>> 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)

(upbeat music) >> Oracle continues to pursue a multi-mode converged database strategy. The premise of this all in one approach is to make life easier for practitioners and developers. And the most recent example is the Oracle database API for MongoDB, which was announced today. Now, Oracle, they're not the first to come out with a MongoDB compatible API, but Oracle hopes to use its autonomous database as a differentiator and further build a moat around OCI, Oracle Cloud Infrastructure. And with us to talk about Oracle's MongoDB compatible API is Gerald Venzl, who's a distinguished Product Manager at Oracle. Gerald was a guest along with Maria Colgan on the CUBE a while back, and we talked about Oracle's converge database and the kind of Swiss army knife strategy, I called it, of databases. This is dramatically different. It's an approach that we see at the opposite end of the the spectrum, for instance, from AWS, who, for example, goes after the world of developers with a different database for every use case. So, kind of picking up from there, Gerald, I wonder if you could talk about how this new MongoDB API adds to your converged model and the whole strategy there. Where does it fit? >> Yeah, thank you very much, Dave and, by the way, thanks for having me on the CUBE again. A pleasure to be here. So, essentially the MongoDB API to build the compatibility that we used with this API is a continuation of the converge database story, as you said before. Which is essentially bringing the many features of the many single purpose databases that people often like and use, together into one technology so that everybody can benefit from it. So as such, this is just a continuation that we have from so many other APIs or standards that we support. Since a long time, we already, of course to SQL because we are relational database from the get go. Also other standard like GraphQL, Sparkle, et cetera that we have. And the MongoDB API, is now essentially just the next step forward to give the developers this API that they've gotten to love and use. >> I wonder if you could talk about from the developer angle, what do they get out of it? Obviously you're appealing to the Mongo developers out there, but you've got this Mongo compatible API you're pouting the autonomous database on OCI. Why aren't they just going to use MongoDB Atlas on whatever cloud, Azure or AWS or Google Cloud platform? >> That's a very good question. We believe that the majority of developers want to just worry about their application, writing the application, and not so much about the database backend that they're using. And especially in cloud with cloud services, the reason why developers choose these services is so that they don't have to manage them. Now, autonomous database brings many topnotch advanced capabilities to database cloud services. We firmly believe that autonomous database is essentially the next generation of cloud services with all the self-driving features built in, and MongoDB developers writing applications against the MongoDB API, should not have to hold out on these capabilities either. It's like no developer likes to tune the database. No developer likes to take a downtime when they have to rescale their database to accommodate a bigger workload. And this is really where we see the benefit here, so for the developer, ideally nothing will change. You have MongoDB compatible API so they can keep on using their tools. They can build the applications the way that they do, but the benefit from the best cloud database service out there not having to worry about any of these package things anymore, that even MongoDB Atlas has a lot of shortcomings still today, as we find. >> Of cos, this is always a moving target The technology business, that's why we love it. So everybody's moving fast and investing and shaking and jiving. But, I want to ask you about, well, by the way, that's so you're hiding the underlying complexity, That's really the big takeaway there. So that's you huge for developers. But take, I was talking before about, the Amazon's approach, right tool for the right job. You got document DB, you got Microsoft with Cosmos, they compete with Mongo and they've been doing so for some time. How does Oracle's API for Mongo different from those offerings and how you going to attract their users to your JSON offering. >> So, you know, for first of all we have to kind of separate slightly document DB and AWS and Cosmos DB in Azure, they have slightly different approaches there. Document DB essentially is, a document store owned by and built by AWS, nothing different to Mongo DB, it's a head to head comparison. It's like use my document store versus the other document store. So you don't get any of the benefits of a converge database. If you ever want to do a different data model, run analytics over, etc. You still have to use the many other services that AWS provides you to. You cannot all do it into one database. Now Cosmos DB it's more in interesting because they claim to be a multi-model database. And I say claim because what we understand as multi-model database is different to what they understand as multimodel database. And also one of the reasons why we start differentiating with converge database. So what we mean is you should be able to regardless what data format you want to store in the database leverage all the functionality of the database over that data format, with no trade offs. Cosmos DB when you look at it, it essentially gives you mode of operation. When you connect as the application or the user, you have to decide at connection time, how you want, how this database should be treated. Should it be a document store? Should it be a graph store? Should it be a relational store? Once you make that choice, you are locked into that. As long as you establish that connection. So it's like, if you say, I want a document store, all you get is a document store. There's no way for you to crossly analyze with the relational data sitting in the same service. There's no for you to break these boundaries. If you ever want to add some graph data and graph analytics, you essentially have to disconnect and now treat it as a graph store. So you get multiple data models in it, but really you still get, one trick pony the moment you connect to it that you have to choose to. And that is where we see a