set parent child relationships and how you just think about the relationship with data is much more natural in a document than trying to connect data in a, you know, in hundreds of different tables. >>And so that enabled developers to just move so much faster. The second thing they focused on was building a truly distributed architecture, not kind of some adjunct, you know, you know, architecture that maybe made the existing architecture a little bit more scalable. They really took from the ground up a truly distributed architecture. So where you can do native replication, you can do charting and you can do it on a global basis. And so that was the, the other profound, you know, thing that they did. And then since then, what we've also done is, you know, the document model is truly a super set of other models. So we enabled other capabilities like search you can do joins, so you can do very transaction intensive use case among be where fully asset compliant. So you have the highest forms of data guarantees you can do very sophisticated things like time series, you can do device synchronization, you can do real time analytics because we can carve off read only nodes to be able to read and query data in real time rather than have to offload that data into a data warehouse. >>And so that enables developers to just build a wide variety of, of application longing to be, and they get one unified developer interface. It's highly elegant and seamless. And so essentially the cost and tax of matching multiple point tools goes away when, when I think of the term isv, I think of the notion of someone building an end solution for a customer to get something done. Or what we're building is essentially a developer data platform and we have thousands of ISVs who build software applications on our platform. So how could we be an isv? Because by definition I, you know, we enable people to do so many different things and you know, they can be the, you know, the largest companies in the world trying to transform their business or startups or trying to disrupt either existing industries or create new ones. And so that's, and and that's how our customers view MongoDB and, and the whole Atlas platform basically enables them to do some amazing things. >>Yeah, we're seeing a lot of activity on the Atlas. Do you see yourself as a ISV or you just go with that because that's kind of a nomenclature? >>No, we don't view ourselves as ISV at all. We view ourselves as a developer data platform. And the reason for that is, you know, you know, we believe that what we are enabling developers to do is be able to reduce the friction and the work required to build modern applications through the document model, which is really intuitive to the way developers think and code through the distributed nature of platforms. So, you know, things like sharding, no other company on the planet offers the capabilities we do to enable people to build the most highly performant and scalable applications. And also what we also do is enable people to, you know, run different types of workflows on our platform. So we have obviously transactional, we have search, we have time series, we enable people to do things like sophisticated device synchronization from Edge to the back end. We do graph, we do real time analytics. So being able to consolidate all that with developers on one elegant unified platform really makes, you know, it attractive for developers to build on long ndb. >>You know, the cloud adoption really is putting a lot of pressure on these systems and you're seeing companies in the ecosystem and AWS stepping up, you guys are doing great job, but we're seeing a lot more acceleration around it, on staying on premise for certain use cases. Yet you got the cloud as well growing for workloads and, and you get this hybrid steady state as an operational mode. I call that 10 of the classic cloud adoption track record. You guys are an example of multiple iterations in cloud. You're doing a lot more, we're starting to see this tipping point with others and customers coming kind of on that same pattern. Building platforms on top of aws on top of the primitives, more horsepower, higher level services, industry specific capabilities with data. I mean this is a new kind of cloud, kind of a next generation, you knows next gen you got the classic high performance infrastructure, it's getting better and better, but now you've got this new application platform, you know, reminds me of the old asp, you know, if you will. I mean, so are you seeing customers doing things differently? Can you share your, your reaction to this role of, you know, this new kind of SaaS platform that just isn't an application, it's, it's more, it's deeper than that. What's going on here? We call it super cloud, but >>Like what? Yeah, so essentially what what, you know, a lot of our customers doing, and by the way we have over 37,000 customers of all shapes and sizes from the largest companies in the world to cutting edge startups who are building applications among B, why do they choose MongoDB? Because essentially it's the, you know, the fastest way to innovate and the reason it's the fastest way to innovate is because they can work with data so much easier than working with data on other types of architecture. So the document model is profoundly a breakthrough way to work with data to make it very, very easy. So customers are essentially building these modern applications, you know, applications built on microservices, event driven architectures, you know, addressing sophisticated use cases like time series to, and then ultimately now they're getting into machine learning. We have a bunch of companies building machine learning applications on top of MongoDB. And the reason they're doing that is because one, they get the benefits of being able to, you know, build and work with, with data so much easier than any other platform. And it's highly scale and performant in a way that no other platform is. So literally they can run their, you know, workloads both locally and one, you know, autonomous zone or they can basically be or available zone or they could be basically, you know, anywhere in the world. And we also offer multicloud capabilities, which I can get into later. >>Let's talk about the performance side. I know I was speaking with some Amazon folks every year it's the same story. They're really working on the physics, they're getting the chips, they wanna squeeze as much energy out of that. I've never met a developer that said they wanna run their workload on a slower platform or slower hardware. We know said no developer, right? No one wants to do that. >>Correct. >>So you guys have a lot of experience tuning in with Graviton instances, we're seeing a lot more AWS EC two instances, we're seeing a lot more kind of integrated end to end stories. Data is now security, it's tied into data stacks or data modern kind of data hybrid stack. A lot going on around the hardware performance specialization, the role of data, kind of a modern data stack emerging. What, what's your thoughts on the that that Yeah, >>I, I think if you had asked me, you know, when the cloud started going vogue, like you know, the, you know, the, the later part of the last decade and told me, you know, sitting here 12, 15 years later, would you know, would we be talking about, you know, chip processing speeds? I'd probably thought, nah, we would've moved on by then. But what's really clear is that customers, to your point, customers care about performance, they care about price performance, right? So AWS's investments in Graviton, we have actually deployed a significant portion of our at fleet on Amazon now runs on Graviton. You know, they've built other chip sets like train and, and inferential for like, you know, training models and running inferences. They're doing things like Nitro. And so what that really speaks to is that the cloud providers are focusing on the price performance of their, as you call it, their primitives and their infrastructure and the infrastructure layer that are still very, very important. >>And, and you know, if you look at their revenue, about 60 to 70% of the revenue comes from that pure infrastructure. So to your point, they can't offer a second class solution and still win. So given that now they're seeing a lot of competition from Azure, Azure's building their own chip sets, Google's already obviously doing that and and building specialized chip sets for machine learning. You're seeing these cloud providers compete. So they have to really compete to make their platform the most performant, the most price competitive in the marketplace. Which gives us a great platform to build on to enable developers to build these incredibly highly performant applications that customers are now demand. >>I think that's a really great point. I mean, you know, it's so funny Dave, because you know, I remember those, we don't talk speeds and feeds anymore. We're not talking about boxes. I mean that's old kind of school thinking because it was a data center mentality, speeds and feeds and that was super important. But we're kind of coming back to that in the cloud now in distributed architecture, as you put your platforms out there for developers, you have to run fast. You gotta, you can't give the developer subpar or any kind of performance that's, they'll, they'll go somewhere else. I mean that's the reality of what developers, no one, again, no one says I wanna go on the slower platform unless it's some sort of policy based on price or some sort of thing. But, but for the most part it's gotta run fast. So you got the tail of two clouds going on here, you got Amazon classic ias, keep making it faster under the hood. >>And then you got the new abstraction layers of the higher level services. That's where you guys are bridging this new, new generational shift where it's like, hey, you know what? I can go, I can run a headless application, I can run a SAS app that's refactored with data. So you've seen a lot more innovation with developers, you know, running stuff in, in the C I C D pipeline that was once it, and you're seeing security and data operations kind of emerging as a structural change of how companies are, are are transforming on the business side. What's your reaction to that business transformation and the role of the developer? >>Right, so I mean I have to obviously give amazing kudos to the, you know, to AWS and the Amazon team for what they've built. Obviously they're the ones who kind of created the cloud industry and they continue to push the innovation in the space. I mean today they have over 300 services and you know, obviously, you know, no star today is building anything not on the cloud because they have so many building blocks to start with. But what we though have found from our talking to our customers is that in some ways there is still, you know, the onus is on the customer to figure out which building block to use to be able to stitch together the applications and solutions they wanna build. And what we have done is taken essentially an opinionated point of view and said we will enable you to do that. >>You know, using one data model. You know, Amazon today offers I think 17 or 18 different types of databases. We don't think like, you know, having a tool for every job makes sense because over time the tax and cost of learning, managing and supporting those different applications just don't make a lot of sense or just become cost prohibitive. And so we think offering one data model, one, you know, elegant user experience, you know, one way to address the broadest set of of use cases is that we think is a better way. But clearly customers have choice. They can use Amazon's primitives and those second layer services as you as you described, or they can use us. Unfortunately we've seen a lot of customers come to us with our approach and so does Amazon. And I have to give obviously again kudos and Amazon is very customer obsessed and so we have a great relationship with them, both technically in terms of the product integrations we do as well as working with 'em in the field, you know, on joint customer opportunities. >>Speaking of, while you mentioned that, I wanna just ask you, how is that marketplace relationship going with aws? Some of the partners are really seeing great economic and joint selling or them selling your, your stuff. So there's a real revenue pop there in that religion. Can you comment on that? >>So we had been working the partner in the marketplace for many years now, more from a field point of view where customers could leverage their existing commitments to AWS and leverage essentially, you know, using Atlas and applying in an atlas towards their commits. There was also some sales incentives for people in the field to basically work together so that, you know, everyone won should we collectively win a customer? What we recently announced is as pay as you Go initiative, where literally a customer on the Amazon marketplace can basically turn up, you know, an Alice instance with no commitment. So it's so easy. So we're just pushing the envelope to just reduce the friction for people to use Atlas on aws. And it's working really very well. The uptake has been been very strong and and we feel like we're just getting started because we're so excited about the results we're >>Seeing. You know, one of the things that's kind of not core in the keynote theme, but I think it's underlying message is clear in the industry, is the developer productivity. You said making things easy is a big deal, self-service, getting in and trying, these are what developer friendly tools are like and platform. So I have to ask you, cuz this comes up a lot in our kind of business conversation, is, is if you take digital transformation concept to its completion, assuming now you know, as a thought exercise, you completely transform a company with technology that's, that is the business transformation outcome. Take it to completion. What does that look like? I mean, if you go there you'd say, okay, the company is the app, the company is the data, it's not a department serving the business, it's the business. And so I think this is kind of what we're seeing as the next big mountain climb, which is companies that do transform there, they are technology companies, they're not a department like it. So I think a lot of companies are kind of saying, wait a minute, why would we have a department? It should be the company. What's your your your view on this because this >>Yeah, so I I've had the for good fortune of being able to talk to thousand customers all over the world. And you know, one thing John, they never tell me, they never tell me that they're innovating too quickly. In fact, they always tell me the reverse. They tell me all the obstacles and impediments they have to be able to be able to be able to move fast. So one of the reasons they gravitate to MongoDB is just the speed that they wish they can build applications to, to your point, developer productivity. And by definition, developer productivity is a proxy for innovation. The faster you can make your developers, you know, move, the faster they can push out code, the faster they can iterate and build new solutions or add more capabilities on the existing applications, the faster you can innovate either to, again, seize new opportunities or to respond to new threats in your business. >>And so that resonates with every C level executive. And to your point, the developers not some side hustle that they kind of think about once in a while. It's core to the business. So developers have amassed enormous amount of power and influence. You know, their, their, their engineering teams are front and center in terms of how they think about building capabilities and and building their business. And that's also obviously enabled, you know, to your point, every software company, every company's not becoming a software company because it all starts with softwares, software enables, defines or creates almost every company's value proposition. >>You know, it makes me smile because I love operating systems as one of my hobbies in college was, you know, systems programming and I remember those network kind of like the operating systems, the cloud. So, you know, everything's got specialized capabilities and that's a big theme here at Reinvent. If you look at the announcements Monday night with Peter DeSantis, you got, you got new instances, new chips. So this whole engine kind of specialized component is like an engine. You got a core and you got other subsystems. This is gonna be an integral part of how companies architect their platform or you know, Adam calls it the landing zone or whatever they wanna call it. But you gotta start seeing a new architectural thinking for companies. What's your, can you share your experience on how companies should look at this opportunity as a plethora of more goodness on the hardware? On hardware, but like chips and instances? Cause now you can mix and match. You've got, you've got, you got everything you need to kind of not roll your own but like really build foundational high performance capabilities. >>Yeah, so I I, so I think this is where I think Amazon is really enabling all companies, including, you know, companies like Mon db, you know, push the envelope and innovation. So for example, you know, the, the next big hurdle for us, I think we've seen two big platform shifts over the last 15 years of platform shifts, you know, to mobile and the platform shift to cloud. I believe the next big platform shift is going from dumb apps to smart apps, which you're building in, you know, machine learning and you know, AI and just very sophisticated automation. And when you start automating human decision making, rather than, you know, looking at a dashboard and saying, okay, I see the data now, now I have to do this. You can automate that into your applications and make your applications leveraging real time data become that much more smart. And that ultimately then becomes a developer challenge. And so we feel really good about our position in taking advantage of those next big trends and software leveraging the price performance curves that, you know, Amazon continues to push in terms of their hardware performance, networking performance, you know, you know, price, performance and storage to build those next generation of modern applications. >>Okay, so let me get this straight. You have next generation intelligent smart apps and you have AI generative solutions coming out around the corner. This is like pretty good position for Mongo to be in with data. I mean, this is what you do, you're in that exactly of the action. What's it like? I mean, you must be like trying to shake the world and wake up. The world's starting to wake up now through this. So what's, what's it like? >>Well, I mean we're really excited and bullish about the future. We think that we're well positioned because we know as to your point, you know, we have amassed amazing amount of developer mindshare. We are the most popular modern data platform out there in the world. There's developers in almost every corner of the planet using us to do something. And to your point, leveraging data and these advances in machine learning ai. And we think the more AI becomes democratized, not, you know, done by a bunch of data scientists sitting in some corner office, but essentially enabling developers to have the tools to build these very, very sophisticated, smart applications will, you know, will position as well. So that's, you know, obviously gonna be a focus for us over the, frankly, I think this is gonna be like a 10 year, 10 15 year run and we're just getting started in this whole >>Area. I think you guys are really well positioned. I think that's a great point. And Adam mentioned to me and, and Mike interviewed, he said on stage talk about it, the role of a data analyst kind of goes away. Everyone's a data analyst, right? You'll still see specialization on, on core data engineering, which is kind of like an SRE role for data. So data ops and data as code is a big deal making data applications. So again, exciting times and you guys are well positioned. If you had to bumper sticker the event this week here at Reinvent, what would you, how would you categorize this this point in time? I mean, Adam's great leader, he is gonna help educate customers how to use technology to, for business advantage and transformation. You know, Andy did a great job making technology great and innovative and setting the table, Adam's gotta bring it to the enterprises and businesses. So it's gonna be an interesting point in time we're in now. What, how would you categorize this year's reinvent, >>Right? I think the, the, the tech world is pivoting towards what I'd call rationalization or cost optimization. I think people obviously in, you know, the last 10 years have, you know, it's all about speed, speed, speed. And I think people still value speed, but they wanna do it at some sort of predictable cost model. And I think you're gonna see a lot more focus around cost and cost optimization. That's where we think having one platform is by definition of vendor consolidation way for people to cut costs so that they can basically, you know, still move fast but don't have to incur the tax of using a whole bunch of different point tools. And so we think we're well positioned. So the bumper sticker I think about is essentially, you know, do more for less with MongoDB. >>Yeah. And the developers on the front lines. Great stuff. You guys are great partner, a top partner at AWS and great reflection on, on where you guys been, but really where you are now and great opportunity. David Didier, thank you so much for spending the time and it's been great following Mongo and the continued rise of, of developers of the on the front lines really driving the business and that, and they are, I know, driving the business, so, and I think they're gonna continue Smart apps, intelligent apps, ai, generative apps are coming. I mean this is real. >>Thanks John. It's great speaking with >>You. Yeah, thanks. Thanks so much. Okay.