huge differentiation again with our converge database, because we essentially say, look, one database cloud service on Oracle cloud, where it allows you to do anything, if you wish to do so. You can start as a document store if you wish to do so. If you want to write some SQL queries on top, you can do so. If you want to add some graph data, you can do so. But there's no way for you to have to rewrite your application, use different libraries and frameworks now to connect et cetera, et cetera. >> Got it. Thank you for that. Do you have any data when you talk to customers? Like I'm interested in the diversity of deployments, like for instance, how many customers are using more than one data model? Do for instance, do JSON users need support for other data types or are they happy to stay kind of in their own little sandbox? Do you have any data on that? >> So what we see from the majority of our customers, there is no such thing as one data model fits everything. So, and it's like, there again we have to differentiate the developer that builds a certain microservice, that makes happy to stay in the JSON world or relational world, or the company that's trying to derive value from the data. So it's like the relational model has not gone away since 40 years of it existence. It's still kicking strong. It's still really good at what it does. The JSON data model is really good in what it does. The graph model is really good at what it does. But all these models have been built for different purposes. Try to do graph analytics on relational or JSON data. It's like, it's really tricky, but that's why you use a graph model to begin with. Try to shield yourself from the organization of the data, how it's structured, that's really easy in the relational world, not so much when you get into a document store world. And so what we see about our customers is like as they accumulate more data, is they have many different applications to run their enterprises. The question always comes back, as we have predicted since about six, seven years now, where they say, hey, we have all this different data and different data formats. We want to bring it all together, analyze it together, get value out of the data together. We have seen a whole trend of big data emerge and disappear to answer the question and didn't quite do the trick. And we are basically now back to where we were in the early 2000's when XML databases have faded away, because everybody just allowed you to store XML in the database. >> Got it. So let's make this real for people. So maybe you could give us some examples. You got this new API from Mongo, you have your multi model database. How, take a, paint a picture of how customers are going to benefit in real world use cases. How does it kind of change the customer's world before and after if you will? >> Yeah, absolutely. So, you know the API essentially we are going to use it to accept before, you know, make the lives of the developers easier, but also of course to assist our customers with migrations from Mongo DB over to Oracle Autonomous Database. One customer that we have, for example, that would've benefited of the API several a couple of years ago, two, three years ago, it's one of the largest logistics company on the planet. They track every package that is being sent in JSON documents. So every track package is entries resembled in a JSON document, and they very early on came in with the next question of like, hey, we track all these packages and document in JSON documents. It will be really nice to know actually which packages are stuck, or anywhere where we have to intervene. It's like, can we do this? Can we analyze just how many packages get stuck, didn't get delivered on, the end of a day or whatever. And they found this struggle with this question a lot, they found this was really tricky to do back then, in that case in MongoDB. So they actually approached Oracle, they came over, they migrated over and they rewrote their applications to accommodate that. And there are happy JSON users in Oracle database, but if we were having this API already for them then they wouldn't have had to rewrite their applications or would we often see like worry about the rewriting the application later on. Usually migration use cases, we want to get kind of the migration done, get the data over be running, and then worry about everything else. So this would be one where they would've greatly benefited to shorten this migration time window. If we had already demo the Mongo API back then or this compatibility layer. >> That's a good use case. I mean, it's, one of the most prominent and painful, so anything you could do to help that is key. I remember like the early days of big data, NoSQL, of course was the big thing. There was a lot of confusion. No, people thought was none or not only SQL, which is kind of the more widely accepted interpretation today. But really, it's talking about data that's stored in a non-relational format. So, some people, again they thought that SQL was going to fade away, some people probably still believe that. And, we saw the rise of NoSQL and document databases, but if I understand it correctly, a premise for your Mongo DB API is you really see SQL as a main contributor over Mongo DB's document collections for analytics for example. Can you make, add some color here? Are you seeing, what are you seeing in terms of resurgence of SQL or the momentum in SQL? Has it ever really waned? What's your take? >> Yeah, no, it's a very good point. So I think there as well, we see to some extent history repeating itself from, this all has been tried beforehand with object databases, XML database, et cetera. But if we stay with the NoSQL databases, I think it speaks at length that every NoSQL database that as you write for the sensor you started with NoSQL, and then while actually we always meant, not only SQL, everybody has introduced a SQL like engine or interface. The last two actually join this family is MongoDB. Now they have just recently introduced a SQL compatibility for the aggregation pipelines, something where you can put in a SQL statement and that essentially will then work with aggregation pipeline. So they all acknowledge that SQL is powerful, for us this was always clear. SQL is a declarative language. Some argue it's the only true 4GL language out there. You don't have to code how to get the data, but you just ask the question and the rest is done for you. And, we think that as we, basically, has SQL ever diminished as you said before, if you look out there? SQL has always been a demand. Look at the various developer surveys, etc. The various top skills that are asked for SQL has never gone away. Everybody loves and likes and you wants to use SQL. And so, yeah, we don't think this has ever been, going away. It has maybe just been, put in the shadow by some hypes. But again, we had the same discussion in the 2000's with XML databases, with the same discussions in the 90's with object databases. And we have just frankly, all forgotten about it. >> I love when you guys come on and and let me do my thing and I can pretty much ask any question I want, because, I got to say, when Oracle starts talking about another company I know that company's doing well. So I like, I see Mongo in the marketplace and I love that you guys are calling it out and making some moves there. So here's the thing, you guys have a large install base and that can be an advantage, but it can also be a weight in your shoulder. These specialized cloud databases they don't have that legacy. So they can just kind of move freely about, less friction. Now, all the cloud database services they're going to have more and more automation. I mean, I think that's pretty clear and inevitable. And most if not all of the database vendors they're going to provide support for these kind of converged data models. However they choose to do that. They might do it through the ecosystem, like what Snowflake's trying to do, or bring it in the house themselves, like a watch maker that brings an in-house movement, if you will. But it's like death and taxes, you can't avoid it. It's got to happen. That's what customers want. So with all that being said, how do you see the capabilities that you have today with automation and converge capabilities, How do you see that, that playing out? What's, do you think it gives you enough of an advantage? And obviously it's an advantage, but is it enough of an advantage over the specialized cloud database vendors, where there's clearly a lot of momentum today? >> I mean, honestly yes, absolutely. I mean, we are with some of these databases 20 years ahead. And I give you concrete examples. It's like Oracle had transaction support asset transactions since forever. NoSQL players all said, oh, we don't need assets transactions, base transactions is fine. Yada, yada, yada. Mongo DB started introducing some transaction support. It comes with some limits, cannot be longer than 60 seconds, cannot touch more than a thousand documents as well, et cetera. They still will have to do some catching up there. I mean, it took us a while to get there, let's be honest. Glad We have been around for a long time. Same thing, now that happened with version five, is like we started some simple version of multi version concurrency control that comes along with asset transactions. The interesting part here is like, we've introduced this also an Oracle five, which was somewhere in the 80's before I even started using Oracle Database. So there's a lot of catching up to do. And then you look at the cloud services as well, there's actually certain, a lot of things that we kind of gotten take, we've kind of, we Oracle people have taken for granted and we kind of keep forgetting. For example, our elastic scale, you want to add one CPU, you add one CPU. Should you take downtime for that? Absolutely not. It's like, this is ridiculous. Why would you, you cannot take it downtime in a 24/7 backend system that runs the world. Take any of our customers. If you look at most of these cloud services or you want to reshape, you want to scale your cloud service, that's fine. It's just the VM under the covers, we just shut everything down, give you a VM with more CPU, and you boot it up again, downtown right there. So it's like, there's a lot of these things where we go like, well, we solved this frankly decades ago, that these cloud vendors will run into. And just to add one more point here, so it's like one thing that we see with all these migrations happening is exactly in that field. It's like people essentially started building on whether it's Mongo DB or other of these NoSQL databases or cloud databases. And eventually as these systems grow, as they ask more difficult questions, their use cases expand, they find shortcomings. Whether it's the scalability, whether it's the security aspects, the functionalities that we have, and this is essentially what drives them back to Oracle. And this is why we see essentially this popularity now of pendulum swimming towards our direction again, where people actually happily come over back and they come over to us, to get their workloads enterprise grade if you like. >> Well, It's true. I mean, I just reported on this recently, the momentum that you guys have in cloud because it is, 'cause you got the best mission critical database. You're all about maps. I got to tell you a quick story. I was at a vertical conference one time, I was on stage with Kurt Monash. I don't know if you know Kurt, but he knows this space really well. He's probably forgot and more about database than I'll ever know. But, and I was kind of busting his chops. He was talking about asset transactions. I'm like, well with NoSQL, who needs asset transactions, just to poke him. And he was like, "Are you out of your mind?" And, and he said, look it's everybody is going to head in this direction. It turned out, it's true. So I got to give him props for that. And so, my last question, if you had a message for, let's say there's a skeptical developer out there that's using Mongo DB and Atlas, what would you say to them? >> I would say go try it for yourself. If you don't believe us, we have an always free cloud tier out there. You just go to oracle.com/cloud/free. You sign up for an always free tier, spin up an autonomous database, go try it for yourself. See what's actually possible today. Don't just follow your trends on Hackernews and use a set study here or there. Go try it for yourself and see what's capable of >> All right, Gerald. Hey, thanks for coming into my firing line today. I really appreciate your time. >> Thank you for having me again. >> Good luck with the announcement. You're very welcome, and thank you for watching this CUBE conversation. This is Dave Vellante, We'll see you next time. (gentle music)