>>Okay, we're back. I'm Dave Valante with The Cube and you're watching Evolving Influx DB into the smart data platform made possible by influx data. Anna East Otis Georgio is here. She's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into realtime analytics. Anna is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IO X is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory, of course for speed. It's a kilo store, so it gives you compression efficiency, it's gonna give you faster query speeds, it gonna use store files and object storages. So you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOCs is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's lift tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import, super useful. Also, broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so a lot there. Now we talked to Brian about how you're using Rust and and which is not a new programming language and of course we had some drama around Russ during the pandemic with the Mozilla layoffs, but the formation of the Russ Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Rust was chosen because of his exceptional performance and rebi reliability. So while rust is synt tactically similar to c c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers and dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on card for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ, Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fixed race conditions to protect against buffering overflows and to ensure thread safe ay caching structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learned about the the new engine and the, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you're really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data and so much of the efficiency and performance of IOCs comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of illustrate why calmer data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then neighbor each other and when they neighbor each other in the storage format. This provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the min and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one times stamp and do that for every single row. So you're scanning across a ton more data and that's why row oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, calmer data fit framework. So that's where a lot of the advantages come >>From. Okay. So you've basically described like a traditional database, a row approach, but I've seen like a lot of traditional databases say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native it, is it not as effective as the, is the form not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. >>Yeah. Got it. So let's talk about Arrow data fusion. What is data fusion? I know it's written in rust, but what does it bring to to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as its in memory format. So the way that it helps influx DB IOx is that okay, it's great if you can write unlimited amount of cardinality into influx cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PDA's data frames as well and all of the machine learning tools associated with pandas. >>Okay. You're also leveraging par K in the platform course. We heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Par K and why is it important? >>Sure. So Par K is the calm oriented durable file format. So it's important because it'll enable bulk import and bulk export. It has compatibility with Python and pandas so it supports a broader ecosystem. Parque files also take very little disc disc space and they're faster to scan because again they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and these, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call it the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOCs and I really encourage if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and I just wanna learn more, then I would encourage you to go to the monthly tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel. Look for the influx D DB underscore IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about IOCs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how influx TB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and you guys super responsive, so really appreciate that. All right, thank you so much and East for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yokum. He's the director of engineering for Influx Data and we're gonna talk about how you update a SaaS engine while the plane is flying at 30,000 feet. You don't wanna miss this.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now, in this program, we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program, you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think, like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean, if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems. Certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean, commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away. Just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean, we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is, you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like, take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and, you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because, you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally, I would just say please, like watch in ice in Tim's sessions, Like these are two of our best and brightest. They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time, really hot area. As Brian said in a moment, I'll be right back with Anna East Dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't want to miss this.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>>When Oracle acquired my SQL via the Sun acquisition, nobody really thought the company would put much effort into the platform preferring to focus all the wood behind its leading Oracle database, Arrow pun intended. But two years ago, Oracle surprised many folks by announcing my SQL Heatwave a new database as a service with a massively parallel hybrid Columbia in Mary Mary architecture that brings together transactional and analytic data in a single platform. Welcome to our latest database, power panel on the cube. My name is Dave Ante, and today we're gonna discuss Oracle's MySQL Heat Wave with a who's who of cloud database industry analysts. Holgar Mueller is with Constellation Research. Mark Stammer is the Dragon Slayer and Wikibon contributor. And Ron Westfall is with Fu Chim Research. Gentlemen, welcome back to the Cube. Always a pleasure to have you on. Thanks for having us. Great to be here. >>So we've had a number of of deep dive interviews on the Cube with Nip and Aggarwal. You guys know him? He's a senior vice president of MySQL, Heatwave Development at Oracle. I think you just saw him at Oracle Cloud World and he's come on to describe this is gonna, I'll call it a shock and awe feature additions to to heatwave. You know, the company's clearly putting r and d into the platform and I think at at cloud world we saw like the fifth major release since 2020 when they first announced MySQL heat wave. So just listing a few, they, they got, they taken, brought in analytics machine learning, they got autopilot for machine learning, which is automation onto the basic o l TP functionality of the database. And it's been interesting to watch Oracle's converge database strategy. We've contrasted that amongst ourselves. Love to get your thoughts on Amazon's get the right tool for the right job approach. >>Are they gonna have to change that? You know, Amazon's got the specialized databases, it's just, you know, the both companies are doing well. It just shows there are a lot of ways to, to skin a cat cuz you see some traction in the market in, in both approaches. So today we're gonna focus on the latest heat wave announcements and we're gonna talk about multi-cloud with a native MySQL heat wave implementation, which is available on aws MySQL heat wave for Azure via the Oracle Microsoft interconnect. This kind of cool hybrid action that they got going. Sometimes we call it super cloud. And then we're gonna dive into my SQL Heatwave Lake house, which allows users to process and query data across MyQ databases as heatwave databases, as well as object stores. So, and then we've got, heatwave has been announced on AWS and, and, and Azure, they're available now and Lake House I believe is in beta and I think it's coming out the second half of next year. So again, all of our guests are fresh off of Oracle Cloud world in Las Vegas. So they got the latest scoop. Guys, I'm done talking. Let's get into it. Mark, maybe you could start us off, what's your opinion of my SQL Heatwaves competitive position? When you think about what AWS is doing, you know, Google is, you know, we heard Google Cloud next recently, we heard about all their data innovations. You got, obviously Azure's got a big portfolio, snowflakes doing well in the market. What's your take? >>Well, first let's look at it from the point of view that AWS is the market leader in cloud and cloud services. They own somewhere between 30 to 50% depending on who you read of the market. And then you have Azure as number two and after that it falls off. There's gcp, Google Cloud platform, which is further way down the list and then Oracle and IBM and Alibaba. So when you look at AWS and you and Azure saying, hey, these are the market leaders in the cloud, then you start looking at it and saying, if I am going to provide a service that competes with the service they have, if I can make it available in their cloud, it means that I can be more competitive. And if I'm compelling and compelling means at least twice the performance or functionality or both at half the price, I should be able to gain market share. >>And that's what Oracle's done. They've taken a superior product in my SQL heat wave, which is faster, lower cost does more for a lot less at the end of the day and they make it available to the users of those clouds. You avoid this little thing called egress fees, you avoid the issue of having to migrate from one cloud to another and suddenly you have a very compelling offer. So I look at what Oracle's doing with MyQ and it feels like, I'm gonna use a word term, a flanking maneuver to their competition. They're offering a better service on their platforms. >>All right, so thank you for that. Holger, we've seen this sort of cadence, I sort of referenced it up front a little bit and they sat on MySQL for a decade, then all of a sudden we see this rush of announcements. Why did it take so long? And and more importantly is Oracle, are they developing the right features that cloud database customers are looking for in your view? >>Yeah, great question, but first of all, in your interview you said it's the edit analytics, right? Analytics is kind of like a marketing buzzword. Reports can be analytics, right? The interesting thing, which they did, the first thing they, they, they crossed the chasm between OTP and all up, right? In the same database, right? So major engineering feed very much what customers want and it's all about creating Bellevue for customers, which, which I think is the part why they go into the multi-cloud and why they add these capabilities. And they certainly with the AI capabilities, it's kind of like getting it into an autonomous field, self-driving field now with the lake cost capabilities and meeting customers where they are, like Mark has talked about the e risk costs in the cloud. So that that's a significant advantage, creating value for customers and that's what at the end of the day matters. >>And I believe strongly that long term it's gonna be ones who create better value for customers who will get more of their money From that perspective, why then take them so long? I think it's a great question. I think largely he mentioned the gentleman Nial, it's largely to who leads a product. I used to build products too, so maybe I'm a little fooling myself here, but that made the difference in my view, right? So since he's been charged, he's been building things faster than the rest of the competition, than my SQL space, which in hindsight we thought was a hot and smoking innovation phase. It kind of like was a little self complacent when it comes to the traditional borders of where, where people think, where things are separated between OTP and ola or as an example of adjacent support, right? Structured documents, whereas unstructured documents or databases and all of that has been collapsed and brought together for building a more powerful database for customers. >>So I mean it's certainly, you know, when, when Oracle talks about the competitors, you know, the competitors are in the, I always say they're, if the Oracle talks about you and knows you're doing well, so they talk a lot about aws, talk a little bit about Snowflake, you know, sort of Google, they have partnerships with Azure, but, but in, so I'm presuming that the response in MySQL heatwave was really in, in response to what they were seeing from those big competitors. But then you had Maria DB coming out, you know, the day that that Oracle acquired Sun and, and launching and going after the MySQL base. So it's, I'm, I'm interested and we'll talk about this later and what you guys think AWS and Google and Azure and Snowflake and how they're gonna respond. But, but before I do that, Ron, I want to ask you, you, you, you can get, you know, pretty technical and you've probably seen the benchmarks. >>I know you have Oracle makes a big deal out of it, publishes its benchmarks, makes some transparent on on GI GitHub. Larry Ellison talked about this in his keynote at Cloud World. What are the benchmarks show in general? I mean, when you, when you're new to the market, you gotta have a story like Mark was saying, you gotta be two x you know, the performance at half the cost or you better be or you're not gonna get any market share. So, and, and you know, oftentimes companies don't publish market benchmarks when they're leading. They do it when they, they need to gain share. So what do you make of the benchmarks? Have their, any results that were surprising to you? Have, you know, they been challenged by the competitors. Is it just a bunch of kind of desperate bench marketing to make some noise in the market or you know, are they real? What's your view? >>Well, from my perspective, I think they have the validity. And to your point, I believe that when it comes to competitor responses, that has not really happened. Nobody has like pulled down the information that's on GitHub and said, Oh, here are our price performance results. And they counter oracles. In fact, I think part of the reason why that hasn't happened is that there's the risk if Oracle's coming out and saying, Hey, we can deliver 17 times better query performance using our capabilities versus say, Snowflake when it comes to, you know, the Lakehouse platform and Snowflake turns around and says it's actually only 15 times better during performance, that's not exactly an effective maneuver. And so I think this is really to oracle's credit and I think it's refreshing because these differentiators are significant. We're not talking, you know, like 1.2% differences. We're talking 17 fold differences, we're talking six fold differences depending on, you know, where the spotlight is being shined and so forth. >>And so I think this is actually something that is actually too good to believe initially at first blush. If I'm a cloud database decision maker, I really have to prioritize this. I really would know, pay a lot more attention to this. And that's why I posed the question to Oracle and others like, okay, if these differentiators are so significant, why isn't the needle moving a bit more? And it's for, you know, some of the usual reasons. One is really deep discounting coming from, you know, the other players that's really kind of, you know, marketing 1 0 1, this is something you need to do when there's a real competitive threat to keep, you know, a customer in your own customer base. Plus there is the usual fear and uncertainty about moving from one platform to another. But I think, you know, the traction, the momentum is, is shifting an Oracle's favor. I think we saw that in the Q1 efforts, for example, where Oracle cloud grew 44% and that it generated, you know, 4.8 billion and revenue if I recall correctly. And so, so all these are demonstrating that's Oracle is making, I think many of the right moves, publishing these figures for anybody to look at from their own perspective is something that is, I think, good for the market and I think it's just gonna continue to pay dividends for Oracle down the horizon as you know, competition intens plots. So if I were in, >>Dave, can I, Dave, can I interject something and, and what Ron just said there? Yeah, please go ahead. A couple things here, one discounting, which is a common practice when you have a real threat, as Ron pointed out, isn't going to help much in this situation simply because you can't discount to the point where you improve your performance and the performance is a huge differentiator. You may be able to get your price down, but the problem that most of them have is they don't have an integrated product service. They don't have an integrated O L T P O L A P M L N data lake. Even if you cut out two of them, they don't have any of them integrated. They have multiple services that are required separate integration and that can't be overcome with discounting. And the, they, you have to pay for each one of these. And oh, by the way, as you grow, the discounts go away. So that's a, it's a minor important detail. >>So, so that's a TCO question mark, right? And I know you look at this a lot, if I had that kind of price performance advantage, I would be pounding tco, especially if I need two separate databases to do the job. That one can do, that's gonna be, the TCO numbers are gonna be off the chart or maybe down the chart, which you want. Have you looked at this and how does it compare with, you know, the big cloud guys, for example, >>I've looked at it in depth, in fact, I'm working on another TCO on this arena, but you can find it on Wiki bod in which I compared TCO for MySEQ Heat wave versus Aurora plus Redshift plus ML plus Blue. I've compared it against gcps services, Azure services, Snowflake with other services. And there's just no comparison. The, the TCO differences are huge. More importantly, thefor, the, the TCO per performance is huge. We're talking in some cases multiple orders of magnitude, but at least an order of magnitude difference. So discounting isn't gonna help you much at the end of the day, it's only going to lower your cost a little, but it doesn't improve the automation, it doesn't improve the performance, it doesn't improve the time to insight, it doesn't improve all those things that you want out of a database or multiple databases because you >>Can't discount yourself to a higher value proposition. >>So what about, I wonder ho if you could chime in on the developer angle. You, you followed that, that market. How do these innovations from heatwave, I think you used the term developer velocity. I've heard you used that before. Yeah, I mean, look, Oracle owns Java, okay, so it, it's, you know, most popular, you know, programming language in the world, blah, blah blah. But it does it have the, the minds and hearts of, of developers and does, where does heatwave fit into that equation? >>I think heatwave is gaining quickly mindshare on the developer side, right? It's not the traditional no sequel database which grew up, there's a traditional mistrust of oracles to developers to what was happening to open source when gets acquired. Like in the case of Oracle versus Java and where my sql, right? And, but we know it's not a good competitive strategy to, to bank on Oracle screwing up because it hasn't worked not on Java known my sequel, right? And for developers, it's, once you get to know a technology product and you can do more, it becomes kind of like a Swiss army knife and you can build more use case, you can build more powerful applications. That's super, super important because you don't have to get certified in multiple databases. You, you are fast at getting things done, you achieve fire, develop velocity, and the managers are happy because they don't have to license more things, send you to more trainings, have more risk of something not being delivered, right? >>So it's really the, we see the suite where this best of breed play happening here, which in general was happening before already with Oracle's flagship database. Whereas those Amazon as an example, right? And now the interesting thing is every step away Oracle was always a one database company that can be only one and they're now generally talking about heat web and that two database company with different market spaces, but same value proposition of integrating more things very, very quickly to have a universal database that I call, they call the converge database for all the needs of an enterprise to run certain application use cases. And that's what's attractive to developers. >>It's, it's ironic isn't it? I mean I, you know, the rumor was the TK Thomas Curian left Oracle cuz he wanted to put Oracle database on other clouds and other places. And maybe that was the rift. Maybe there was, I'm sure there was other things, but, but Oracle clearly is now trying to expand its Tam Ron with, with heatwave into aws, into Azure. How do you think Oracle's gonna do, you were at a cloud world, what was the sentiment from customers and the independent analyst? Is this just Oracle trying to screw with the competition, create a little diversion? Or is this, you know, serious business for Oracle? What do you think? >>No, I think it has lakes. I think it's definitely, again, attriting to Oracle's overall ability to differentiate not only my SQL heat wave, but its overall portfolio. And I think the fact that they do have the alliance with the Azure in place, that this is definitely demonstrating their commitment to meeting the multi-cloud needs of its customers as well as what we pointed to in terms of the fact that they're now offering, you know, MySQL capabilities within AWS natively and that it can now perform AWS's own offering. And I think this is all demonstrating that Oracle is, you know, not letting up, they're not resting on its laurels. That's clearly we are living in a multi-cloud world, so why not just make it more easy for customers to be able to use cloud databases according to their own specific, specific needs. And I think, you know, to holder's point, I think that definitely lines with being able to bring on more application developers to leverage these capabilities. >>I think one important announcement that's related to all this was the JSON relational duality capabilities where now it's a lot easier for application developers to use a language that they're very familiar with a JS O and not have to worry about going into relational databases to store their J S O N application coding. So this is, I think an example of the innovation that's enhancing the overall Oracle portfolio and certainly all the work with machine learning is definitely paying dividends as well. And as a result, I see Oracle continue to make these inroads that we pointed to. But I agree with Mark, you know, the short term discounting is just a stall tag. This is not denying the fact that Oracle is being able to not only deliver price performance differentiators that are dramatic, but also meeting a wide range of needs for customers out there that aren't just limited device performance consideration. >>Being able to support multi-cloud according to customer needs. Being able to reach out to the application developer community and address a very specific challenge that has plagued them for many years now. So bring it all together. Yeah, I see this as just enabling Oracles who ring true with customers. That the customers that were there were basically all of them, even though not all of them are going to be saying the same things, they're all basically saying positive feedback. And likewise, I think the analyst community is seeing this. It's always refreshing to be able to talk to customers directly and at Oracle cloud there was a litany of them and so this is just a difference maker as well as being able to talk to strategic partners. The nvidia, I think partnerships also testament to Oracle's ongoing ability to, you know, make the ecosystem more user friendly for the customers out there. >>Yeah, it's interesting when you get these all in one tools, you know, the Swiss Army knife, you expect that it's not able to be best of breed. That's the kind of surprising thing that I'm hearing about, about heatwave. I want to, I want to talk about Lake House because when I think of Lake House, I think data bricks, and to my knowledge data bricks hasn't been in the sites of Oracle yet. Maybe they're next, but, but Oracle claims that MySQL, heatwave, Lakehouse is a breakthrough in terms of capacity and performance. Mark, what are your thoughts on that? Can you double click on, on Lakehouse Oracle's claims for things like query performance and data loading? What does it mean for the market? Is Oracle really leading in, in the lake house competitive landscape? What are your thoughts? >>Well, but name in the game is what are the problems you're solving for the customer? More importantly, are those problems urgent or important? If they're urgent, customers wanna solve 'em. Now if they're important, they might get around to them. So you look at what they're doing with Lake House or previous to that machine learning or previous to that automation or previous to that O L A with O ltp and they're merging all this capability together. If you look at Snowflake or data bricks, they're tacking one problem. You look at MyQ heat wave, they're tacking multiple problems. So when you say, yeah, their queries are much better against the lake house in combination with other analytics in combination with O ltp and the fact that there are no ETLs. So you're getting all this done in real time. So it's, it's doing the query cross, cross everything in real time. >>You're solving multiple user and developer problems, you're increasing their ability to get insight faster, you're having shorter response times. So yeah, they really are solving urgent problems for customers. And by putting it where the customer lives, this is the brilliance of actually being multicloud. And I know I'm backing up here a second, but by making it work in AWS and Azure where people already live, where they already have applications, what they're saying is, we're bringing it to you. You don't have to come to us to get these, these benefits, this value overall, I think it's a brilliant strategy. I give Nip and Argo wallet a huge, huge kudos for what he's doing there. So yes, what they're doing with the lake house is going to put notice on data bricks and Snowflake and everyone else for that matter. Well >>Those are guys that whole ago you, you and I have talked about this. Those are, those are the guys that are doing sort of the best of breed. You know, they're really focused and they, you know, tend to do well at least out of the gate. Now you got Oracle's converged philosophy, obviously with Oracle database. We've seen that now it's kicking in gear with, with heatwave, you know, this whole thing of sweets versus best of breed. I mean the long term, you know, customers tend to migrate towards suite, but the new shiny toy tends to get the growth. How do you think this is gonna play out in cloud database? >>Well, it's the forever never ending story, right? And in software right suite, whereas best of breed and so far in the long run suites have always won, right? So, and sometimes they struggle again because the inherent problem of sweets is you build something larger, it has more complexity and that means your cycles to get everything working together to integrate the test that roll it out, certify whatever it is, takes you longer, right? And that's not the case. It's a fascinating part of what the effort around my SQL heat wave is that the team is out executing the previous best of breed data, bringing us something together. Now if they can maintain that pace, that's something to to, to be seen. But it, the strategy, like what Mark was saying, bring the software to the data is of course interesting and unique and totally an Oracle issue in the past, right? >>Yeah. But it had to be in your database on oci. And but at, that's an interesting part. The interesting thing on the Lake health side is, right, there's three key benefits of a lakehouse. The first one is better reporting analytics, bring more rich information together, like make the, the, the case for silicon angle, right? We want to see engagements for this video, we want to know what's happening. That's a mixed transactional video media use case, right? Typical Lakehouse use case. The next one is to build more rich applications, transactional applications which have video and these elements in there, which are the engaging one. And the third one, and that's where I'm a little critical and concerned, is it's really the base platform for artificial intelligence, right? To run deep learning to run things automatically because they have all the data in one place can create in one way. >>And that's where Oracle, I know that Ron talked about Invidia for a moment, but that's where Oracle doesn't have the strongest best story. Nonetheless, the two other main use cases of the lake house are very strong, very well only concern is four 50 terabyte sounds long. It's an arbitrary limitation. Yeah, sounds as big. So for the start, and it's the first word, they can make that bigger. You don't want your lake house to be limited and the terabyte sizes or any even petabyte size because you want to have the certainty. I can put everything in there that I think it might be relevant without knowing what questions to ask and query those questions. >>Yeah. And you know, in the early days of no schema on right, it just became a mess. But now technology has evolved to allow us to actually get more value out of that data. Data lake. Data swamp is, you know, not much more, more, more, more logical. But, and I want to get in, in a moment, I want to come back to how you think the competitors are gonna respond. Are they gonna have to sort of do a more of a converged approach? AWS in particular? But before I do, Ron, I want to ask you a question about autopilot because I heard Larry Ellison's keynote and he was talking about how, you know, most security issues are human errors with autonomy and autonomous database and things like autopilot. We take care of that. It's like autonomous vehicles, they're gonna be safer. And I went, well maybe, maybe someday. So Oracle really tries to emphasize this, that every time you see an announcement from Oracle, they talk about new, you know, autonomous capabilities. It, how legit is it? Do people care? What about, you know, what's new for heatwave Lakehouse? How much of a differentiator, Ron, do you really think autopilot is in this cloud database space? >>Yeah, I think it will definitely enhance the overall proposition. I don't think people are gonna buy, you know, lake house exclusively cause of autopilot capabilities, but when they look at the overall picture, I think it will be an added capability bonus to Oracle's benefit. And yeah, I think it's kind of one of these age old questions, how much do you automate and what is the bounce to strike? And I think we all understand with the automatic car, autonomous car analogy that there are limitations to being able to use that. However, I think it's a tool that basically every organization out there needs to at least have or at least evaluate because it goes to the point of it helps with ease of use, it helps make automation more balanced in terms of, you know, being able to test, all right, let's automate this process and see if it works well, then we can go on and switch on on autopilot for other processes. >>And then, you know, that allows, for example, the specialists to spend more time on business use cases versus, you know, manual maintenance of, of the cloud database and so forth. So I think that actually is a, a legitimate value proposition. I think it's just gonna be a case by case basis. Some organizations are gonna be more aggressive with putting automation throughout their processes throughout their organization. Others are gonna be more cautious. But it's gonna be, again, something that will help the overall Oracle proposition. And something that I think will be used with caution by many organizations, but other organizations are gonna like, hey, great, this is something that is really answering a real problem. And that is just easing the use of these databases, but also being able to better handle the automation capabilities and benefits that come with it without having, you know, a major screwup happened and the process of transitioning to more automated capabilities. >>Now, I didn't attend cloud world, it's just too many red eyes, you know, recently, so I passed. But one of the things I like to do at those events is talk to customers, you know, in the spirit of the truth, you know, they, you know, you'd have the hallway, you know, track and to talk to customers and they say, Hey, you know, here's the good, the bad and the ugly. So did you guys, did you talk to any customers my SQL Heatwave customers at, at cloud world? And and what did you learn? I don't know, Mark, did you, did you have any luck and, and having some, some private conversations? >>Yeah, I had quite a few private conversations. The one thing before I get to that, I want disagree with one point Ron made, I do believe there are customers out there buying the heat wave service, the MySEQ heat wave server service because of autopilot. Because autopilot is really revolutionary in many ways in the sense for the MySEQ developer in that it, it auto provisions, it auto parallel loads, IT auto data places it auto shape predictions. It can tell you what machine learning models are going to tell you, gonna give you your best results. And, and candidly, I've yet to meet a DBA who didn't wanna give up pedantic tasks that are pain in the kahoo, which they'd rather not do and if it's long as it was done right for them. So yes, I do think people are buying it because of autopilot and that's based on some of the conversations I had with customers at Oracle Cloud World. >>In fact, it was like, yeah, that's great, yeah, we get fantastic performance, but this really makes my life easier and I've yet to meet a DBA who didn't want to make their life easier. And it does. So yeah, I've talked to a few of them. They were excited. I asked them if they ran into any bugs, were there any difficulties in moving to it? And the answer was no. In both cases, it's interesting to note, my sequel is the most popular database on the planet. Well, some will argue that it's neck and neck with SQL Server, but if you add in Mariah DB and ProCon db, which are forks of MySQL, then yeah, by far and away it's the most popular. And as a result of that, everybody for the most part has typically a my sequel database somewhere in their organization. So this is a brilliant situation for anybody going after MyQ, but especially for heat wave. And the customers I talk to love it. I didn't find anybody complaining about it. And >>What about the migration? We talked about TCO earlier. Did your t does your TCO analysis include the migration cost or do you kind of conveniently leave that out or what? >>Well, when you look at migration costs, there are different kinds of migration costs. By the way, the worst job in the data center is the data migration manager. Forget it, no other job is as bad as that one. You get no attaboys for doing it. Right? And then when you screw up, oh boy. So in real terms, anything that can limit data migration is a good thing. And when you look at Data Lake, that limits data migration. So if you're already a MySEQ user, this is a pure MySQL as far as you're concerned. It's just a, a simple transition from one to the other. You may wanna make sure nothing broke and every you, all your tables are correct and your schema's, okay, but it's all the same. So it's a simple migration. So it's pretty much a non-event, right? When you migrate data from an O LTP to an O L A P, that's an ETL and that's gonna take time. >>But you don't have to do that with my SQL heat wave. So that's gone when you start talking about machine learning, again, you may have an etl, you may not, depending on the circumstances, but again, with my SQL heat wave, you don't, and you don't have duplicate storage, you don't have to copy it from one storage container to another to be able to be used in a different database, which by the way, ultimately adds much more cost than just the other service. So yeah, I looked at the migration and again, the users I talked to said it was a non-event. It was literally moving from one physical machine to another. If they had a new version of MySEQ running on something else and just wanted to migrate it over or just hook it up or just connect it to the data, it worked just fine. >>Okay, so every day it sounds like you guys feel, and we've certainly heard this, my colleague David Foyer, the semi-retired David Foyer was always very high on heatwave. So I think you knows got some real legitimacy here coming from a standing start, but I wanna talk about the competition, how they're likely to respond. I mean, if your AWS and you got heatwave is now in your cloud, so there's some good aspects of that. The database guys might not like that, but the infrastructure guys probably love it. Hey, more ways to sell, you know, EC two and graviton, but you're gonna, the database guys in AWS are gonna respond. They're gonna say, Hey, we got Redshift, we got aqua. What's your thoughts on, on not only how that's gonna resonate with customers, but I'm interested in what you guys think will a, I never say never about aws, you know, and are they gonna try to build, in your view a converged Oola and o LTP database? You know, Snowflake is taking an ecosystem approach. They've added in transactional capabilities to the portfolio so they're not standing still. What do you guys see in the competitive landscape in that regard going forward? Maybe Holger, you could start us off and anybody else who wants to can chime in, >>Happy to, you mentioned Snowflake last, we'll start there. I think Snowflake is imitating that strategy, right? That building out original data warehouse and the clouds tasking project to really proposition to have other data available there because AI is relevant for everybody. Ultimately people keep data in the cloud for ultimately running ai. So you see the same suite kind of like level strategy, it's gonna be a little harder because of the original positioning. How much would people know that you're doing other stuff? And I just, as a former developer manager of developers, I just don't see the speed at the moment happening at Snowflake to become really competitive to Oracle. On the flip side, putting my Oracle hat on for a moment back to you, Mark and Iran, right? What could Oracle still add? Because the, the big big things, right? The traditional chasms in the database world, they have built everything, right? >>So I, I really scratched my hat and gave Nipon a hard time at Cloud world say like, what could you be building? Destiny was very conservative. Let's get the Lakehouse thing done, it's gonna spring next year, right? And the AWS is really hard because AWS value proposition is these small innovation teams, right? That they build two pizza teams, which can be fit by two pizzas, not large teams, right? And you need suites to large teams to build these suites with lots of functionalities to make sure they work together. They're consistent, they have the same UX on the administration side, they can consume the same way, they have the same API registry, can't even stop going where the synergy comes to play over suite. So, so it's gonna be really, really hard for them to change that. But AWS super pragmatic. They're always by themselves that they'll listen to customers if they learn from customers suite as a proposition. I would not be surprised if AWS trying to bring things closer together, being morely together. >>Yeah. Well how about, can we talk about multicloud if, if, again, Oracle is very on on Oracle as you said before, but let's look forward, you know, half a year or a year. What do you think about Oracle's moves in, in multicloud in terms of what kind of penetration they're gonna have in the marketplace? You saw a lot of presentations at at cloud world, you know, we've looked pretty closely at the, the Microsoft Azure deal. I think that's really interesting. I've, I've called it a little bit of early days of a super cloud. What impact do you think this is gonna have on, on the marketplace? But, but both. And think about it within Oracle's customer base, I have no doubt they'll do great there. But what about beyond its existing install base? What do you guys think? >>Ryan, do you wanna jump on that? Go ahead. Go ahead Ryan. No, no, no, >>That's an excellent point. I think it aligns with what we've been talking about in terms of Lakehouse. I think Lake House will enable Oracle to pull more customers, more bicycle customers onto the Oracle platforms. And I think we're seeing all the signs pointing toward Oracle being able to make more inroads into the overall market. And that includes garnishing customers from the leaders in, in other words, because they are, you know, coming in as a innovator, a an alternative to, you know, the AWS proposition, the Google cloud proposition that they have less to lose and there's a result they can really drive the multi-cloud messaging to resonate with not only their existing customers, but also to be able to, to that question, Dave's posing actually garnish customers onto their platform. And, and that includes naturally my sequel but also OCI and so forth. So that's how I'm seeing this playing out. I think, you know, again, Oracle's reporting is indicating that, and I think what we saw, Oracle Cloud world is definitely validating the idea that Oracle can make more waves in the overall market in this regard. >>You know, I, I've floated this idea of Super cloud, it's kind of tongue in cheek, but, but there, I think there is some merit to it in terms of building on top of hyperscale infrastructure and abstracting some of the, that complexity. And one of the things that I'm most interested in is industry clouds and an Oracle acquisition of Cerner. I was struck by Larry Ellison's keynote, it was like, I don't know, an hour and a half and an hour and 15 minutes was focused on healthcare transformation. Well, >>So vertical, >>Right? And so, yeah, so you got Oracle's, you know, got some industry chops and you, and then you think about what they're building with, with not only oci, but then you got, you know, MyQ, you can now run in dedicated regions. You got ADB on on Exadata cloud to customer, you can put that OnPrem in in your data center and you look at what the other hyperscalers are, are doing. I I say other hyperscalers, I've always said Oracle's not really a hyperscaler, but they got a cloud so they're in the game. But you can't get, you know, big query OnPrem, you look at outposts, it's very limited in terms of, you know, the database support and again, that that will will evolve. But now you got Oracle's got, they announced Alloy, we can white label their cloud. So I'm interested in what you guys think about these moves, especially the industry cloud. We see, you know, Walmart is doing sort of their own cloud. You got Goldman Sachs doing a cloud. Do you, you guys, what do you think about that and what role does Oracle play? Any thoughts? >>Yeah, let me lemme jump on that for a moment. Now, especially with the MyQ, by making that available in multiple clouds, what they're doing is this follows the philosophy they've had the past with doing cloud, a customer taking the application and the data and putting it where the customer lives. If it's on premise, it's on premise. If it's in the cloud, it's in the cloud. By making the mice equal heat wave, essentially a plug compatible with any other mice equal as far as your, your database is concern and then giving you that integration with O L A P and ML and Data Lake and everything else, then what you've got is a compelling offering. You're making it easier for the customer to use. So I look the difference between MyQ and the Oracle database, MyQ is going to capture market more market share for them. >>You're not gonna find a lot of new users for the Oracle debate database. Yeah, there are always gonna be new users, don't get me wrong, but it's not gonna be a huge growth. Whereas my SQL heatwave is probably gonna be a major growth engine for Oracle going forward. Not just in their own cloud, but in AWS and in Azure and on premise over time that eventually it'll get there. It's not there now, but it will, they're doing the right thing on that basis. They're taking the services and when you talk about multicloud and making them available where the customer wants them, not forcing them to go where you want them, if that makes sense. And as far as where they're going in the future, I think they're gonna take a page outta what they've done with the Oracle database. They'll add things like JSON and XML and time series and spatial over time they'll make it a, a complete converged database like they did with the Oracle database. The difference being Oracle database will scale bigger and will have more transactions and be somewhat faster. And my SQL will be, for anyone who's not on the Oracle database, they're, they're not stupid, that's for sure. >>They've done Jason already. Right. But I give you that they could add graph and time series, right. Since eat with, Right, Right. Yeah, that's something absolutely right. That's, that's >>A sort of a logical move, right? >>Right. But that's, that's some kid ourselves, right? I mean has worked in Oracle's favor, right? 10 x 20 x, the amount of r and d, which is in the MyQ space, has been poured at trying to snatch workloads away from Oracle by starting with IBM 30 years ago, 20 years ago, Microsoft and, and, and, and didn't work, right? Database applications are extremely sticky when they run, you don't want to touch SIM and grow them, right? So that doesn't mean that heat phase is not an attractive offering, but it will be net new things, right? And what works in my SQL heat wave heat phases favor a little bit is it's not the massive enterprise applications which have like we the nails like, like you might be only running 30% or Oracle, but the connections and the interfaces into that is, is like 70, 80% of your enterprise. >>You take it out and it's like the spaghetti ball where you say, ah, no I really don't, don't want to do all that. Right? You don't, don't have that massive part with the equals heat phase sequel kind of like database which are more smaller tactical in comparison, but still I, I don't see them taking so much share. They will be growing because of a attractive value proposition quickly on the, the multi-cloud, right? I think it's not really multi-cloud. If you give people the chance to run your offering on different clouds, right? You can run it there. The multi-cloud advantages when the Uber offering comes out, which allows you to do things across those installations, right? I can migrate data, I can create data across something like Google has done with B query Omni, I can run predictive models or even make iron models in different place and distribute them, right? And Oracle is paving the road for that, but being available on these clouds. But the multi-cloud capability of database which knows I'm running on different clouds that is still yet to be built there. >>Yeah. And >>That the problem with >>That, that's the super cloud concept that I flowed and I I've always said kinda snowflake with a single global instance is sort of, you know, headed in that direction and maybe has a league. What's the issue with that mark? >>Yeah, the problem with the, with that version, the multi-cloud is clouds to charge egress fees. As long as they charge egress fees to move data between clouds, it's gonna make it very difficult to do a real multi-cloud implementation. Even Snowflake, which runs multi-cloud, has to pass out on the egress fees of their customer when data moves between clouds. And that's really expensive. I mean there, there is one customer I talked to who is beta testing for them, the MySQL heatwave and aws. The only reason they didn't want to do that until it was running on AWS is the egress fees were so great to move it to OCI that they couldn't afford it. Yeah. Egress fees are the big issue but, >>But Mark the, the point might be you might wanna root query and only get the results set back, right was much more tinier, which been the answer before for low latency between the class A problem, which we sometimes still have but mostly don't have. Right? And I think in general this with fees coming down based on the Oracle general E with fee move and it's very hard to justify those, right? But, but it's, it's not about moving data as a multi-cloud high value use case. It's about doing intelligent things with that data, right? Putting into other places, replicating it, what I'm saying the same thing what you said before, running remote queries on that, analyzing it, running AI on it, running AI models on that. That's the interesting thing. Cross administered in the same way. Taking things out, making sure compliance happens. Making sure when Ron says I don't want to be American anymore, I want to be in the European cloud that is gets migrated, right? So tho those are the interesting value use case which are really, really hard for enterprise to program hand by hand by developers and they would love to have out of the box and that's yet the innovation to come to, we have to come to see. But the first step to get there is that your software runs in multiple clouds and that's what Oracle's doing so well with my SQL >>Guys. Amazing. >>Go ahead. Yeah. >>Yeah. >>For example, >>Amazing amount of data knowledge and, and brain power in this market. Guys, I really want to thank you for coming on to the cube. Ron Holger. Mark, always a pleasure to have you on. Really appreciate your time. >>Well all the last names we're very happy for Romanic last and moderator. Thanks Dave for moderating us. All right, >>We'll see. We'll see you guys around. Safe travels to all and thank you for watching this power panel, The Truth About My SQL Heat Wave on the cube. Your leader in enterprise and emerging tech coverage.