>> Narrator: From theCUBE Studios in Palo Alto, in Boston bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> The next 10 years of cloud, they're going to differ dramatically from the past decade. The early days of cloud, deployed virtualization of standard off-the-shelf components, X86 microprocessors, disk drives et cetera, to then scale out and build a large distributed system. The coming decade is going to see a much more data-centric, real-time, intelligent, call it even hyper-decentralized cloud that will comprise on-prem, hybrid, cross-cloud and edge workloads with a services layer that will obstruct the underlying complexity of the infrastructure which will also comprise much more custom and varied components. This was a key takeaway of the guests from theCUBE on Cloud, an event hosted by SiliconANGLE on theCUBE. Welcome to this week's Wikibon CUBE Insights Powered by ETR. In this episode, we'll summarize the findings of our recent event and extract the signal from our great guests with a couple of series and comments and clips from the show. CUBE on Cloud is our very first virtual editorial event. It was designed to bring together our community in an open forum. We ran the day on our 365 software platform and had a great lineup of CEOs, CIOs, data practitioners technologists. We had cloud experts, analysts and many opinion leaders all brought together in a day long series of sessions that we developed in order to unpack the future of cloud computing in the coming decade. Let me briefly frame up the conversation and then turn it over to some of our guests. First, we put forth our view of how modern cloud has evolved and where it's headed. This graphic that we're showing here, talks about the progression of cloud innovation over time. A cloud like many innovations, it started as a novelty. When AWS announced S3 in March of 2006, nobody in the vendor or user communities really even in the trade press really paid too much attention to it. Then later that year, Amazon announced EC2 and people started to think about a new model of computing. But it was largely tire kickers, bleeding-edge developers that took notice and really leaned in. Now the financial crisis of 2007 to 2009, really created what we call a cloud awakening and it put cloud on the radar of many CFOs. Shadow IT emerged within departments that wanted to take IT in bite-sized chunks and along with the CFO wanted to take it as OPEX versus CAPEX. And then I teach transformation that really took hold. We came out of the financial crisis and we've been on an 11-year cloud boom. And it doesn't look like it's going to stop anytime soon, cloud has really disrupted the on-prem model as we've reported and completely transformed IT. Ironically, the pandemic hit at the beginning of this decade, and created a mandate to go digital. And so it accelerated the industry transformation that we're highlighting here, which probably would have taken several more years to mature but overnight the forced March to digital happened. And it looks like it's here to stay. Now the next wave, we think we'll be much more about business or industry transformation. We're seeing the first glimpses of that. Holger Mueller of Constellation Research summed it up at our event very well I thought, he basically said the cloud is the big winner of COVID. Of course we know that now normally we talk about seven-year economic cycles. He said he was talking about for planning and investment cycles. Now we operate in seven-day cycles. The examples he gave where do we open or close the store? How do we pivot to support remote workers without the burden of CAPEX? And we think that the things listed on this chart are going to be front and center in the coming years, data AI, a fully digitized and intelligence stack that will support next gen disruptions in autos, manufacturing, finance, farming and virtually every industry where the system will expand to the edge. And the underlying infrastructure across physical locations will be hidden. Many issues remain, not the least of which is latency which we talked about at the event in quite some detail. So let's talk about how the Big 3 cloud players are going to participate in this next era. Well, in short, the consensus from the event was that the rich get richer. Let's take a look at some data. This chart shows our most recent estimates of IaaS and PaaS spending for the Big 3. And we're going to update this after earning season but there's a couple of points stand out. First, we want to make the point that combined the Big 3 now account for almost $80 billion of infrastructure spend last year. That $80 billion, was not all incremental (laughs) No it's caused consolidation and disruption in the on-prem data center business and within IT shops companies like Dell, HPE, IBM, Oracle many others have felt the heat and have had to respond with hybrid and cross cloud strategies. Second while it's true that Azure and GCP they appear to be growing faster than AWS. We don't know really the exact numbers, of course because only AWS provides a clean view of IaaS and passwords, Microsoft and Google. They kind of hide them all ball on their numbers which by the way, I don't blame them but they do leave breadcrumbs and clues on growth rates. And we have other means of estimating through surveys and the like, but it's undeniable Azure is closing the revenue gap on AWS. The third is that I like the fact that Azure and Google are growing faster than AWS. AWS is the only company by our estimates to grow its business sequentially last quarter. And in and of itself, that's not really enough important. What is significant is that because AWS is so large now at 45 billion, even at their slower growth rates it grows much more in absolute terms than its competitors. So we think AWS is going to keep its lead for some time. We think Microsoft and AWS will continue to lead the pack. You know, they might converge maybe it will be a 200 just race in terms of who's first who's second in terms of cloud revenue and how it's counted depending on what they count in their numbers. And Google look with its balance sheet and global network. It's going to play the long game and virtually everyone else with the exception of perhaps Alibaba is going to be secondary players on these platforms. Now this next graphic underscores that reality and kind of lays out the competitive landscape. What