>>This past May, The Cube in collaboration with Influx data shared with you the latest innovations in Time series databases. We talked at length about why a purpose built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember the time series data is any data that's stamped in time, and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community, we talked about how in theory, those time slices could be taken, you know, every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors and other devices and IOT equipment. A time series databases have had to evolve to efficiently support realtime data in emerging use cases in iot T and other use cases. >>And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the smart Data platform, made possible by influx data and produced by the Cube. My name is Dave Valante and I'll be your host today. Now in this program we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're gonna hear from Brian Gilmore, who is the director of IOT and emerging technologies at Influx Data. And we're gonna talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program you're gonna hear a lot about things like Rust, implementation of Apache Arrow, the use of par k and tooling such as data fusion, which powering a new engine for Influx db. >>Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices, if you will, from, for example, minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're gonna hear from Anna East Dos Georgio, who is a developer advocate at In Flux Data. And we're gonna get into the why of these open source capabilities and how they contribute to the evolution of the Influx DB platform. And then we're gonna close the program with Tim Yokum, he's the director of engineering at Influx Data, and he's gonna explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started. Okay, we're kicking things off with Brian Gilmore. He's the director of i t and emerging Technology at Influx State of Bryan. Welcome to the program. Thanks for coming on. >>Thanks Dave. Great to be here. I appreciate the time. >>Hey, explain why Influx db, you know, needs a new engine. Was there something wrong with the current engine? What's going on there? >>No, no, not at all. I mean, I think it's, for us, it's been about staying ahead of the market. I think, you know, if we think about what our customers are coming to us sort of with now, you know, related to requests like sql, you know, query support, things like that, we have to figure out a way to, to execute those for them in a way that will scale long term. And then we also, we wanna make sure we're innovating, we're sort of staying ahead of the market as well and sort of anticipating those future needs. So, you know, this is really a, a transparent change for our customers. I mean, I think we'll be adding new capabilities over time that sort of leverage this new engine, but you know, initially the customers who are using us are gonna see just great improvements in performance, you know, especially those that are working at the top end of the, of the workload scale, you know, the massive data volumes and things like that. >>Yeah, and we're gonna get into that today and the architecture and the like, but what was the catalyst for the enhancements? I mean, when and how did this all come about? >>Well, I mean, like three years ago we were primarily on premises, right? I mean, I think we had our open source, we had an enterprise product, you know, and, and sort of shifting that technology, especially the open source code base to a service basis where we were hosting it through, you know, multiple cloud providers. That was, that was, that was a long journey I guess, you know, phase one was, you know, we wanted to host enterprise for our customers, so we sort of created a service that we just managed and ran our enterprise product for them. You know, phase two of this cloud effort was to, to optimize for like multi-tenant, multi-cloud, be able to, to host it in a truly like sass manner where we could use, you know, some type of customer activity or consumption as the, the pricing vector, you know, And, and that was sort of the birth of the, of the real first influx DB cloud, you know, which has been really successful. >>We've seen, I think like 60,000 people sign up and we've got tons and tons of, of both enterprises as well as like new companies, developers, and of course a lot of home hobbyists and enthusiasts who are using out on a, on a daily basis, you know, and having that sort of big pool of, of very diverse and very customers to chat with as they're using the product, as they're giving us feedback, et cetera, has has, you know, pointed us in a really good direction in terms of making sure we're continuously improving that and then also making these big leaps as we're doing with this, with this new engine. >>Right. So you've called it a transparent change for customers, so I'm presuming it's non-disruptive, but I really wanna understand how much of a pivot this is and what, what does it take to make that shift from, you know, time series, you know, specialist to real time analytics and being able to support both? >>Yeah, I mean, it's much more of an evolution, I think, than like a shift or a pivot. You know, time series data is always gonna be fundamental and sort of the basis of the solutions that we offer our customers, and then also the ones that they're building on the sort of raw APIs of our platform themselves. You know, the time series market is one that we've worked diligently to lead. I mean, I think when it comes to like metrics, especially like sensor data and app and infrastructure metrics, if we're being honest though, I think our, our user base is well aware that the way we were architected was much more towards those sort of like backwards looking historical type analytics, which are key for troubleshooting and making sure you don't, you know, run into the same problem twice. But, you know, we had to ask ourselves like, what can we do to like better handle those queries from a performance and a, and a, you know, a time to response on the queries, and can we get that to the point where the results sets are coming back so quickly from the time of query that we can like limit that window down to minutes and then seconds. >>And now with this new engine, we're really starting to talk about a query window that could be like returning results in, in, you know, milliseconds of time since it hit the, the, the ingest queue. And that's, that's really getting to the point where as your data is available, you can use it and you can query it, you can visualize it, and you can do all those sort of magical things with it, you know? And I think getting all of that to a place where we're saying like, yes to the customer on, you know, all of the, the real time queries, the, the multiple language query support, but, you know, it was hard, but we're now at a spot where we can start introducing that to, you know, a a limited number of customers, strategic customers and strategic availability zones to start. But you know, everybody over time. >>So you're basically going from what happened to in, you can still do that obviously, but to what's happening now in the moment? >>Yeah, yeah. I mean if you think about time, it's always sort of past, right? I mean, like in the moment right now, whether you're talking about like a millisecond ago or a minute ago, you know, that's, that's pretty much right now, I think for most people, especially in these use cases where you have other sort of components of latency induced by the, by the underlying data collection, the architecture, the infrastructure, the, you know, the, the devices and you know, the sort of highly distributed nature of all of this. So yeah, I mean, getting, getting a customer or a user to be able to use the data as soon as it is available is what we're after here. >>I always thought, you know, real, I always thought of real time as before you lose the customer, but now in this context, maybe it's before the machine blows up. >>Yeah, it's, it's, I mean it is operationally or operational real time is different, you know, and that's one of the things that really triggered us to know that we were, we were heading in the right direction, is just how many sort of operational customers we have. You know, everything from like aerospace and defense. We've got companies monitoring satellites, we've got tons of industrial users, users using us as a processes storing on the plant floor, you know, and, and if we can satisfy their sort of demands for like real time historical perspective, that's awesome. I think what we're gonna do here is we're gonna start to like edge into the real time that they're used to in terms of, you know, the millisecond response times that they expect of their control systems, certainly not their, their historians and databases. >>I, is this available, these innovations to influx DB cloud customers only who can access this capability? >>Yeah. I mean commercially and today, yes. You know, I think we want to emphasize that's a, for now our goal is to get our latest and greatest and our best to everybody over time. Of course. You know, one of the things we had to do here was like we double down on sort of our, our commitment to open source and availability. So like anybody today can take a look at the, the libraries in on our GitHub and, you know, can ex inspect it and even can try to, you know, implement or execute some of it themselves in their own infrastructure. You know, we are, we're committed to bringing our sort of latest and greatest to our cloud customers first for a couple of reasons. Number one, you know, there are big workloads and they have high expectations of us. I think number two, it also gives us the opportunity to monitor a little bit more closely how it's working, how they're using it, like how the system itself is performing. >>And so just, you know, being careful, maybe a little cautious in terms of, of, of how big we go with this right away, just sort of both limits, you know, the risk of, of, you know, any issues that can come with new software rollouts. We haven't seen anything so far, but also it does give us the opportunity to have like meaningful conversations with a small group of users who are using the products, but once we get through that and they give us two thumbs up on it, it'll be like, open the gates and let everybody in. It's gonna be exciting time for the whole ecosystem. >>Yeah, that makes a lot of sense. And you can do some experimentation and, you know, using the cloud resources. Let's dig into some of the architectural and technical innovations that are gonna help deliver on this vision. What, what should we know there? >>Well, I mean, I think foundationally we built the, the new core on Rust. You know, this is a new very sort of popular systems language, you know, it's extremely efficient, but it's also built for speed and memory safety, which goes back to that us being able to like deliver it in a way that is, you know, something we can inspect very closely, but then also rely on the fact that it's going to behave well. And if it does find error conditions, I mean we, we've loved working with Go and, you know, a lot of our libraries will continue to, to be sort of implemented in Go, but you know, when it came to this particular new engine, you know, that power performance and stability rust was critical. On top of that, like, we've also integrated Apache Arrow and Apache Parque for persistence. I think for anybody who's really familiar with the nuts and bolts of our backend and our TSI and our, our time series merged Trees, this is a big break from that, you know, arrow on the sort of in MI side and then Par K in the on disk side. >>It, it allows us to, to present, you know, a unified set of APIs for those really fast real time inquiries that we talked about, as well as for very large, you know, historical sort of bulk data archives in that PARQUE format, which is also cool because there's an entire ecosystem sort of popping up around Parque in terms of the machine learning community, you know, and getting that all to work, we had to glue it together with aero flight. That's sort of what we're using as our, our RPC component. You know, it handles the orchestration and the, the transportation of the Coer data. Now we're moving to like a true Coer database model for this, this version of the engine, you know, and it removes a lot of overhead for us in terms of having to manage all that serialization, the deserialization, and, you know, to that again, like blurring that line between real time and historical data. It's, you know, it's, it's highly optimized for both streaming micro batch and then batches, but true streaming as well. >>Yeah. Again, I mean, it's funny you mentioned Rust. It is, it's been around for a long time, but it's popularity is, is you know, really starting to hit that steep part of the S-curve. And, and we're gonna dig into to more of that, but give us any, is there anything else that we should know about Bryan? Give us the last word? >>Well, I mean, I think first I'd like everybody sort of watching just to like take a look at what we're offering in terms of early access in beta programs. I mean, if, if, if you wanna participate or if you wanna work sort of in terms of early access with the, with the new engine, please reach out to the team. I'm sure you know, there's a lot of communications going out and you know, it'll be highly featured on our, our website, you know, but reach out to the team, believe it or not, like we have a lot more going on than just the new engine. And so there are also other programs, things we're, we're offering to customers in terms of the user interface, data collection and things like that. And, you know, if you're a customer of ours and you have a sales team, a commercial team that you work with, you can reach out to them and see what you can get access to because we can flip a lot of stuff on, especially in cloud through feature flags. >>But if there's something new that you wanna try out, we'd just love to hear from you. And then, you know, our goal would be that as we give you access to all of these new cool features that, you know, you would give us continuous feedback on these products and services, not only like what you need today, but then what you'll need tomorrow to, to sort of build the next versions of your business. Because you know, the whole database, the ecosystem as it expands out into to, you know, this vertically oriented stack of cloud services and enterprise databases and edge databases, you know, it's gonna be what we all make it together, not just, you know, those of us who were employed by Influx db. And then finally I would just say please, like watch in ICE in Tim's sessions, like these are two of our best and brightest, They're totally brilliant, completely pragmatic, and they are most of all customer obsessed, which is amazing. And there's no better takes, like honestly on the, the sort of technical details of this, then there's, especially when it comes to like the value that these investments will, will bring to our customers and our communities. So encourage you to, to, you know, pay more attention to them than you did to me, for sure. >>Brian Gilmore, great stuff. Really appreciate your time. Thank you. >>Yeah, thanks Dave. It was awesome. Look forward to it. >>Yeah, me too. Looking forward to see how the, the community actually applies these new innovations and goes, goes beyond just the historical into the real time really hot area. As Brian said in a moment, I'll be right back with Anna East dos Georgio to dig into the critical aspects of key open source components of the Influx DB engine, including Rust, Arrow, Parque, data fusion. Keep it right there. You don't wanna miss this >>Time series Data is everywhere. The number of sensors, systems and applications generating time series data increases every day. All these data sources producing so much data can cause analysis paralysis. Influx DB is an entire platform designed with everything you need to quickly build applications that generate value from time series data influx. DB Cloud is a serverless solution, which means you don't need to buy or manage your own servers. There's no need to worry about provisioning because you only pay for what you use. Influx DB Cloud is fully managed so you get the newest features and enhancements as they're added to the platform's code base. It also means you can spend time building solutions and delivering value to your users instead of wasting time and effort managing something else. Influx TVB Cloud offers a range of security features to protect your data, multiple layers of redundancy ensure you don't lose any data access controls ensure that only the people who should see your data can see it. >>And encryption protects your data at rest and in transit between any of our regions or cloud providers. InfluxDB uses a single API across the entire platform suite so you can build on open source, deploy to the cloud and then then easily query data in the cloud at the edge or on prem using the same scripts. And InfluxDB is schemaless automatically adjusting to changes in the shape of your data without requiring changes in your application. Logic. InfluxDB Cloud is production ready from day one. All it needs is your data and your imagination. Get started today@influxdata.com slash cloud. >>Okay, we're back. I'm Dave Valante with a Cube and you're watching evolving Influx DB into the smart data platform made possible by influx data. Anna ETOs Georgio is here, she's a developer advocate for influx data and we're gonna dig into the rationale and value contribution behind several open source technologies that Influx DB is leveraging to increase the granularity of time series analysis analysis and bring the world of data into real-time analytics and is welcome to the program. Thanks for coming on. >>Hi, thank you so much. It's a pleasure to be here. >>Oh, you're very welcome. Okay, so IX is being touted as this next gen open source core for Influx db. And my understanding is that it leverages in memory of course for speed. It's a kilo store, so it gives you a compression efficiency, it's gonna give you faster query speeds, you store files and object storage, so you got very cost effective approach. Are these the salient points on the platform? I know there are probably dozens of other features, but what are the high level value points that people should understand? >>Sure, that's a great question. So some of the main requirements that IOx is trying to achieve and some of the most impressive ones to me, the first one is that it aims to have no limits on cardinality and also allow you to write any kind of event data that you want, whether that's live tag or a field. It also wants to deliver the best in class performance on analytics queries. In addition to our already well served metrics queries, we also wanna have operator control over memory usage. So you should be able to define how much memory is used for buffering caching and query processing. Some other really important parts is the ability to have bulk data export and import super useful. Also broader ecosystem compatibility where possible we aim to use and embrace emerging standards in the data analytics ecosystem and have compatibility with things like sql, Python, and maybe even pandas in the future. >>Okay, so lot there. Now we talked to Brian about how you're using Rust and which is not a new programming language and of course we had some drama around Rust during the pandemic with the Mozilla layoffs, but the formation of the Rust Foundation really addressed any of those concerns. You got big guns like Amazon and Google and Microsoft throwing their collective weights behind it. It's really, the adoption is really starting to get steep on the S-curve. So lots of platforms, lots of adoption with rust, but why rust as an alternative to say c plus plus for example? >>Sure, that's a great question. So Russ was chosen because of his exceptional performance and reliability. So while Russ is synt tactically similar to c plus plus and it has similar performance, it also compiles to a native code like c plus plus. But unlike c plus plus, it also has much better memory safety. So memory safety is protection against bugs or security vulnerabilities that lead to excessive memory usage or memory leaks. And rust achieves this memory safety due to its like innovative type system. Additionally, it doesn't allow for dangling pointers. And dangling pointers are the main classes of errors that lead to exploitable security vulnerabilities in languages like c plus plus. So Russ like helps meet that requirement of having no limits on ality, for example, because it's, we're also using the Russ implementation of Apache Arrow and this control over memory and also Russ Russ's packaging system called crates IO offers everything that you need out of the box to have features like AY and a weight to fix race conditions, to protection against buffering overflows and to ensure thread safe async cashing structures as well. So essentially it's just like has all the control, all the fine grain control, you need to take advantage of memory and all your resources as well as possible so that you can handle those really, really high ity use cases. >>Yeah, and the more I learn about the, the new engine and, and the platform IOCs et cetera, you know, you, you see things like, you know, the old days not even to even today you do a lot of garbage collection in these, in these systems and there's an inverse, you know, impact relative to performance. So it looks like you really, you know, the community is modernizing the platform, but I wanna talk about Apache Arrow for a moment. It it's designed to address the constraints that are associated with analyzing large data sets. We, we know that, but please explain why, what, what is Arrow and and what does it bring to Influx db? >>Sure, yeah. So Arrow is a, a framework for defining in memory calmer data. And so much of the efficiency and performance of IOx comes from taking advantage of calmer data structures. And I will, if you don't mind, take a moment to kind of of illustrate why column or data structures are so valuable. Let's pretend that we are gathering field data about the temperature in our room and also maybe the temperature of our stove. And in our table we have those two temperature values as well as maybe a measurement value, timestamp value, maybe some other tag values that describe what room and what house, et cetera we're getting this data from. And so you can picture this table where we have like two rows with the two temperature values for both our room and the stove. Well usually our room temperature is regulated so those values don't change very often. >>So when you have calm oriented st calm oriented storage, essentially you take each row, each column and group it together. And so if that's the case and you're just taking temperature values from the room and a lot of those temperature values are the same, then you'll, you might be able to imagine how equal values will then enable each other and when they neighbor each other in the storage format, this provides a really perfect opportunity for cheap compression. And then this cheap compression enables high cardinality use cases. It also enables for faster scan rates. So if you wanna define like the men and max value of the temperature in the room across a thousand different points, you only have to get those a thousand different points in order to answer that question and you have those immediately available to you. But let's contrast this with a row oriented storage solution instead so that we can understand better the benefits of calmer oriented storage. >>So if you had a row oriented storage, you'd first have to look at every field like the temperature in, in the room and the temperature of the stove. You'd have to go across every tag value that maybe describes where the room is located or what model the stove is. And every timestamp you'd then have to pluck out that one temperature value that you want at that one time stamp and do that for every single row. So you're scanning across a ton more data and that's why Rowe Oriented doesn't provide the same efficiency as calmer and Apache Arrow is in memory calmer data, commoner data fit framework. So that's where a lot of the advantages come >>From. Okay. So you basically described like a traditional database, a row approach, but I've seen like a lot of traditional database say, okay, now we've got, we can handle colo format versus what you're talking about is really, you know, kind of native i, is it not as effective? Is the, is the foreman not as effective because it's largely a, a bolt on? Can you, can you like elucidate on that front? >>Yeah, it's, it's not as effective because you have more expensive compression and because you can't scan across the values as quickly. And so those are, that's pretty much the main reasons why, why RO row oriented storage isn't as efficient as calm, calmer oriented storage. Yeah. >>Got it. So let's talk about Arrow Data Fusion. What is data fusion? I know it's written in Rust, but what does it bring to the table here? >>Sure. So it's an extensible query execution framework and it uses Arrow as it's in memory format. So the way that it helps in influx DB IOCs is that okay, it's great if you can write unlimited amount of cardinality into influx Cbis, but if you don't have a query engine that can successfully query that data, then I don't know how much value it is for you. So Data fusion helps enable the, the query process and transformation of that data. It also has a PANDAS API so that you could take advantage of PANDAS data frames as well and all of the machine learning tools associated with Pandas. >>Okay. You're also leveraging Par K in the platform cause we heard a lot about Par K in the middle of the last decade cuz as a storage format to improve on Hadoop column stores. What are you doing with Parque and why is it important? >>Sure. So parque is the column oriented durable file format. So it's important because it'll enable bulk import, bulk export, it has compatibility with Python and Pandas, so it supports a broader ecosystem. Par K files also take very little disc disc space and they're faster to scan because again, they're column oriented in particular, I think PAR K files are like 16 times cheaper than CSV files, just as kind of a point of reference. And so that's essentially a lot of the, the benefits of par k. >>Got it. Very popular. So and he's, what exactly is influx data focusing on as a committer to these projects? What is your focus? What's the value that you're bringing to the community? >>Sure. So Influx DB first has contributed a lot of different, different things to the Apache ecosystem. For example, they contribute an implementation of Apache Arrow and go and that will support clearing with flux. Also, there has been a quite a few contributions to data fusion for things like memory optimization and supportive additional SQL features like support for timestamp, arithmetic and support for exist clauses and support for memory control. So yeah, Influx has contributed a a lot to the Apache ecosystem and continues to do so. And I think kind of the idea here is that if you can improve these upstream projects and then the long term strategy here is that the more you contribute and build those up, then the more you will perpetuate that cycle of improvement and the more we will invest in our own project as well. So it's just that kind of symbiotic relationship and appreciation of the open source community. >>Yeah. Got it. You got that virtuous cycle going, the people call the flywheel. Give us your last thoughts and kind of summarize, you know, where what, what the big takeaways are from your perspective. >>So I think the big takeaway is that influx data is doing a lot of really exciting things with Influx DB IOx and I really encourage, if you are interested in learning more about the technologies that Influx is leveraging to produce IOCs, the challenges associated with it and all of the hard work questions and you just wanna learn more, then I would encourage you to go to the monthly Tech talks and community office hours and they are on every second Wednesday of the month at 8:30 AM Pacific time. There's also a community forums and a community Slack channel look for the influx DDB unders IAC channel specifically to learn more about how to join those office hours and those monthly tech tech talks as well as ask any questions they have about iacs, what to expect and what you'd like to learn more about. I as a developer advocate, I wanna answer your questions. So if there's a particular technology or stack that you wanna dive deeper into and want more explanation about how INFLUX DB leverages it to build IOCs, I will be really excited to produce content on that topic for you. >>Yeah, that's awesome. You guys have a really rich community, collaborate with your peers, solve problems, and, and you guys super responsive, so really appreciate that. All right, thank you so much Anise for explaining all this open source stuff to the audience and why it's important to the future of data. >>Thank you. I really appreciate it. >>All right, you're very welcome. Okay, stay right there and in a moment I'll be back with Tim Yoakum, he's the director of engineering for Influx Data and we're gonna talk about how you update a SAS engine while the plane is flying at 30,000 feet. You don't wanna miss this. >>I'm really glad that we went with InfluxDB Cloud for our hosting because it has saved us a ton of time. It's helped us move faster, it's saved us money. And also InfluxDB has good support. My name's Alex Nada. I am CTO at Noble nine. Noble Nine is a platform to measure and manage service level objectives, which is a great way of measuring the reliability of your systems. You can essentially think of an slo, the product we're providing to our customers as a bunch of time series. So we need a way to store that data and the corresponding time series that are related to those. The main reason that we settled on InfluxDB as we were shopping around is that InfluxDB has a very flexible query language and as a general purpose time series database, it basically had the set of features we were looking for. >>As our platform has grown, we found InfluxDB Cloud to be a really scalable solution. We can quickly iterate on new features and functionality because Influx Cloud is entirely managed, it probably saved us at least a full additional person on our team. We also have the option of running InfluxDB Enterprise, which gives us the ability to even host off the cloud or in a private cloud if that's preferred by a customer. Influx data has been really flexible in adapting to the hosting requirements that we have. They listened to the challenges we were facing and they helped us solve it. As we've continued to grow, I'm really happy we have influx data by our side. >>Okay, we're back with Tim Yokum, who is the director of engineering at Influx Data. Tim, welcome. Good to see you. >>Good to see you. Thanks for having me. >>You're really welcome. Listen, we've been covering open source software in the cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem. The cloud has been being built out on open source, mobile, social platforms, key databases, and of course influx DB and influx data has been a big consumer and contributor of open source software. So my question to you is, where have you seen the biggest bang for the buck from open source software? >>So yeah, you know, influx really, we thrive at the intersection of commercial services and open, so open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service from our core storage engine technologies to web services temping engines. Our, our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants and like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product influx db. >>You know, but I gotta ask you, Tim, because one of the challenge that that we've seen in particular, you saw this in the heyday of Hadoop, the, the innovations come so fast and furious and as a software company you gotta place bets, you gotta, you know, commit people and sometimes those bets can be risky and not pay off well, how have you managed this challenge? >>Oh, it moves fast. Yeah, that, that's a benefit though because it, the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we, what we tend to do is, is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example, that ecosystem is driven by thousands of intelligent developers, engineers, builders, they're adding value every day. So we have to really keep up with that. And as the stack changes, we, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's, it's something that we just do every day. >>So we have a survey partner down in New York City called Enterprise Technology Research etr, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity particularly, you know, along with cloud. But, but really Kubernetes is just, you know, still up until the right consistently even with, you know, the macro headwinds and all, all of the stuff that we're sick of talking about. But, so what are you doing with Kubernetes in the platform? >>Yeah, it, it's really central to our ability to run the product. When we first started out, we were just on AWS and, and the way we were running was, was a little bit like containers junior. Now we're running Kubernetes everywhere at aws, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers and we can manage that in code so our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google and figure out how to deliver services on those three clouds with all of their differences. >>Just to follow up on that, is it, no. So I presume it's sounds like there's a PAs layer there to allow you guys to have a consistent experience across clouds and out to the edge, you know, wherever is that, is that correct? >>Yeah, so we've basically built more or less platform engineering, This is the new hot phrase, you know, it, it's, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on and they only have to learn one way of deploying their application, managing their application. And so that, that just gets all of the underlying infrastructure out of the way and, and lets them focus on delivering influx cloud. >>Yeah, and I know I'm taking a little bit of a tangent, but is that, that, I'll call it a PAs layer if I can use that term. Is that, are there specific attributes to Influx db or is it kind of just generally off the shelf paths? You know, are there, is, is there any purpose built capability there that, that is, is value add or is it pretty much generic? >>So we really build, we, we look at things through, with a build versus buy through a, a build versus by lens. Some things we want to leverage cloud provider services, for instance, Postgres databases for metadata, perhaps we'll get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can, can deliver on that has consistency that is, is all generated from code that we can as a, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions and in no time. >>So how, so sometimes you build, sometimes you buy it. How do you make those decisions and and what does that mean for the, for the platform and for customers? >>Yeah, so what we're doing is, it's like everybody else will do, we're we're looking for trade offs that make sense. You know, we really want to protect our customers data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers you don't even see that, but we don't want to try to reinvent the wheel, like I had mentioned with SQL data stores for metadata, perhaps let's build on top of what of these three large cloud providers have already perfected. And we can then focus on our platform engineering and we can have our developers then focus on the influx data, software, influx, cloud software. >>So take it to the customer level, what does it mean for them? What's the value that they're gonna get out of all these innovations that we've been been talking about today and what can they expect in the future? >>So first of all, people who use the OSS product are really gonna be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you, but then you want to scale up. We have some 270 terabytes of data across, over 4 billion series keys that people have stored. So there's a proven ability to scale now in terms of the open source, open source software and how we've developed the platform. You're getting highly available high cardinality time series platform. We manage it and, and really as, as I mentioned earlier, we can keep up with the state of the art. We keep reinventing, we keep deploying things in real time. We deploy to our platform every day repeatedly all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change new features, better ways of doing deployments, safer ways of doing deployments. >>All of that happens behind the scenes. And like we had mentioned earlier, Kubernetes, I mean that, that allows us to get that done. We couldn't do it without having that platform as a, as a base layer for us to then put our software on. So we, we iterate quickly. When you're on the, the Influx cloud platform, you really are able to, to take advantage of new features immediately. We roll things out every day and as those things go into production, you have, you have the ability to, to use them. And so in the end we want you to focus on getting actual insights from your data instead of running infrastructure, you know, let, let us do that for you. So, >>And that makes sense, but so is the, is the, are the innovations that we're talking about in the evolution of Influx db, do, do you see that as sort of a natural evolution for existing customers? I, is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >>Yeah, it really is it, it's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are, are really the hot thing. Iot, industrial iot especially, people want to just shove tons of data out there and be able to do queries immediately and they don't wanna manage infrastructure. What we've started to see are people that use the cloud service as their, their data store backbone and then they use edge computing with R OSS product to ingest data from say, multiple production lines and downsample that data, send the rest of that data off influx cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that and being in all sorts of different regions allows for people to really get out of the, the business of man trying to manage that big data, have us take care of that. And of course as we change the platform end users benefit from that immediately. And, >>And so obviously taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IOT and the Edge? How should we be thinking about the value that you bring from a security perspective? >>Yeah, we take, we take security super seriously. It, it's built into our dna. We do a lot of work to ensure that our platform is secure, that the data we store is, is kept private. It's of course always a concern. You see in the news all the time, companies being compromised, you know, that's something that you can have an entire team working on, which we do to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You know, you look at things like software, bill of materials, if you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that, that's just part of our jobs to make sure that the platform that we're running it has, has fully vetted software and, and with open source especially, that's a lot of work. And so it's, it's definitely new territory. Supply chain attacks are, are definitely happening at a higher clip than they used to, but that is, that is really just part of a day in the, the life for folks like us that are, are building platforms. >>Yeah, and that's key. I mean especially when you start getting into the, the, you know, we talk about IOT and the operations technologies, the engineers running the, that infrastructure, you know, historically, as you know, Tim, they, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >>That >>Connected now, right? And so you've gotta have a partner that is again, take away that heavy lifting to r and d so you can focus on some of the other activities. Right. Give us the, the last word and the, the key takeaways from your perspective. >>Well, you know, from my perspective I see it as, as a a two lane approach with, with influx, with Anytime series data, you know, you've got a lot of stuff that you're gonna run on-prem, what you had mentioned, air gaping. Sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want torus their data to, to a company that's, that's got a full platform set up for them that they can build on, send that data over to the cloud, the cloud is not going away. I think more hybrid approach is, is where the future lives and that's what we're prepared for. >>Tim, really appreciate you coming to the program. Great stuff. Good to see you. >>Thanks very much. Appreciate it. >>Okay, in a moment I'll be back to wrap up. Today's session, you're watching The Cube. >>Are you looking for some help getting started with InfluxDB Telegraph or Flux Check >>Out Influx DB University >>Where you can find our entire catalog of free training that will help you make the most of your time series data >>Get >>Started for free@influxdbu.com. >>We'll see you in class. >>Okay, so we heard today from three experts on time series and data, how the Influx DB platform is evolving to support new ways of analyzing large data sets very efficiently and effectively in real time. And we learned that key open source components like Apache Arrow and the Rust Programming environment Data fusion par K are being leveraged to support realtime data analytics at scale. We also learned about the contributions in importance of open source software and how the Influx DB community is evolving the platform with minimal disruption to support new workloads, new use cases, and the future of realtime data analytics. Now remember these sessions, they're all available on demand. You can go to the cube.net to find those. Don't forget to check out silicon angle.com for all the news related to things enterprise and emerging tech. And you should also check out influx data.com. There you can learn about the company's products. You'll find developer resources like free courses. You could join the developer community and work with your peers to learn and solve problems. And there are plenty of other resources around use cases and customer stories on the website. This is Dave Valante. Thank you for watching Evolving Influx DB into the smart data platform, made possible by influx data and brought to you by the Cube, your leader in enterprise and emerging tech coverage.

>> This past May, the Cube, in collaboration with Influx Data shared with you the latest innovations in Time series databases. We talked at length about why a purpose-built time series database for many use cases, was a superior alternative to general purpose databases trying to do the same thing. Now, you may, you may remember that time series data is any data that's stamped in time and if it's stamped, it can be analyzed historically. And when we introduced the concept to the community we talked about how in theory those time slices could be taken, you know every hour, every minute, every second, you know, down to the millisecond and how the world was moving toward realtime or near realtime data analysis to support physical infrastructure like sensors, and other devices and IOT equipment. Time series databases have had to evolve to efficiently support realtime data in emerging use, use cases in IOT and other use cases. And to do that, new architectural innovations have to be brought to bear. As is often the case, open source software is the linchpin to those innovations. Hello and welcome to Evolving Influx DB into the Smart Data platform, made possible by influx data and produced by the cube. My name is Dave Vellante, and I'll be your host today. Now, in this program, we're going to dig pretty deep into what's happening with Time series data generally, and specifically how Influx DB is evolving to support new workloads and demands and data, and specifically around data analytics use cases in real time. Now, first we're going to hear from Brian Gilmore who is the director of IOT and emerging technologies at Influx Data. And we're going to talk about the continued evolution of Influx DB and the new capabilities enabled by open source generally and specific tools. And in this program, you're going to hear a lot about things like rust implementation of Apache Arrow, the use of Parquet and tooling such as data fusion, which are powering a new engine for Influx db. Now, these innovations, they evolve the idea of time series analysis by dramatically increasing the granularity of time series data by compressing the historical time slices if you will, from, for example minutes down to milliseconds. And at the same time, enabling real time analytics with an architecture that can process data much faster and much more efficiently. Now, after Brian, we're going to hear from Anais Dotis-Georgiou who is a developer advocate at Influx Data. And we're going to get into the "why's" of these open source capabilities, and how they contribute to the evolution of the Influx DB platform. And then we're going to close the program with Tim Yocum. He's the director of engineering at Influx Data, and he's going to explain how the Influx DB community actually evolved the data engine in mid-flight and which decisions went into the innovations that are coming to the market. Thank you for being here. We hope you enjoy the program. Let's get started.

(upbeat music) >> Okay, we're back with Tim Yoakum, who is the Director of Engineering at Influx Data. Tim, welcome. Good to see you. >> Good to see you. Thanks for having me. >> You're really welcome. Listen, we've been covering open source software on the Cube for more than a decade, and we've kind of watched the innovation from the big data ecosystem, the cloud is being built out on open source, mobile social platforms, key databases, and of course Influx DB, and Influx Data has been a big consumer and contributor of open source software. So my question to you is where have you seen the biggest bang for the buck from open source software? >> So, yeah, you know, Influx, really, we thrive at the intersection of commercial services and open source software. So OSS keeps us on the cutting edge. We benefit from OSS in delivering our own service, from our core storage engine technologies to web services, templating engines. Our team stays lean and focused because we build on proven tools. We really build on the shoulders of giants. And like you've mentioned, even better, we contribute a lot back to the projects that we use as well as our own product, Influx DB. >> You know, but I got to ask you, Tim, because one of the challenge that we've seen, in particular, you saw this in the heyday of Hadoop. The innovations come so fast and furious, and as a software company, you got to place bets, you got to, you know, commit people, and sometimes those bets can be risky and not pay off. How have you managed this challenge? >> Oh, it moves fast, yeah. That's a benefit though, because the community moves so quickly that today's hot technology can be tomorrow's dinosaur. And what we tend to do is we fail fast and fail often. We try a lot of things. You know, you look at Kubernetes for example. That ecosystem is driven by thousands of intelligent developers, engineers, builders. They're adding value every day. So we have to really keep up with that. And as the stack changes, we try different technologies, we try different methods, and at the end of the day, we come up with a better platform as a result of just the constant change in the environment. It is a challenge for us, but it's something that we just do every day. >> So we have a survey partner down in New York City called Enterprise Technology Research, ETR, and they do these quarterly surveys of about 1500 CIOs, IT practitioners, and they really have a good pulse on what's happening with spending. And the data shows that containers generally, but specifically Kubernetes, is one of the areas that has kind of, it's been off the charts and seen the most significant adoption and velocity, particularly, you know, along with cloud. But really Kubernetes is just, you know, still up and to the right consistently, even with, you know the macro headwinds and all of the other stuff that we're sick of talking about. So what are you doing with Kubernetes in the platform? >> Yeah, it's really central to our ability to run the product. When we first started out, we were just on AWS, and the way we were running was a little bit like containers junior. Now we're running Kubernetes everywhere, at AWS, Azure, Google Cloud. It allows us to have a consistent experience across three different cloud providers, and we can manage that in code. So our developers can focus on delivering services, not trying to learn the intricacies of Amazon, Azure, and Google, and figure out how to deliver services on those three clouds with all of their differences. >> Just a follow up on that, is it, now, so I presume it sounds like there's a PaaS layer there to allow you guys to have a consistent experience across clouds and up to the edge, you know, wherever. Is that, is that correct? >> Yeah, so we've basically built, more or less, platform engineering. This is the new hot phrase. You know, Kubernetes has made a lot of things easy for us because we've built a platform that our developers can lean on, and they only have to learn one way of deploying their application, managing their application. And so that just gets all of the underlying infrastructure out of the way and lets them focus on delivering Influx Cloud. >> Yeah, and I know I'm taking a little bit of a tangent, but is that, I'll call it a PaaS layer if I can use that term, are there specific attributes to Influx DB, or is it kind of just generally off the shelf PaaS? You know, is there any purpose built capability there that is value add, or is it pretty much generic? >> So we really build, we look at things with a build versus buy, through a build versus buy lens. Some things we want to leverage, cloud provider services for instance, Postgres databases for metadata perhaps, get that off of our plate, let someone else run that. We're going to deploy a platform that our engineers can deliver on, that has consistency, that is all generated from code that we can, as an SRE group, as an ops team, that we can manage with very few people really, and we can stamp out clusters across multiple regions in no time. >> So how, so sometimes you build, sometimes you buy it. How do you make those decisions, and what does that mean for the platform and for customers? >> Yeah, so what we're doing is, it's like everybody else will do. We're looking for trade offs that make sense. You know, we really want to protect our customers' data. So we look for services that support our own software with the most uptime, reliability, and durability we can get. Some things are just going to be easier to have a cloud provider take care of on our behalf. We make that transparent for our own team. And of course for customers, you don't even see that, but we don't want to try to reinvent the wheel. Like I had had mentioned with SQL data storage for metadata perhaps. Let's build on top of what these three large cloud providers have already perfected, and we can then focus on our platform engineering, and we can have our developers then focus on the Influx Data software, Influx Cloud software. >> So take it to the customer level. What does it mean for them? What's the value that they're going to get out of all these innovations that we've been been talking about today? And what can they expect in the future? >> So first of all, people who use the OSS product are really going to be at home on our cloud platform. You can run it on your desktop machine, on a single server, what have you. But then you want to scale up. We have some 270 terabytes of data across over 4 billion series keys that people have stored. So there's a proven ability to scale. Now, in terms of the open source software, and how we've developed the platform, you're getting highly available, high cardinality time series platform. We manage it, and really as I mentioned earlier, we can keep up with the state of the art. We keep reinventing. We keep deploying things in real time. We deploy to our platform every day repeatedly, all the time. And it's that continuous deployment that allows us to continue testing things in flight, rolling things out that change, new features, better ways of doing deployments, safer ways of doing deployments. All of that happens behind the scenes. And we had mentioned earlier Kubernetes, I mean that allows us to get that done. We couldn't do it without having that platform as a base layer for us to then put our software on. So we iterate quickly. When you're on the Influx Cloud platform, you really are able to take advantage of new features immediately. We roll things out every day. And as those things go into production, you have the ability to use them. And so in the end, we want you to focus on getting actionable insights from your data instead of running infrastructure. You know, let us do that for you. >> And that makes sense, but so is the, are the innovations that we're talking about in the evolution of Influx DB, do you see that as sort of a natural evolution for existing customers? Is it, I'm sure the answer is both, but is it opening up new territory for customers? Can you add some color to that? >> Yeah, it really is. It's a little bit of both. Any engineer will say, well, it depends. So cloud native technologies are really the hot thing. IoT, industrial IoT especially, people want to just shove tons of data out there and be able to do queries immediately, and they don't want to manage infrastructure. What we've started to see are people that use the cloud service as their data store backbone, and then they use edge computing with our OSS product to ingest data from say multiple production lines and down-sample that data, send the rest of that data off to Influx Cloud where the heavy processing takes place. So really us being in all the different clouds and iterating on that, and being in all sorts of different regions allows for people to really get out of the business of trying to manage that big data, have us take care of that. And of course, as we change the platform, end users benefit from that immediately. >> And so obviously, taking away a lot of the heavy lifting for the infrastructure, would you say the same thing about security, especially as you go out to IoT and the edge? How should we be thinking about the value that you bring from a security perspective? >> Yeah, we take security super seriously. It's built into our DNA. We do a lot of work to ensure that our platform is secure, that the data we store is kept private. It's of course always a concern. You see in the news all the time companies being compromised. You know, that's something that you can have an entire team working on, which we do, to make sure that the data that you have, whether it's in transit, whether it's at rest, is always kept secure, is only viewable by you. You look at things like software bill of materials. If you're running this yourself, you have to go vet all sorts of different pieces of software. And we do that, you know, as we use new tools. That's something that's just part of our jobs, to make sure that the platform that we're running has fully vetted software. And with open source especially, that's a lot of work. And so it's definitely new territory. Supply chain attacks are definitely happening at a higher clip than they used to. But that is really just part of a day in the life for folks like us that are building platforms. >> Yeah, and that's key. I mean, especially when you start getting into the, you know, we talk about IoT and the operations technologies, the engineers running that infrastructure. You know, historically, as you know, Tim, they would air gap everything. That's how they kept it safe. But that's not feasible anymore. Everything's >> Can't do that. >> connected now, right? And so you've got to have a partner that is, again, take away that heavy lifting to R and D so you can focus on some of the other activities. All right. Give us the last word and the key takeaways from your perspective. >> Well, you know, from my perspective, I see it as a a two lane approach. With Influx, with any any time series data, you know, you've got a lot of stuff that you're going to run on-prem. What you mentioned, air gaping, sure there's plenty of need for that, but at the end of the day, people that don't want to run big data centers, people that want to entrust their data to a company that's got a full platform set up for them that they can build on, send that data over to the cloud. The cloud is not going away. I think a more hybrid approach is where the future lives, and that's what we're prepared for. >> Tim, really appreciate you coming to the program. Great stuff. Good to see you. >> Thanks very much. Appreciate it. >> Okay, in a moment, I'll be back to wrap up today's session. You're watching the Cube. (gentle music)