we're showing here is survey data from ETR of more than 1400 CIOs and IT buyers and on the vertical axis is Net Score which measures spending momentum on the horizontal axis is so-called Market Share which is a measure of pervasiveness in the data set. The key points are AWS and Microsoft look at it. They stand alone so far ahead of the pack. I mean, they really literally, it would have to fall down to lose their lead high spending velocity and large share of the market or the hallmarks of these two companies. And we don't think that's going to change anytime soon. Now, Google, even though it's far behind they have the financial strength to continue to position themselves as an alternative to AWS. And of course, an analytics specialist. So it will continue to grow, but it will be challenged. We think to catch up to the leaders. Now take a look at the hybrid zone where the field is playing. These are companies that have a large on-prem presence and have been forced to initiate a coherent cloud strategy. And of course, including multicloud. And we include Google in this so pack because they're behind and they have to take a differentiated approach relative to AWS, and maybe cozy up to some of these traditional enterprise vendors to help Google get to the enterprise. And you can see from the on-prem crowd, VMware Cloud on AWS is stands out as having some, some momentum as does Red Hat OpenShift, which is it's cloudy, but it's really sort of an ingredient it's not really broad IaaS specifically but it's a component of cloud VMware cloud which includes VCF or VMware Cloud Foundation. And even Dell's cloud. We would expect HPE with its GreenLake strategy. Its financials is shoring up, should be picking up momentum in the future in terms of what the customers of this survey consider cloud. And then of course you could see IBM and Oracle you're in the game, but they don't have the spending momentum and they don't have the CAPEX chops to compete with the hyperscalers IBM's cloud revenue actually dropped 7% last quarter. So that highlights the challenges that that company facing Oracle's cloud business is growing in the single digits. It's kind of up and down, but again underscores these two companies are really about migrating their software install basis to their captive clouds and as well for IBM, for example it's launched a financial cloud as a way to differentiate and not take AWS head-on an infrastructure as a service. The bottom line is that other than the Big 3 in Alibaba the rest of the pack will be plugging into hybridizing and cross-clouding those platforms. And there are definitely opportunities there specifically related to creating that abstraction layer that we talked about earlier and hiding that underlying complexity and importantly creating incremental value good examples, snowfallLike what snowflake is doing with its data cloud, what the data protection guys are doing. A company like Loomio is headed in that direction as are others. So, you keep an eye on that and think about where the white space is and where the value can be across-clouds. That's where the opportunity is. So let's see, what is this all going to look like? How does the cube community think it's going to unfold? Let's hear from theCUBE Guests and theCUBE on Cloud speakers and some of those highlights. Now, unfortunately we don't have time to show you clips from every speaker. We are like 10-plus hours of video content but we've tried to pull together some comments that summarize the sentiment from the community. So I'm going to have John Furrier briefly explain what theCUBE on Cloud is all about and then let the guests speak for themselves. After John, Pradeep Sindhu is going to give a nice technical overview of how the cloud was built out and what's changing in the future. I'll give you a hint it has to do with data. And then speaking of data, Mai-Lan Bukovec, who heads up AWS is storage portfolio. She'll explain how she views the coming changes in cloud and how they look at storage. Again, no surprise, it's all about data. Now, one of the themes that you'll hear from guests is the notion of a distributed cloud model. And Zhamak Deghani, he was a data architect. She'll explain her view of the future of data architectures. We also have thoughts from analysts like Zeus Karavalla and Maribel Lopez, and some comments from both Microsoft and Google to compliment AWS's view of the world. In fact, we asked JG Chirapurath from Microsoft to comment on the common narrative that Microsoft products are not best-to-breed. They put out a one dot O and then they get better, or sometimes people say, well, they're just good enough. So we'll see what his response is to that. And Paul Gillin asks, Amit Zavery of Google his thoughts on the cloud leaderboard and how Google thinks about their third-place position. Dheeraj Pandey gives his perspective on how technology has progressed and been miniaturized over time. And what's coming in the future. And then Simon Crosby gives us a framework to think about the edge as the most logical opportunity to process data not necessarily a physical place. And this was echoed by John Roese, and Chris Wolf to experience CTOs who went into some great depth on this topic. Unfortunately, I don't have the clips of those two but their comments can be found on the CTO power panel the technical edge it's called that's the segment at theCUBE on Cloud events site which we'll share the URL later. Now, the highlight reel ends with CEO Joni Klippert she talks about the changes in securing the cloud from a developer angle. And finally, we wrap up with a CIO perspective, Dan Sheehan. He provides some practical advice on building on his experience as a CIO, COO and CTO specifically how do you as a business technology leader deal with the rapid pace of change and still be able to drive business results? Okay, so let's now hear from the community please run the highlights. >> Well, I think one of the things we talked about COVID is the personal impact to me but other people as well one of the things that people are craving right now is information, factual information, truth, textures that we call it. But here this event for us Dave is our first inaugural editorial event. Rob, both Kristen Nicole the entire cube team, SiliconANGLE on theCUBE we're really trying to put together more of a cadence. We're going to do more of these events where we can put out and feature