>>Oracle continues to enhance my sequel Heatwave at a very rapid pace. The company is now in its fourth major release since the original announcement in December 2020. 1 of the main criticisms of my sequel, Heatwave, is that it only runs on O. C I. Oracle Cloud Infrastructure and as a lock in to Oracle's Cloud. Oracle recently announced that heat wave is now going to be available in AWS Cloud and it announced its intent to bring my sequel Heatwave to Azure. So my secret heatwave on AWS is a significant TAM expansion move for Oracle because of the momentum AWS Cloud continues to show. And evidently the Heatwave Engineering team has taken the development effort from O. C I. And is bringing that to A W S with a number of enhancements that we're gonna dig into today is senior vice president. My sequel Heatwave at Oracle is back with me on a cube conversation to discuss the latest heatwave news, and we're eager to hear any benchmarks relative to a W S or any others. Nippon has been leading the Heatwave engineering team for over 10 years and there's over 100 and 85 patents and database technology. Welcome back to the show and good to see you. >>Thank you. Very happy to be back. >>Now for those who might not have kept up with the news, uh, to kick things off, give us an overview of my sequel, Heatwave and its evolution. So far, >>so my sequel, Heat Wave, is a fully managed my secret database service offering from Oracle. Traditionally, my secret has been designed and optimised for transaction processing. So customers of my sequel then they had to run analytics or when they had to run machine learning, they would extract the data out of my sequel into some other database for doing. Unlike processing or machine learning processing my sequel, Heat provides all these capabilities built in to a single database service, which is my sequel. He'd fake So customers of my sequel don't need to move the data out with the same database. They can run transaction processing and predicts mixed workloads, machine learning, all with a very, very good performance in very good price performance. Furthermore, one of the design points of heat wave is is a scale out architecture, so the system continues to scale and performed very well, even when customers have very large late assignments. >>So we've seen some interesting moves by Oracle lately. The collaboration with Azure we've we've covered that pretty extensively. What was the impetus here for bringing my sequel Heatwave onto the AWS cloud? What were the drivers that you considered? >>So one of the observations is that a very large percentage of users of my sequel Heatwave, our AWS users who are migrating of Aurora or so already we see that a good percentage of my secret history of customers are migrating from GWS. However, there are some AWS customers who are still not able to migrate the O. C. I to my secret heat wave. And the reason is because of, um, exorbitant cost, which was charges. So in order to migrate the workload from AWS to go see, I digress. Charges are very high fees which becomes prohibitive for the customer or the second example we have seen is that the latency of practising a database which is outside of AWS is very high. So there's a class of customers who would like to get the benefits of my secret heatwave but were unable to do so and with this support of my secret trip inside of AWS, these customers can now get all the grease of the benefits of my secret he trip without having to pay the high fees or without having to suffer with the poorly agency, which is because of the ws architecture. >>Okay, so you're basically meeting the customer's where they are. So was this a straightforward lifted shift from from Oracle Cloud Infrastructure to AWS? >>No, it is not because one of the design girls we have with my sequel, Heatwave is that we want to provide our customers with the best price performance regardless of the cloud. So when we decided to offer my sequel, he headed west. Um, we have optimised my sequel Heatwave on it as well. So one of the things to point out is that this is a service with the data plane control plane and the console are natively running on AWS. And the benefits of doing so is that now we can optimise my sequel Heatwave for the E. W s architecture. In addition to that, we have also announced a bunch of new capabilities as a part of the service which will also be available to the my secret history of customers and our CI, But we just announced them and we're offering them as a part of my secret history of offering on AWS. >>So I just want to make sure I understand that it's not like you just wrapped your stack in a container and stuck it into a W s to be hosted. You're saying you're actually taking advantage of the capabilities of the AWS cloud natively? And I think you've made some other enhancements as well that you're alluding to. Can you maybe, uh, elucidate on those? Sure. >>So for status, um, we have taken the mind sequel Heatwave code and we have optimised for the It was infrastructure with its computer network. And as a result, customers get very good performance and price performance. Uh, with my secret he trade in AWS. That's one performance. Second thing is, we have designed new interactive counsel for the service, which means that customers can now provision there instances with the council. But in addition, they can also manage their schemas. They can. Then court is directly from the council. Autopilot is integrated. The council we have introduced performance monitoring, so a lot of capabilities which we have introduced as a part of the new counsel. The third thing is that we have added a bunch of new security features, uh, expose some of the security features which were part of the My Secret Enterprise edition as a part of the service, which gives customers now a choice of using these features to build more secure applications. And finally, we have extended my secret autopilot for a number of old gpus cases. In the past, my secret autopilot had a lot of capabilities for Benedict, and now we have augmented my secret autopilot to offer capabilities for elderly people. Includes as well. >>But there was something in your press release called Auto thread. Pooling says it provides higher and sustained throughput. High concerns concerns concurrency by determining Apple number of transactions, which should be executed. Uh, what is that all about? The auto thread pool? It seems pretty interesting. How does it affect performance? Can you help us understand that? >>Yes, and this is one of the capabilities of alluding to which we have added in my secret autopilot for transaction processing. So here is the basic idea. If you have a system where there's a large number of old EP transactions coming into it at a high degrees of concurrency in many of the existing systems of my sequel based systems, it can lead to a state where there are few transactions executing, but a bunch of them can get blocked with or a pilot tried pulling. What we basically do is we do workload aware admission control and what this does is it figures out, what's the right scheduling or all of these algorithms, so that either the transactions are executing or as soon as something frees up, they can start executing, so there's no transaction which is blocked. The advantage to the customer of this capability is twofold. A get significantly better throughput compared to service like Aurora at high levels of concurrency. So at high concurrency, for instance, uh, my secret because of this capability Uh oh, thread pulling offers up to 10 times higher compared to Aurora, that's one first benefit better throughput. The second advantage is that the true part of the system never drops, even at high levels of concurrency, whereas in the case of Aurora, the trooper goes up, but then, at high concurrency is, let's say, starting, uh, level of 500 or something. It depends upon the underlying shit they're using the troopers just dropping where it's with my secret heatwave. The truth will never drops. Now, the ramification for the customer is that if the truth is not gonna drop, the user can start off with a small shape, get the performance and be a show that even the workload increases. They will never get a performance, which is worse than what they're getting with lower levels of concurrency. So this let's leads to customers provisioning a shape which is just right for them. And if they need, they can, uh, go with the largest shape. But they don't like, you know, over pay. So those are the two benefits. Better performance and sustain, uh, regardless of the level of concurrency. >>So how do we quantify that? I know you've got some benchmarks. How can you share comparisons with other cloud databases especially interested in in Amazon's own databases are obviously very popular, and and are you publishing those again and get hub, as you have done in the past? Take us through the benchmarks. >>Sure, So benchmarks are important because that gives customers a sense of what performance to expect and what price performance to expect. So we have run a number of benchmarks. And yes, all these benchmarks are available on guitar for customers to take a look at. So we have performance results on all the three castle workloads, ol DB Analytics and Machine Learning. So let's start with the Rdp for Rdp and primarily because of the auto thread pulling feature. We show that for the IPCC for attended dataset at high levels of concurrency, heatwave offers up to 10 times better throughput and this performance is sustained, whereas in the case of Aurora, the performance really drops. So that's the first thing that, uh, tend to alibi. Sorry, 10 gigabytes. B B C c. I can come and see the performance are the throughput is 10 times better than Aurora for analytics. We have done a comparison of my secret heatwave in AWS and compared with Red Ship Snowflake Googled inquiry, we find that the price performance of my secret heatwave compared to read ship is seven times better. So my sequel, Heat Wave in AWS, provides seven times better price performance than red ship. That's a very, uh, interesting results to us. Which means that customers of Red Shift are really going to take the service seriously because they're gonna get seven times better price performance. And this is all running in a W s so compared. >>Okay, carry on. >>And then I was gonna say, compared to like, Snowflake, uh, in AWS offers 10 times better price performance. And compared to Google, ubiquity offers 12 times better price performance. And this is based on a four terabyte p PCH workload. Results are available on guitar, and then the third category is machine learning and for machine learning, uh, for training, the performance of my secret heatwave is 25 times faster compared to that shit. So all the three workloads we have benchmark's results, and all of these scripts are available on YouTube. >>Okay, so you're comparing, uh, my sequel Heatwave on AWS to Red Shift and snowflake on AWS. And you're comparing my sequel Heatwave on a W s too big query. Obviously running on on Google. Um, you know, one of the things Oracle is done in the past when you get the price performance and I've always tried to call fouls you're, like, double your price for running the oracle database. Uh, not Heatwave, but Oracle Database on a W s. And then you'll show how it's it's so much cheaper on on Oracle will be like Okay, come on. But they're not doing that here. You're basically taking my sequel Heatwave on a W s. I presume you're using the same pricing for whatever you see to whatever else you're using. Storage, um, reserved instances. That's apples to apples on A W s. And you have to obviously do some kind of mapping for for Google, for big query. Can you just verify that for me, >>we are being more than fair on two dimensions. The first thing is, when I'm talking about the price performance for analytics, right for, uh, with my secret heat rape, the cost I'm talking about from my secret heat rape is the cost of running transaction processing, analytics and machine learning. So it's a fully loaded cost for the case of my secret heatwave. There has been I'm talking about red ship when I'm talking about Snowflake. I'm just talking about the cost of these databases for running, and it's only it's not, including the source database, which may be more or some other database, right? So that's the first aspect that far, uh, trip. It's the cost for running all three kinds of workloads, whereas for the competition, it's only for running analytics. The second thing is that for these are those services whether it's like shit or snowflakes, That's right. We're talking about one year, fully paid up front cost, right? So that's what most of the customers would pay for. Many of the customers would pay that they will sign a one year contract and pay all the costs ahead of time because they get a discount. So we're using that price and the case of Snowflake. The costs were using is their standard edition of price, not the Enterprise edition price. So yes, uh, more than in this competitive. >>Yeah, I think that's an important point. I saw an analysis by Marx Tamer on Wiki Bond, where he was doing the TCO comparisons. And I mean, if you have to use two separate databases in two separate licences and you have to do et yelling and all the labour associated with that, that that's that's a big deal and you're not even including that aspect in in your comparison. So that's pretty impressive. To what do you attribute that? You know, given that unlike, oh, ci within the AWS cloud, you don't have as much control over the underlying hardware. >>So look hard, but is one aspect. Okay, so there are three things which give us this advantage. The first thing is, uh, we have designed hateful foreign scale out architecture. So we came up with new algorithms we have come up with, like, uh, one of the design points for heat wave is a massively partitioned architecture, which leads to a very high degree of parallelism. So that's a lot of hype. Each were built, So that's the first part. The second thing is that although we don't have control over the hardware, but the second design point for heat wave is that it is optimised for commodity cloud and the commodity infrastructure so we can have another guys, what to say? The computer we get, how much network bandwidth do we get? How much of, like objects to a brand that we get in here? W s. And we have tuned heat for that. That's the second point And the third thing is my secret autopilot, which provides machine learning based automation. So what it does is that has the users workload is running. It learns from it, it improves, uh, various premieres in the system. So the system keeps getting better as you learn more and more questions. And this is the third thing, uh, as a result of which we get a significant edge over the competition. >>Interesting. I mean, look, any I SV can go on any cloud and take advantage of it. And that's, uh I love it. We live in a new world. How about machine learning workloads? What? What did you see there in terms of performance and benchmarks? >>Right. So machine learning. We offer three capabilities training, which is fully automated, running in France and explanations. So one of the things which many of our customers told us coming from the enterprise is that explanations are very important to them because, uh, customers want to know that. Why did the the system, uh, choose a certain prediction? So we offer explanations for all models which have been derailed by. That's the first thing. Now, one of the interesting things about training is that training is usually the most expensive phase of machine learning. So we have spent a lot of time improving the performance of training. So we have a bunch of techniques which we have developed inside of Oracle to improve the training process. For instance, we have, uh, metal and proxy models, which really give us an advantage. We use adaptive sampling. We have, uh, invented in techniques for paralysing the hyper parameter search. So as a result of a lot of this work, our training is about 25 times faster than that ship them health and all the data is, uh, inside the database. All this processing is being done inside the database, so it's much faster. It is inside the database. And I want to point out that there is no additional charge for the history of customers because we're using the same cluster. You're not working in your service. So all of these machine learning capabilities are being offered at no additional charge inside the database and as a performance, which is significantly faster than that, >>are you taking advantage of or is there any, uh, need not need, but any advantage that you can get if two by exploiting things like gravity. John, we've talked about that a little bit in the past. Or trainee. Um, you just mentioned training so custom silicon that AWS is doing, you're taking advantage of that. Do you need to? Can you give us some insight >>there? So there are two things, right? We're always evaluating What are the choices we have from hybrid perspective? Obviously, for us to leverage is right and like all the things you mention about like we have considered them. But there are two things to consider. One is he is a memory system. So he favours a big is the dominant cost. The processor is a person of the cost, but memory is the dominant cost. So what we have evaluated and found is that the current shape which we are using is going to provide our customers with the best price performance. That's the first thing. The second thing is that there are opportunities at times when we can use a specialised processor for vaccinating the world for a bit. But then it becomes a matter of the cost of the customer. Advantage of our current architecture is on the same hardware. Customers are getting very good performance. Very good, energetic performance in a very good machine learning performance. If you will go with the specialised processor, it may. Actually, it's a machine learning, but then it's an additional cost with the customers we need to pay. So we are very sensitive to the customer's request, which is usually to provide very good performance at a very low cost. And we feel is that the current design we have as providing customers very good performance and very good price performance. >>So part of that is architectural. The memory intensive nature of of heat wave. The other is A W s pricing. If AWS pricing were to flip, it might make more sense for you to take advantage of something like like cranium. Okay, great. Thank you. And welcome back to the benchmarks benchmarks. Sometimes they're artificial right there. A car can go from 0 to 60 in two seconds. But I might not be able to experience that level of performance. Do you? Do you have any real world numbers from customers that have used my sequel Heatwave on A W s. And how they look at performance? >>Yes, absolutely so the my Secret service on the AWS. This has been in Vera for, like, since November, right? So we have a lot of customers who have tried the service. And what actually we have found is that many of these customers, um, planning to migrate from Aurora to my secret heat rape. And what they find is that the performance difference is actually much more pronounced than what I was talking about. Because with Aurora, the performance is actually much poorer compared to uh, like what I've talked about. So in some of these cases, the customers found improvement from 60 times, 240 times, right? So he travels 100 for 240 times faster. It was much less expensive. And the third thing, which is you know, a noteworthy is that customers don't need to change their applications. So if you ask the top three reasons why customers are migrating, it's because of this. No change to the application much faster, and it is cheaper. So in some cases, like Johnny Bites, what they found is that the performance of their applications for the complex storeys was about 60 to 90 times faster. Then we had 60 technologies. What they found is that the performance of heat we have compared to Aurora was 100 and 39 times faster. So, yes, we do have many such examples from real workloads from customers who have tried it. And all across what we find is if it offers better performance, lower cost and a single database such that it is compatible with all existing by sequel based applications and workloads. >>Really impressive. The analysts I talked to, they're all gaga over heatwave, and I can see why. Okay, last question. Maybe maybe two and one. Uh, what's next? In terms of new capabilities that customers are going to be able to leverage and any other clouds that you're thinking about? We talked about that upfront, but >>so in terms of the capabilities you have seen, like they have been, you know, non stop attending to the feedback from the customers in reacting to it. And also, we have been in a wedding like organically. So that's something which is gonna continue. So, yes, you can fully expect that people not dressed and continue to in a way and with respect to the other clouds. Yes, we are planning to support my sequel. He tripped on a show, and this is something that will be announced in the near future. Great. >>All right, Thank you. Really appreciate the the overview. Congratulations on the work. Really exciting news that you're moving my sequel Heatwave into other clouds. It's something that we've been expecting for some time. So it's great to see you guys, uh, making that move, and as always, great to have you on the Cube. >>Thank you for the opportunity. >>All right. And thank you for watching this special cube conversation. I'm Dave Volonte, and we'll see you next time.

>>Good afternoon, everyone. And welcome back to the VMware Explorer. 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah. Yeah. The big set >>And we're very excited to be welcoming buck. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but first >>Time, first time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west, >>I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or hiatus. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives was on my board. Bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago and now Ko. So that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors. Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah, I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassey at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian, IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NSX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and vs a and VCF very relevant to that part of the data management and data security continuum, which I think could end VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player. It's gonna be hard. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multi-cloud. So I can go to an, a Walmart and say, I can back up your data into Azure if you choose to, but the control plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna believe the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multicloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to him, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform. >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 29 >>Quite a bit in that session, he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be. So I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep. And I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right for disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimball and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right on snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank SL doesn't like when I say playbook, cuz I says, Dave, I'm a situational CEO, no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I see you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do you, as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X by cheaper, faster with the Webscale glass offer the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just backup recovery. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes getting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no later than one month. Okay. And I don't wanna pay the bad guys at penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar are going up. Yep. >>And, and when you pay the ransom, you don't always get your data back. So you that's not. >>And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not voted on. Okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, this >>Security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things that we've talking about Mount ransomware, I want to ask you, you I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look, >>Thank you. That's all good. Oh, >>Shucks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, if the area announced today, Mongo, DB, beat and Ray, that things getting crushed and, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You've seen where I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to where this, I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank Salman. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo D and snowflake are fantastic companies, they're CEOs of people I respect. They've actually kind of an, a, you know, advisor to us as a company, you knows moat very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the Snowflake's had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB, open source, two data technology, that's, you know, winning, I, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most ask? What are you most anxious about and what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come Tom world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies I've worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but there are are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of them. And it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's Steph Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me. It's a victory for the team. >>I always think I'm glad that you brought up the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda Naor. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels of going right. That sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a midsize company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you. Best of luck. Congratulations. On the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanjay. We always appreciate having you. Thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanja poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.

>>Good afternoon, everyone. And welcome back to the Cube's day two coverage of VMware Explorer, 2022 live from San Francisco. Lisa Martin, here with Dave. Valante good to be sitting next to you, sir. >>Yeah, the big >>Set and we're very excited to be welcoming back. One of our esteemed alumni Sanja poin joins us, the CEO and president of cohesive. Nice to see >>You. Thank you, Lisa. Thank you, Dave. It's great to meet with you all the time and the new sort of setting here, but >>First time we've been in west, is that right? We've been in north. We've been in south. We've been in Las Vegas, right. But west >>Nice. Well, I mean, it's also good to be back with live shows with absolutely, you know, after sort of the two or three or high. And it was a hard time for the whole world, but I'm kind of driving a little bit of adrenaline just being here with people. So >>You've also got some adrenaline, sorry, Dave. Yeah, you're good because you are new in the role at cohesive. You wrote a great blog that you are identified. The four reasons I came to cohesive. Tell the audience, just give 'em a little bit of a teaser about that. >>Yeah, I think you should all read it. You can Google and, and Google find that article. I talked about the people Mohi is a fantastic founder. You know, he was the, you know, the architect of the Google file system. And you know, one of the senior Google executives who was on my board, bill Corrin said one of the smartest engineers. He was the true father of hyperconverge infrastructure. A lot of the code of Nutanix. He wrote, I consider him really the father of that technology, which brought computer storage. And when he took that same idea of bringing compute to secondary storage, which is really what made the scale out architect unique. And we were at your super cloud event talking about that, Dave. Yeah. Right. So it's a people I really got to respect his smarts, his integrity and the genius, what he is done. >>I think the customer base, I called a couple of customers. One of them, a fortune 100 customer. I, I can't tell you who it was, but a very important customer. I've known him. He said, I haven't seen tech like this since VMware, 20 years ago, Amazon 10 years ago. And now COER so that's special league. We're winning very much in the enterprise and that type of segment, the partners, you know, we have HPE, Cisco as investors, Amazon's an investors. So, you know, and then finally the opportunity, I think this whole area of data management and data security now with threats, like ransomware big opportunity. >>Sure. Okay. So when you were number two at VMware, you would come on and say, we'd love all our partners and of course, okay. So you know, a little bit about how to work with, with VMware. So, so when you now think about the partnership between cohesive and VMware, what are the things that you're gonna stress to your constituents on the VMware side to convince them that Hey, partnering with cohesive is gonna gonna drive more value for customers, you know, put your thumb on the scale a little bit. You know, you gotta, you gotta unfair advantage somewhat, but you should use it. So what's the narrative gonna be like? >>Yeah. I think listen with VMware and Amazon, that probably their top two partners, Dave, you know, like one of the first calls I made was to Raghu and he knew about this decision before. That's the level of trust I have in him. I even called Michael Dell, you know, before I made the decision, there's a little bit of an overlap with Dell, but it's really small compared to the overlap, the potential with Dell hardware that we could compliment. And then I called four CEOs. I was, as I was making this decision, Andy Jassy at Amazon, he was formerly AWS CEO sat Nadela at Microsoft Thomas cor at Google and Arvin Christian at IBM to say, I'm thinking about this making decision. They are many of the mentors and friends to me. So I believe in an ecosystem. And you know, even Chuck Robbins, who the CEO of Cisco is an investor, I texted him and said, Hey, finally, we can be friends. >>It was harder to us to be friends with Cisco, given the overlap of NEX. So I have a big tent towards everybody in our ecosystem with VMware. I think the simple answer is there's no overlap okay. With, with the kind of the primary storage capabilities with VSAN. And by the same thing with Nutanix, we will be friends and, and extend that to be the best data protection solution. But given also what we could do with security, I think this is gonna go a lot further. And then it's all about meet in the field. We have common partners. I think, you know, sort of the narrative I talked about in that blog is just like snowflake was replacing Terada and ServiceNow replace remedy and CrowdStrike, replacing Symantec, we're replacing legacy vendors. We are viewed as the modern solution cloud optimized for private and public cloud. We can help you and make VMware and VSAN and VCF very relevant to that part of the data management and data security continuum, which I think could enhance VMware. And by the way, the same thing into the public cloud. So most of the places where we're being successful is clearly withs, but increasingly there's this discussion also about playing into the cloud. So I think both with VMware and Amazon, and of course the other partners in the hyperscaler service, storage, networking place and security, we have some big plans. >>How, how much do you see this? How do you see this multi-cloud narrative that we're hearing here from, from VMware evolving? How much of an opportunity is it? How are customers, you know, we heard about cloud chaos yesterday at the keynote, are customers, do they, do they admit that there's cloud chaos? Some probably do some probably don't how much of an opportunity is that for cohesive, >>It's tremendous opportunity. And I think that's why you need a Switzerland type player in this space to be successful. And you know, and you can't explicitly rule out the fact that the big guys get into this space, but I think it's, if you're gonna back up office 365 or what they call now, Microsoft 365 into AWS or Google workspace into Azure or Salesforce into one of those clouds, you need a Switzerland player it's gonna be out. And in many cases, if you're gonna back up data or you protect that data into AWS banks need a second copy of that either on premise or Azure. So it's very hard, even if they have their own native data protection for them to be dual cloud. So I think a multi-cloud story and the fact that there's at least three big vendors of cloud in, in the us, you know, one in China, if include Alibaba creates a Switzerland opportunity for us, that could be fairly big. >>And I think, you know, what we have to do is make sure while we'll be optimized, our preferred cloud is AWS. Our control plane runs there. We can't take an all in AWS stack with the control plane and the data planes at AWS to Walmart. So what I've explained to both Microsoft and AWS is that data plane will need to be multicloud. So I can go to an a Walmart and say, I can back up your data into Azure if you choose to, but the control, plane's still gonna be an AWS, same thing with Google. Maybe they have another account. That's very Google centric. So that's how we're gonna play the, the control plane will be in AWS. We'll optimize it there, but the data plane will be multi-cloud. >>Yeah. And that's what Mo had explained at Supercloud. You know, and I talked to, he really helped me hone in on the deployment models. Yes. Where, where, where the cohesive deployment model is instantiating that technology stack into each cloud region and each cloud, which gives you latency advantages and other advantages >>And single code based same platform, >>And then bringing it, tying it together with a unified, you know, interface. That was he, he was, he was key. In fact, I, I wrote about it recently and, and gave him and the other 20, >>Quite a bit in that session. Yeah. So he went deep with you. I >>Mean, with Mohi, when you get a guy who developed a Google file system, you know, who can technically say, okay, this is technically correct or no, Dave, your way off be so I that's why I had to >>Go. I, I thought you did a great job in that interview because you probed him pretty deep and I'm glad we could do that together with him next time. Well, maybe do that together here too, but it was really helpful. He's the, he's the, he's the key reason I'm here. >>So you say data management is ripe for disrupt disruption. Talk about that. You talked about this Switzerland effect. That sounds to me like a massive differentiator for cohesive. Why is data management right. For disruption and why is cohesive the right partner to do it? >>Yeah, I think, listen, everyone in this sort of data protection backup from years ago have been saying the S Switzerland argument 18 years ago, I was a at Veras an executive there. We used the Switzerland argument, but what's changed is the cloud. And what's changed as a threat vector in security. That's, what's changed. And in that the proposition of a, a Switzerland player has just become more magnified because you didn't have a sales force or Workday service now then, but now you do, you didn't have multi-cloud. You had hardware vendors, you know, Dell, HPE sun at the time. IBM, it's now Lenovo. So that heterogeneity of, of on-premise service, storage, networking, HyperCloud, and, and the apps world has gotten more and more diverse. And I think you really need scale out architectures. Every one of the legacy players were not built with scale out architectures. >>If you take that fundamental notion of bringing compute to storage, you could almost paralyze. Imagine you could paralyze backup recovery and bring so much scale and speed that, and that's what Mo invented. So he took that idea of how he had invented and built Nutanix and applied that to secondary storage. So now everything gets faster and cheaper at scale. And that's a disruptive technology ally. What snowflake did to ator? I mean, the advantage of snowflake is when you took that same concept data, warehousing is not a new concept it's existed from since Ralph Kimble and bill Inman and the people who are fathers of data warehousing, they took that to Webscale. And in that came a disruptive force toter data, right? And snowflake. And then of course now data bricks and big query, similar things. So we're doing the same thing. We just have to showcase the customers, which we do. And when large customers see that they're replacing the legacy solutions, I have a lot of respect for legacy solutions, but at some point in time of a solution was invented in 1995 or 2000, 2005. It's right. For change. >>So you use snowflake as an example, Frank sluman doesn't like when I say playbook, cuz I says, Dave, I'm a situational. See you no playbook, but there are patterns here. And one of the things he did is to your point go after, you know, Terra data with a better data warehouse, simplify scale, et cetera. And now he's, he's a constructing a Tam expansion strategy, same way he did at ServiceNow. And I, you guys following a similar pattern. Okay. You get your foot in the door. Let's face it. I mean, a lot of this started with, you know, just straight back. Okay, great. Now it's extending into data management now extending to multi-cloud that's like concentric circles in a Tam expansion strategy. How, how do as, as a CEO, that's part of your job is Tam expansion. >>So yeah, I think the way to think about the Tam is, I mean, people say it's 20, 30 billion, but let me tell you how you can piece it apart in size, Dave and Lisa number one, I estimate there's probably about 10 to 20 exabytes of data managed by these legacy players of on-prem stores that they back up to. Okay. So you add them all up in the market shares that they respectively are. And by the way, at the peak, the biggest of these companies got to 2 billion and then shrunk. That was Verto when I was there in 2004, 2 billion, every one of them is small and they stopped growing. You look at the IDC charts. Many of them are shrinking. We are the fastest growing in the last two years, but I estimate there's about 20 exabytes of data that collectively among the legacy players, that's either gonna stay on prem or move to the cloud. Okay. So the opportunity as they replace one of those legacy tools with us is first off to manage that 20 X bike cheaper, faster with the Webscale, a glass or for the cloud guys, we could tip that into the cloud. Okay. >>But you can't stop there. >>Okay. No, we are not doing just back recovery. Right. We have a platform that can do files. We can do test dev analytics and now security. Okay. That data is potentially at a risk, not so much in the past, but for ransomware, right? How do we classify that? How do we govern that data? How do we run potential? You know, the same way you did antivirus some kind of XDR algorithms on the data to potentially not just catch the recovery process, which is after fact, but maybe the predictive act of before to know, Hey, there's somebody loitering around this data. So if I'm basically managing in the exabytes of data and I can proactively tell you what, this is, one CIO described this very simply to me a few weeks ago that I, and she said, I have 3000 applications, okay. I wanna be prepared for a black Swan event, except it's not a nine 11 planes hitting the, the buildings. >>It is an extortion event. And I want to know when that happens, which of my 3000 apps I recover within one hour within one day within one week, no lay than one month. Okay. And I don't wanna pay the bad guys of penny. That's what we do. So that's security discussions. We didn't have that discussion in 2004 when I was at another company, because we were talking about flood floods and earthquakes as a disaster recovery. Now you have a lot more security opportunity to be able to describe that. And that's a boardroom discussion. She needs to have that >>Digital risk. O O okay, go ahead please. I >>Was just gonna say, ransomware attack happens every what? One, every 11, 9, 11 seconds. >>And the dollar amount are going up, you know, dollar of what? >>Yep. And, and when you pay the ransom, you don't always get your data back. So you that's >>Not. And listen, there's always an ethical component. Should you do it or not do it? If you, if you don't do it and you're threatened, they may have left an Easter egg there. Listen, I, I feel very fortunate that I've been doing a lot in security, right? I mean, I built the business at, at, at VMware. We got it to over a billion I'm on the board of sneak. I've been doing security and then at SAP ran. So I know a lot about security. So what we do in security and the ecosystem that supports us in security, we will have a very carefully crafted stay tuned. Next three weeks months, you'll see us really rolling out a very kind of disciplined aspect, but we're not gonna pivot this company and become a cyber security company. Some others in our space have done that. I think that's not who we are. We are a data management and a data security company. We're not just a pure security company. We're doing both. And we do it well, intelligently, thoughtfully security is gonna be built into our platform, not bolted on, okay. And there'll be certain security things that we do organically. There's gonna be a lot that we do through partnerships, >>This security market that's coming to you. You don't have to go claim that you're now a security vendor, right? The market very naturally saying, wow, a comprehensive security strategy has to incorporate a data protection strategy and a recovery, you know, and the things we've talking about, Mount ransomware, I want to ask you, you know, I've been around a long time, longer than you actually Sanjay. So, but you you've, you've seen a lot. You look incredibly, >>Thank you. That's all good. Oh, >>Shocks. So the market, I've never seen a market like this, right? I okay. After the.com crash, we said, and I know you can't talk about IPO. That's not what I'm talking about, but everything was bad after that. Right. 2008, 2000, everything was bad. I've never seen a market. That's half full, half empty, you know, snowflake beats and raises the stock, goes through the roof. Dev if it, the area announced today, Mongo, DB, beat and Ray, that things getting crushed. And, and after market never seen anything like this. It's so fed, driven and, and hard to protect. And, and of course, I know it's a marathon, you know, it's not a sprint, but have you ever seen anything like this? >>Listen, I walk worked through 18 quarters as COO of VMware. You seen, I've seen public quarters there and you know, was very fortunate. Thanks to the team. I don't think I missed my numbers in 18 quarters except maybe once close. But we, it was, it's tough. Being a public company. Officer of the company is tough. I did that also at SAP. So the journey from 10 to 20 billion at SAP, the journey from six to 12 at VMware, that I was able to be fortunate. It's humbling because you, you really, you know, we used to have this, we do the earnings call and then we kind of ask ourselves, what, what do you think the stock price was gonna be a day and a half later? And we'd all take bets as to wear this. I think you just basically, as a, as a sea level executive, you try to build a culture of beaten, raise, beaten, raise, beaten, raise, and you wanna set expectations in a way that you're not setting them up for failure. >>And you know, it's you, there's, Dave's a wonderful CEO as is Frank movement. So it's hard for me to dissect. And sometimes the market are fickle on some small piece of it. But I think also the, when I, I encourage people say, take the long term view. When you take the long term view, you're not bothered about the ups and downs. If you're building a great company over the length of time, now it will be very clear over the arc of many, many quarters that you're business is trouble. If you're starting to see a decay in growth. And like, for example, when you start to see a growth, start to decay significantly by five, 10 percentage points, okay, there's something macro going on at this company. And that's what you won't avoid. But these, you know, ups and downs, my view is like, if you've got both Mongo, DIA and snowflake are fantastic companies, they're CEOs of people I respect. They've actually a kind of an, a, you know, advisor to us as a company, you knows mot very well. So we respect him, respect Frank, and you, there have been other quarters where Frank's, you know, the snowflakes had a down result after that. So you build a long term and they are on the right side of history, snowflake, and both of them in terms of being a modern cloud relevant in the case of MongoDB open source to data technology, that's, you know, winning, I, we would like to be like them one day >>As, as the new CEO of cohesive, what are you most, what are you most anxious about? And what are you most excited about? >>I think, listen, you know, you know, everything starts with the employee. You, I always believe I wrote my first memo to all employees. There was an article in Harvard business review called service profit chains that had a seminal impact on my leadership, which is when they studied companies who had been consistently profitable over a long period of time. They found that not just did those companies serve their customers well, but behind happy engaged customers were happy, engaged employees. So I always believe you start with the employee and you ensure that they're engaged, not just recruiting new employees. You know, I put on a tweet today, we're hiring reps and engineers. That's okay. But retaining. So I wanna start with ensuring that everybody, sometimes we have to make some unfortunate decisions with employees. We've, we've got a part company with, but if we can keep the best and brightest retained first, then of course, you know, recruiting machine, I'm trying to recruit the best and brightest to this company, people all over the place. >>I want to get them here. It's been, so I mean, heartwarming to come to world and just see people from all walks, kind of giving me hugs. I feel incredibly blessed. And then, you know, after employees, it's customers and partners, I feel like the tech is in really good hands. I don't have to worry about that. Cuz Mo it's in charge. He's got this thing. I can go to bed knowing that he's gonna keep innovating the future. Maybe in some of the companies, I would worried about the tech innovation piece, but most doing a great job there. I can kind of leave that in his cap of hands, but employees, customers, partners, that's kind of what I'm focused on. None of them are for me, like a keep up at night, but they're are opportunities, right? And sometimes there's somebody you're trying to salvage to make sure or somebody you're trying to convince to join. >>But you know, customers, I love pursuing customers. I love the win. I hate to lose. So fortune 1000 global, 2000 companies, small companies, big companies, I wanna win every one of 'em and it's not, it's not like, I mean, I know all these CEOs in my competitors. I texted him the day I joined and said, listen, I'll compete, honorably, whatever have you, but it's like Kobe and LeBron Kobe's passed away now. So maybe it's step Curry. LeBron, whoever your favorite athlete is you put your best on the court and you win. And that's how I am. That's nothing I've known no other gear than to put my best on the court and win, but do it honorably. It should not be the one that you're doing it. Unethically. You're doing it personally. You're not calling people's names. You're competing honorably. And when you win the team celebrates, it's not a victory for me, it's a victory for the team. >>I always think I'm glad that you brought out the employee experience and we're almost out of time, but I always think the employee experience and the customer experience are inextricably linked. This employees have to be empowered. They have to have the data that they need to do their job so that they can deliver to the customer. You can't do one without the other. >>That's so true. I mean, I, it's my belief. And I've talked also on this show and others about servant leadership. You know, one of my favorite poems is Brenda NA Tago. I went to bed in life. I dreamt that life was joy. I woke up and realized life was service. I acted in service was joy. So when you have a leadership model, which is it's about, I mean, there's lots of layers between me and the individual contributor, but I really care about that sales rep and the engineer. That's the leaf level of the organization. What can I get obstacle outta their way? I love skipping levels and going write that sales rep let's go and crack this deal. You know? So you have that mindset. Yeah. I mean, you, you empower, you invert the pyramid and you realize the power is at the leaf level of an organization. >>So that's what I'm trying to do. It's a little easier to do it with 2000 people than I dunno, either 20, 20, 2000 people or 35,000 reported me at VMware. And I mean a similar number at SAP, which was even bigger, but you can shape this. Now we are, we're not a startup anymore. We're a mid-size company. We'll see. Maybe along the way, there's an IP on the path. We'll wait for that. When it comes, it's a milestone. It's not the destination. So we do that and we are, we, I told people we are gonna build this green company. Cohesive is gonna be a great company like VMware one day, like Amazon. And there's always a day of early beginnings, but we have to work harder. This is kind of like the, you know, eight year old version of your kid, as opposed to the 18 year old version of the kid. And you gotta work a little harder. So I love it. Yeah. >>Good luck. Awesome. Thank you too. Best of luck. Congratulations on the role, it sounds like there's a tremendous amount of adrenaline, a momentum carrying you forward Sanja. We always appreciate having thank >>You for having in your show. >>Thank you. Our pleasure, Lisa. Thank you for Sanjay poin and Dave ante. I'm Lisa Martin. You're watching the cube live from VMware Explorer, 2022, stick around our next guest. Join us momentarily.