the best people in our community that have great fresh voices. You know, we do interview the big names Andy Jassy, Michael Dell, the billionaires of people making things happen, but it's often the people under them that are the real Newsmakers. >> If you look at the architecture of cloud data centers the single most important invention was scale-out. Scale-out of identical or near identical servers all connected to a standard IP ethernet network. That's the architecture. Now the building blocks of this architecture is ethernet switches which make up the network, IP ethernet switches. And then the server is all built using general purpose x86 CPU's with DRAM, with SSD, with hard drives all connected to inside the CPU. Now, the fact that you scale these server nodes as they're called out was very, very important in addressing the problem of how do you build very large scale infrastructure using general purpose compute but this architecture, Dave is a compute centric architecture. And the reason it's a compute centric architecture is if you open this, is server node. What you see is a connection to the network typically with a simple network interface card. And then you have CPU's which are in the middle of the action. Not only are the CPU's processing the application workload but they're processing all of the IO workload what we call data centric workload. And so when you connect SSDs and hard drives and GPU is everything to the CPU, as well as to the network you can now imagine that the CPU is doing two functions. It's running the applications but it's also playing traffic cop for the IO. So every IO has to go to the CPU and you're executing instructions typically in the operating system. And you're interrupting the CPU many many millions of times a second. Now general purpose CPU and the architecture of the CPU's was never designed to play traffic cop because the traffic cop function is a function that requires you to be interrupted very, very frequently. So it's critical that in this new architecture where does a lot of data, a lot of these stress traffic the percentage of workload, which is data centric has gone from maybe one to 2% to 30 to 40%. >> The path to innovation is paved by data. If you don't have data, you don't have machine learning you don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data Lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> We are actually moving towards decentralization if we think today, like if it let's move data aside if we said is the only way web would work the only way we get access to various applications on the web or pages to centralize it We would laugh at that idea. But for some reason we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organizations that are beyond the bounds of organization. And then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in, you know, globally in place then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me that requires a paradigm shift a full stack from how we organize our organizations how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> I actually think we're in the midst of the transition to what's called a distributed cloud, where if you look at modernized cloud apps today they're actually made up of services from different clouds. And also distributed edge locations. And that's going to have a pretty profound impact on the way we go vast. >> We wake up every day, worrying about our customer and worrying about the customer condition and to absolutely make sure we dealt with the best in the first attempt that we do. So when you take the plethora of products we've dealt with in Azure, be it Azure SQL be it Azure cosmos DB, Synapse, Azure Databricks, which we did in partnership with Databricks Azure machine learning. And recently when we sort of offered the world's first comprehensive data governance solution and Azure overview, I would, I would humbly submit to you that we are leading the way. >> How important are rankings within the Google cloud team or are you focused mainly more on growth and just consistency? >> No, I don't think again, I'm not worried about we are not focused on ranking or any of that stuff. Typically I think we are worried about making sure customers are satisfied and the adding more and more customers. So if you look at the volume of customers we are signing up a lot of the large deals we did doing. If you look at the announcement we've made over the last year has been tremendous momentum around that. >> The thing that is really interesting about where we have been versus where we're going is we spend a lot of time talking about virtualizing hardware and moving that around. And what does that look like? And creating that as more of a software paradigm. And the thing we're talking about now is what does cloud as an operating model look like? What is the manageability of that? What is the security of that? What, you know, we've talked a lot about containers and moving into different, DevSecOps and all those different trends that we've been talking about. Like now we're doing them. So we've only gotten to the first crank of that. And I think every technology vendor we talked to now has to address how are they are going to do a highly distributed management insecurity landscape? Like, what are they going to layer on top of that? Because it's not just about, oh, I've taken a rack of something, server storage, compute, and virtualized it. I know have to create a new operating model around it in a way we're almost redoing what the OSI stack looks like and what the software and solutions are for that. >> And the whole idea of we in every recession we make things smaller. You know, in 91 we said we're going to go away from mainframes into Unix servers. And we made the unit of compute smaller. Then in the year, 2000 windows the next bubble burst and the recession afterwards we moved from Unix servers to Wintel windows and Intel x86 and eventually Linux as well. Again, we made things smaller going from million dollar servers to $5,000 servers, shorter lib servers. And that's what we did in 2008, 2009. I said, look, we don't even need to buy servers. We can do things with virtual machines which are servers that are an incarnation in the digital world. There's nothing in the physical world that actually even lives but we made it even smaller. And now with cloud in the last three, four years and what will happen in this