>>Okay, welcome back everyone. There's the cubes coverage of VMware Explorer, 22 formerly world. We've been here since 2010 and world 2010 to now it's 2022. And it's VMware Explorer. We're here at the CEO, regular writer. Welcome back to the cube. Great to see you in person. >>Yeah. Great to be here in person, >>Dave and I are, are proud to say that we've been to 12 straight years of covering VMware's annual conference. And thank you. We've seen the change in the growth over time and you know, it's kind of, I won't say pinch me moment, but it's more of a moment of there's the VMware that's grown into the cloud after your famous deal with Andy jazzy in 2016, we've been watching what has been a real sea change and VMware since taking that legacy core business and straightening out the cloud strategy in 2016, and then since then an acceleration of, of cloud native, like direction under your leadership at VMware. Now you're the CEO take us through that because this is where we are right now. We are here at the pinnacle of VMware 2.0 or cloud native VMware, as you point out on your keynote, take us through that history real quick. Cuz I think it's important to know that you've been the architect of a lot of this change and it's it's working. >>Yeah, definitely. We are super excited because like I said, it's working, the history is pretty simple. I mean we tried running our own cloud cloud air. We cloud air didn't work so well. Right. And then at that time, customers really gave us strong feedback that the hybrid they wanted was a Amazon together. Right. And so that's what we went back and did and the andjay announcement, et cetera. And then subsequently as we were continue to build it out, I mean, once that happened, we were able to go work with the Satia and Microsoft and others to get the thing built out all over. Then the next question was okay, Hey, that's great for the workloads that are running on vSphere. What's the story for workloads that are gonna be cloud native and benefit a lot from being cloud native. So that's when we went the Tansu route and the Kubernetes route, we did a couple of acquisitions and then we started that started paying off now with the Tansu portfolio. And last but not the least is once customers have this distributed portfolio now, right. Increasingly everything is becoming multi-cloud. How do you manage and connect and secure. So that's what you start seeing that you saw the management announcement, networking and security and everything else is cooking. And you'll see more stuff there. >>Yeah know, we've been talking about super cloud. It's kinda like a multi-cloud on steroids kind a little bit different pivot of it. And we're seeing some use cases. >>No, no, it's, it's a very great, it's a, it's pretty close to what we talk about. >>Awesome. I mean, and we're seeing this kind of alignment in the industry. It's kind of open, but I have to ask you, when did you, you have the moment where you said multicloud is the game changer moment. When did you have, because you guys had hybrid, which is really early as well. When was the Raghu? When did you have the moment where you said, Hey, multicloud is what's happening. That's we're doubling down on that go. >>I mean, if you think about the evolution of the cloud players, right. Microsoft really started picking up around the 2018 timeframe. I mean, I'm talking about Azure, right? >>In a big way. >>Yeah. In a big way. Right. When that happened and then Google got really serious, it became pretty clear that this was gonna be looking more like the old database market than it looked like a single player cloud market. Right. Equally sticky, but very strong players all with lots of IP creation capability. So that's when we said, okay, from a supplier side, this is gonna become multi. And from a customer side that has always been their desire. Right. Which is, Hey, I don't want to get locked into anybody. I want to do multiple things. And the cloud vendors also started leveraging that OnPrem. Microsoft said, Hey, if you're a windows customer, your licensing is gonna be better off if you go to Azure. Right. Oracle did the same thing. So it just became very clear. >>I am, I have gone make you laugh. I always go back to the software mainframe because I, I think you were here. Right. I mean, you're, you're almost 20 years in. Yeah. And I, the reason I appreciate that is because, well, that's technically very challenging. How do you make virtualization overhead virtually non-existent how do you run any workload? Yeah. How do you recover from, I mean, that's was not trivial. Yeah. Okay. So what's the technical, you know, analog today, the real technical challenge. When you think about cross cloud services. >>Yeah. I mean, I think it's different for each of these layers, right? So as I was alluding to for management, I mean, you can go each one of them by themselves, there is one way of Mo doing multi-cloud, which is multiple clouds. Right. You could say, look, I'm gonna build a great product for AWS. And then I'm gonna build a great product for Azure. I'm gonna build a great product for Google. That's not what aria is. Aria is a true multi-cloud, which means it pulls data in from multiple places. Right? So there are two or three, there are three things that aria has done. That's I think is super interesting. One is they're not trying to take all the data and bring it in. They're trying to federate the data sources. And secondly, they're doing it in real time and they're able to construct this graph of a customer's cloud resources. >>Right. So to keep the graph constructed and pulling data, federating data, I think that's a very interesting concept. The second thing that, like I said is it's a real time because in the cloud, a container might come and go like that. Like that is a second technical challenge. The third it's not as much a technical challenge, but I really like what they have done for the interface they've used GraphQL. Right? So it's not about if you remember in the old world, people talk about single pan or glass, et cetera. No, this is nothing to do with pan or glass. This is a data model. That's a graph and a query language that's suited for that. So you can literally think of whatever you wanna write. You can write and express it in GraphQL and pull all sorts of management applications. You can say, Hey, I can look at cost. I can look at metrics. I can look at whatever it is. It's not five different types of applications. It's one, that's what I think had to do it at scale is the other problem. And, and >>The, the technical enable there is just it's good software. It's a protocol. It's >>No, no, it's, it's, it's it's software. It's a data model. And it's the Federation architecture that they've got, which is open. Right. You can pull in data from Datadog, just as well as from >>Pretty >>Much anything data from VR op we don't care. Right? >>Yeah. Yeah. So rego, I have to ask you, I'm glad you like the Supercloud cuz you know, we, we think multi-cloud still early, but coming fast. I mean, everyone has multiple clouds, but spanning this idea of spanning across has interesting sequences. Do you data, do you do computer both and a lot of good things happening. Kubernetes been containers, all that good stuff. Okay. How do you see the first rev of multi-cloud evolving? Like is it what happens? What's the sequence, what's the order of operations for a client standpoint? Customer standpoint of, of multicloud or Supercloud because we think we're seeing it as a refactoring of something like snowflake, they're a data base, they're a data warehouse on the cloud. They, they say data cloud they'd they like they'll tell us no, you, we're not a data. We're not a data warehouse. We're data cloud. Okay. You're a data warehouse refactored for the CapEx from Amazon and cooler, newer things. Yeah, yeah, yeah. That's a behavior change. Yeah. But it's still a data warehouse. Yeah. How do you see this multi-cloud environment? Refactoring? Is there something that you see that might be different? That's the same if you know what I'm saying? Like what's what, what's the ne the new thing that's happening with multi-cloud, that's different than just saying I'm I'm doing SAS on the cloud. >>Yeah. So I would say, I would point to a, a couple of things that are different. Firstly, my, the answer depends on which category you are in. Like the category that snowflake is in is very different than Kubernetes or >>Something or Mongo DB, right? >>Yeah. Or Mongo DB. So, so it is not appropriate to talk about one multi-cloud approach across data and compute and so, so on and so forth. So I'll talk about the spaces that we play. Right. So step one, for most customers is two application architectures, right? The cloud native architecture and an enterprise native architecture and tying that together either through data or through networks or through et cetera. So that's where most of the customers are. Right. And then I would say step two is to bring these things together in a more, in a closer fashion and that's where we are going. And that is why you saw the cloud universal announcement and that's already, you've seen the Tansu announcement, et cetera. So it's really, the step one was two distinct clouds. That is just two separate islands. >>So the other thing that we did, that's really what my, the other thing that I'd like to get to your reaction on, cause this is great. You're like a masterclass in the cube here. Yeah, totally is. We see customers becoming super clouds because they're getting the benefit of, of VMware, AWS. And so if I'm like a media company or insurance company, if I have scale, if I continue to invest in, in cloud native development, I do all these things. I'm gonna have a da data scale advantage, possibly agile, which means I can build apps and functionality very quick for customers. I might become my own cloud within the vertical. Exactly. And so I could then service other people in the insurance vertical if I'm the insurance company with my technology and create a separate power curve that never existed before. Cause the CapEx is off the table, it's operating expense. Yep. That runs into the income statement. Yep. This is a fundamental business model shift and an advantage of this kind of scenario. >>And that's why I don't think snowflakes, >>What's your reaction to that? Cuz that's something that, that is not really, talk's highly nuanced and situational. But if Goldman Sachs builds the biggest cloud on the planet for financial service for their own benefit, why wouldn't they >>Exactly. >>And they're >>Gonna build it. They sort of hinted at it that when they were up on stage on AWS, right. That is just their first big step. I'm pretty sure over time they would be using other clouds. Think >>They already are on >>Prem. Yeah. On prem. Exactly. They're using VMware technology there. Right? I mean think about it, AWS. I don't know how many billions of dollars they're spending on AWS R and D Microsoft is doing the same thing. Google's doing the same thing we are doing. Not as much as them that you're doing oral chair. Yeah. If you are a CIO, you would be insane not to take advantage of all of this IP that's getting created and say, look, I'm just gonna bet on one. Doesn't make any sense. Right. So that's what you're seeing. And then >>I think >>The really smart companies, like you talked about would say, look, I will do something for my industry that uses these underlying clouds as the substrate, but encapsulates my IP and my operating model that I then offer to other >>Partners. Yeah. And their incentive for differentiation is scale. Yeah. And capability. And that's a super cloud. That's a, or would be say it environment. >>Yeah. But this is why this, >>It seems like the same >>Game, but >>This, I mean, I think it environment is different than >>Well, I mean it advantage to help the business, the old day service, you >>Said snowflake guys out the marketing guys. So you, >>You said snowflake data warehouse. See, I don't think it's in data warehouse. It's not, that's like saying, you >>Know, I, over >>VMware is a virtualization company or service now is a help desk tool. I, this is the change. Yes. That's occurring. Yes. And that you're enabling. So take the Goldman Sachs example. They're gonna run OnPrem. They're gonna use your infrastructure to do selfer. They're gonna build on AWS CapEx. They're gonna go across clouds and they're gonna need some multi-cloud services. And that's your opportunity. >>Exactly. That's that's really, when you, in the keynote, I talked about cloud universal. Right? So think of a future where we can go to a customer and say, Mr. Customer buy thousand scores, a hundred thousand cores, whatever capacity you can use it, any which way you want on any application platform. Right. And it could be OnPrem. It could be in the cloud, in the cloud of their choice in multiple clouds. And this thing can be fungible and they can tie it to the right services. If they like SageMaker they could tie it to Sage or Aurora. They could tie it to Aurora, cetera, et cetera. So I think that's really the foundation that we are setting. Well, I think, I >>Mean, you're building a cloud across clouds. I mean, that's the way I look at it. And, and that's why it's, to me, the, the DPU announcement, the project Monterey coming to fruition is so important. Yeah. Because if you don't have that, if you're not on that new Silicon curve yep. You're gonna be left behind. Oh, >>Absolutely. It allows us to build things that you would not otherwise be able to do, >>Not to pat ourselves on the back Ragu. But we, in what, 2013 day we said, feel >>Free. >>We, we said with Lou Tucker when OpenStack was crashing. Yeah. Yeah. And then Kubernetes was just a paper. We said, this could be the interoperability layer. Yeah. You got it. And you could have inter clouding cuz there was no clouding. I was gonna riff on inter networking. But if you remember inter networking during the OSI model, TCP and IP were hardened after the physical data link layer was taken care of. So that enabled an entire new industry that was open, open interconnect. Right. So we were saying inter clouding. So what you're kind of getting at with cross cloud is you're kind of creating this routing model if you will. Not necessarily routing, but like connection inter clouding, we called it. I think it's kinda a terrible name. >>What you said about Kubernetes is super critical. It is turning out to be the infrastructure API so long. It has been an infrastructure API for a certain cluster. Right. But if you think about what we said about VSE eight with VSE eight Kubernetes becomes the data center API. Now we sort of glossed over the point of the keynote, but you could do operations storage, anything that you can do on vSphere, you can do using a Kubernetes API. Yeah. And of course you can do all the containers in the Kubernetes clusters and et cetera, is what you could always do. Now you could do that on a VMware environment. OnPrem, you could do that on EKS. Now Kubernetes has become the standard programming model for infrastructure across. It >>Was the great equalizer. Yeah. You, we used to say Amazon turned the data center through an API. It turns, turns of like a lot of APIs and a lot of complexity. Right. And Kubernetes changed. >>Well, the role, the role of defacto standards played a lot into the T C P I P revolution before it became a standard standard. What the question Raghu, as you look at, we had submit on earlier, we had tutorial on as well. What's the disruptive enabler from a defacto. What in your mind, what should, because Kubernetes became kind of defacto, even though it was in the CNCF and in an open source open, it wasn't really standard standard. There's no like standards, body, but what de facto thing has to happen in your mind's eye around making inter clouding or connecting clouds in a, in a way that's gonna create extensibility and growth. What do you see as a de facto thing that the industry should rally around? Obviously Kubernetes is one, is there something else that you see that's important for in an open way that the industry can discuss and, and get behind? >>Yeah. I mean, there are things like identity, right? Which are pretty critical. There is connectivity and networking. So these are all things that the industry can rally around. Right. And that goes along with any modern application infrastructure. So I would say those are the building blocks that need to happen on the data side. Of course there are so many choices as well. So >>How about, you know, security? I think about, you know, when after stuck net, the, the whole industry said, Hey, we have to do a better job of collaborating. And then when you said identity, it just sort of struck me. But then a lot of people tried to sort of monetize private reporting and things like that. So you do you see a movement within the technology industry to do a better job of collaborating to, to solve the acute, you know, security problems? >>Yeah. I think the customer pressure and government pressure right. Causes that way. Yeah. Even now, even in our current universe, you see, there is a lot of behind the scenes collaboration amongst the security teams of all of the tech companies that is not widely seen or known. Right. For example, my CISO knows the AWS CSO or the Microsoft CSO and they all talk and they share the right information about vulnerability attacks and so on and so forth. So there's already a certain amount of collaboration that's happening and that'll only increase. Do, >>Do you, you know, I was somewhat surprised. I didn't hear more in your face about security would, is that just because you had such a strong multi-cloud message that you wanted to get, get across, cuz your security story is very strong and deep. When you get into the DPU side of things, the, you know, the separation of resources and the encryption and I'll end to end >>I'm well, we have a phenomenal security story. Yeah. Yeah. Tell security story and yes. I mean I'll need guilty to the fact that in the keynote you have yeah, yeah, sure time. But what we are doing with NSX and you will hear about some NSX projects as you, if you have time to go to some of the, the sessions. Yeah. There's one called project, not star. Another is called project Watchman or watch, I think it's called, we're all dealing with this. That is gonna strengthen the security story even more. Yeah. >>We think security and data is gonna be a big part of it. Right. As CEO, I have to ask you now that you're the CEO, first of all, I'd love to talk about product with you cuz you're yeah. Yeah. We just great conversation. We want to kind of read thet leaves and ask pointed questions cuz we're putting the puzzle together in real time here with the audience. But as CEO, now you have a lot of discussions around the business. You, the Broadcom thing happening, you got the rename here, you got multi-cloud all good stuff happening. Dave and I were chatting before we came on this morning around the marketplace, around financial valuations and EBIDA numbers. When you have so much strategic Goodwill and investment in the oven right now with the, with the investments in cloud native multi-year investments on a trajectory, you got economies of scale there. >>It's just now coming out to be harvest and more behind it. Yeah. As you come into the Broadcom and or the new world wave that's coming, how do you talk about that value? Cuz you can't really put a number on it yet because there's no customers on it. I mean some customers, but you can't probably some for form. It's not like sales numbers. Yeah. Yeah. How do you make the argument to the PE type folks out there? Like EBIDA and then all the strategic value. What's the, what's the conversation like if you can share any, I know it's obviously public company, all the things going down, but like how do you talk about strategic value to numbers folks? >>Yeah. I mean, we are not talking to PE guys at all. Right. I mean the only conversation we have is helping Broadcom with >>Yeah. But, but number people who are looking at the number, EBIDA kind of, >>Yeah. I mean, you'd be surprised if, for, for example, even with Broadcom, they look at the business holistically as what are the prospects of this business becoming a franchise that is durable and could drive a lot of value. Right. So that's how they look at it holistically. It's not a number driven. >>They do. They look at that. >>Yeah. Yeah, absolutely. So I think it's a misperception to say, Hey, it's a numbers driven conversation. It's a business driven conversation where, I mean, and Hawk's been public about it. He says, look, I look at businesses. Can they be leaders in their market? Yeah. Because leaders get, as we all know a disproportionate share of the economic value, is it a durable franchise that's gonna last 10 years or more, right. Obviously with technology changes in between, but 10 years or more >>Or 10, you got your internal, VMware talent customers and >>Partners. Yeah. Significant competitive advantage. So that's, that's really where the conversation starts and the numbers fall out of it. Got it. >>Okay. So I think >>There's a track record too. >>That culture >>That VMware has, you've always had an engineering culture. That's turned, you know, ideas and problems into products that, that have been very successful. >>Well, they had different engineering cultures. They're chips. You guys are software. Right. You guys know >>Software. Yeah. Mean they've been very successful with Broadcom, the standalone networking company since they took it over. Right. I mean, it's, there's a lot of amazing innovation going on there. >>Yeah. Not, not that I'm smiling. I want to kind of poke at this question question. I'll see if I get an answer out of you, when you talk to Hawk tan, does he feel like he bought a lot more than he thought or does he, did he, does he know it's all here? So >>The last two months, I mean, they've been going through a very deliberate process of digging into each business and certainly feels like he got a phenomenal asset base. Yeah. He said that to me even today after the keynote, right. Is the amazing amount of product capability that he's seeing in every one of our businesses. And that's been the constant frame. >>But congratulations on that. >>I've heard, I've heard Hawk talk about the shift to, to Mer merchant Silicon. Yeah. From custom Silicon. But I wanted to ask you when you look at things like AWS nitro yeah. And graviton and train and the advantage that AWS has with custom Silicon, you see Google and Microsoft sort of Alibaba following suit. Would it benefit you to have custom Silicon for, for DPU? I mean, I guess you, you know, to have a tighter integration or do you feel like with the relationships that you have that doesn't buy you anything? >>Yeah. I mean we have pretty strong relationships with in fact fantastic relationships with the Invidia and Intel and AMD >>Benon and AMD now. >>Yeah. Yeah. I mean, we've been working with the Pendo team in their previous incarnations for years. Right, right. When they were at Cisco and then same thing with the, we know the Melanox team as well as the invi original teams and Intel is the collaboration right. From the get go of the company. So we don't feel a need for any of that. We think, I mean, it's clear for those cloud folks, right. They're going towards a vertical integration model and select portions of their stack, like you talked about, but there is always a room for horizontal integration model. Right. And that's what we are a part of. Right. So there'll be a number of DPU pro vendors. There'll be a number of CPU vendors. There'll be a number of other storage, et cetera, et cetera. And we think that is goodness in an alternative model compared to a vertically integr >>And yeah. What this trade offs, right. It's not one or the other, I mean I used to tell, talk to Al Shugar about this all the time. Right. I mean, if vertically integrated, there may be some cost advantages, but then you've got flexibility advantages. If you're using, you know, what the industry is building. Right. And those are the tradeoffs, so yeah. Yeah. >>Greg, what are you excited about right now? You got a lot going on obviously great event. Branding's good. Love the graphics. I was kind of nervous about the name changed. I likem world, but you know, that's, I'm kind of like it >>Doesn't readily roll off your phone. Yeah. >>I know. We, I had everyone miscue this morning already and said VMware Explorer. So >>You pay Laura fine. Yeah. >>Now, I >>Mean a quarter >>Curse jar, whatever I did wrong. I don't believe it. Only small mistake that's because the thing wasn't on. Okay. Anyway, what's on your plate. What's your, what's some of the milestones. Do you share for your employees, your customers and your partners out there that are watching that might wanna know what's next in the whole Broadcom VMware situation. Is there a timeline? Can you talk publicly about what? To what people can expect? >>Yeah, no, we, we talk all the time in the company about that. Right? Because even if there is no news, you need to talk about what is where we are. Right. Because this is such a big transaction and employees need to know where we are at every minute of the day. Right? Yeah. So, so we definitely talk about that. We definitely talk about that with customers too. And where we are is that the, all the processes are on track, right? There is a regulatory track going on. And like I alluded to a few minutes ago, Broadcom is doing what they call the discovery phase of the integration planning, where they learn about the business. And then once that is done, they'll figure out what the operating model is. What Broadcom is said publicly is that the acquisition will close in their fiscal 23, which starts in November of this year, runs through October of next year. >>So >>Anywhere window, okay. As to where it is in that window. >>All right, Raghu, thank you so much for taking valuable time out of your conference time here for the queue. I really appreciate Dave and I both appreciate your friendship. Congratulations on the success as CEO, cuz we've been following your trials and tribulations and endeavors for many years and it's been great to chat with you. >>Yeah. Yeah. It's been great to chat with you, not just today, but yeah. Over a period of time and you guys do great work with this, so >>Yeah. And you guys making, making all the right calls at VMware. All right. More coverage. I'm shot. Dave ante cube coverage day one of three days of world war cup here in Moscone west, the cube coverage of VMware Explorer, 22 be right back.