coming decade. They're going to make it even smaller not just in space, which is size, with functions and containers and virtual machines, but also in time. >> So I think the right way to think about edges where can you reasonably process the data? And it obviously makes sense to process data at the first opportunity you have but much data is encrypted between the original device say and the application. And so edge as a place doesn't make as much sense as edge as an opportunity to decrypt and analyze it in the care. >> When I think of Shift-left, I think of that Mobius that we all look at all of the time and how we deliver and like plan, write code, deliver software, and then manage it, monitor it, right like that entire DevOps workflow. And today, when we think about where security lives, it either is a blocker to deploying production or most commonly it lives long after code has been deployed to production. And there's a security team constantly playing catch up trying to ensure that the development team whose job is to deliver value to their customers quickly, right? Deploy as fast as we can as many great customer facing features. They're then looking at it months after software has been deployed and then hurrying and trying to assess where the bugs are and trying to get that information back to software developers so that they can fix those issues. Shifting left to me means software engineers are finding those bugs as they're writing code or in the CIC CD pipeline long before code has been deployed to production. >> During this for quite a while now, it still comes down to the people. I can get the technology to do what it needs to do as long as they have the right requirements. So that goes back to people making sure we have the partnership that goes back to leadership and the people and then the change management aspects right out of the gate, you should be worrying about how this change is going to be how it's going to affect, and then the adoption and an engagement, because adoption is critical because you can go create the best thing you think from a technology perspective. But if it doesn't get used correctly, it's not worth the investment. So I agree, what is a digital transformation or innovation? It still comes down to understand the business model and injecting and utilizing technology to grow our reduce costs, grow the business or reduce costs. >> Okay, so look, there's so much other content on theCUBE on Cloud events site we'll put the link in the description below. We have other CEOs like Kathy Southwick and Ellen Nance. We have the CIO of UI path. Daniel Dienes talks about automation in the cloud and Appenzell from Anaplan. And a plan is not her company. By the way, Dave Humphrey from Bain also talks about his $750 million investment in Nutanix. Interesting, Rachel Stevens from red monk talks about the future of software development in the cloud and CTO, Hillary Hunter talks about the cloud going vertical into financial services. And of course, John Furrier and I along with special guests like Sergeant Joe Hall share our take on key trends, data and perspectives. So right here, you see the coupon cloud. There's a URL, check it out again. We'll, we'll pop this URL in the description of the video. So there's some great content there. I want to thank everybody who participated and thank you for watching this special episode of theCUBE Insights Powered by ETR. This is Dave Vellante and I'd appreciate any feedback you might have on how we can deliver better event content for you in the future. We'll be doing a number of these and we look forward to your participation and feedback. Thank you, all right, take care, we'll see you next time. (upbeat music)

>> Live from Orlando, Florida, it's theCUBE, covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back to theCUBE'S coverage of Microsoft Ignite 2018 here in Orlando. I'm Stu Miniman, and happy to welcome back to the program John Mracek who's the CEO of a Imanis Data. It's our first time at the show, but not your first time on theCube. Thanks so much for joining us and tell us we caught up with you in New York City talking about kind of the AI, analytics, all those things there. what what what brings a Imanis to Microsoft Ignite? >> So this has been a great show for us. And what I really see happening here is there's a vibrancy that probably didn't exist in Microsoft events, maybe four or five years ago. Because Microsoft really getting their act together on the whole how you migrate and bring people to the Azure. Right, because that's their agenda. And so where we fit in there is in our data management platform. We help customers migrate to a Azure. So whether it's moving your Hadoop workloads to Azure, or one of the products that's been featured here that we've gotten a lot of Microsoft support on is our migration tool to move from MongoDB to Cosmo DB. So we play really well into the migration story and it really leverages our platform. >> Yeah, one of the questions we talk about all the time is customers trying to figure out where things live and, well, it's like your cloud strategy. Things are changing over time. Customers have really multi-cloud environments, which really means they're doing a lot of different things and a lot of times they need to move them and sort those out. So what are the challenges you're seeing? How do you help those businesses make decisions today and be able to move things as needed in the future? >> Yeah, what we see and what we're playing into is really this evolution. You know, solutions really drive technologies. So in a large enterprise, you might have a division or a particular group that says, I need this BI or analytics tool and I need a big data platform to do it. So they build this. They build on top of some either NoSQL or Hadoop and then they've got this great solution. Well, that happens four or five times across the enterprise, and at some point in the enterprise, the CIO or somebody says, "you know, "we kind of got all these distributed data systems, "and like, who's managing them? "How is that data being moved "to your point about cloud migration? "Well, these are on-prem, these are in the cloud. "We want to put them all in the cloud, how do we do that?" And so that's where we're seeing as kind of the call for our product, which is, okay, I need a central way to manage and manipulate this data, as a fundamental problem. >> Yeah, so we all know that data is fundamental to a business. It's one of the most important things. We can use all the tropes of, it's the new oil or anything like that. But when you dig down, it's a lot of complexity into how, how do I get data? How do I manage data? How do I share data? We're sitting here in Cohesity, is the where we are in the booth. Can you help us understand, what are the solutions that you complement in the data space? What are the solutions that you replace? or a modern version or compete against in this space? >> So the way to look at us, we're at our most general, we're a platform for moving data from one platform to another. Okay, and that has many different use cases. But where we're getting a lot of customer uptake is on the backup recovery. It's like, I've got it here, I want to make a backup. We also see a lot in terms of migration, whether it's the Mongo to DB or I want to move from on-prem to cloud or cloud to cloud. And where we fit is if you look there's a legacy providers who don't traditionally go after the NoSQL and Hadoop space. And so where were a perfect complement to either those companies or folks like Cohesity. We have partnerships with Cohesity, Veem and others where they get in RFP or they're talking to a customer and the customer has a specific request for data management solution for NoSQL or Hadoop platforms. And that's where we come in. Because that's what we focused on exclusively from day one. >> Yeah, well, being at a Microsoft show, I mean, applications are central to so much and Microsoft does. Everything from Office, but on the data side, we spend a lot of time this week talking about SQL. Talking about Cosmos DB and cool new things they're doing. And of course, Microsoft's playing in a lot of the modern areas. We see them, big developers base here, even more of it at the Microsoft Build show, what do you see in the Microsoft space on the application modernization? Sounds like that would tie in quite a bit to what you're helping customers with. >> Yeah, so we have customers across all the cloud providers. But what we see in the Microsoft case is really people looking for maybe global easy deployment, customer facing as typical examples. So people who are really pushing the envelope, frankly. And there's almost like a bi-modal distribution. There's kind of some folks who are still trying to retrofit the old world and then others who are really embracing some of the new platforms. >> I'm not sure if you were at the keynote on Monday, Satya Nadella unveiled the Open Data Initiative. We've got Adobe and SAP and Microsoft there. I was talking to one analyst and reading some reports, and I'm like, well, it's not a coincidence that this was launched the week of Salesforce. Salesforce has a lot of data. Maybe that's a little bit of an attack there. But data across these big providers is important. I want to be able to share and leverage my data. You're in the data business. But what viewpoint you have of some of these really big providers of the application as they're going through their digital transformation, and making how do customers get the best value out of their data? >> So, my background, most recent background, I was in an ad tech company, where we're all big data. And the whole play there, is how do you manage your audiences, right? How do you have a unifying way to look at audiences? And so this is what's playing out on a more higher level, a more general level of how do I normalize and create a unified view of the customer and consistent data so that I can then manage it. And so that's an essential requirement to get the maximum value at out of that. Once you have that and you're in your data repositories, it's incredibly critical to protect them, to be able to orchestrate and move around. Where we fit in and how we see it is, these things are data, to reuse the term is the new oil and the new gold. And companies are realizing that it's really time to protect this data. I put all this investment into getting unified view of data. Wow, what are we doing about how do we back it up, restore it and move it? >> It's interesting, I've watched the space long enough. You go back kind of BI and DW days, go through big data. Now, we talk about a lot more of the analytics in the intelligence there. Help us as to, what are we actually realizing today that we were been talking about for years, and what what are still some of the stumbling blocks as to what we need to mature as an industry to really help unlock data. >> So, I mean, there's clearly the, what's driven a lot of the machine learning AI is the availability of data. It wasn't so much algorithms change dramatically, it was, we have a, so all the machine learning applications are really benefiting from this. But what we see as you know, some of the immediate things with our customers, is they're using big data as they create their front ends, engage with their customers. So how do they have the most up to date, real-time information to whether it's present an offer to a customer, provide customer service. So a lot of the use case we see is in that really bread and butter customer-level interactions and having an appropriate database to front end that process. >> Alright, so one of the biggest challenges of our time is really talking about distributed architecture. When I talk to companies scale comes on a lot, but it means very different things to different people. Can maybe talk about what you're hearing from customers, and how your solution helps customers for a variety of implementations. >> Yeah, so, we typically are targeting and working with customers in the 10s to 100s of terabytes. Up to, and our system handles up into into the petabytes. Typically, what we see is an evolution is, as I said earlier, somebody will develop a solution in a particular division, and then realize we've got this asset to protect. And then so IT starts to get involved and basically look at it holistically. So, we had one of our prospects, we went in and pitched at an SVP level and said, "what are the problems you're facing?" and it was basically this, I have all these silos of data. To get the maximum value out of them, and have a uniform look, whether it's look at our customers, the market, I need a uniform view to do my BI and AI. And so they brought us in and said, "Okay, paint a picture of how I can continue to have "these groups run autonomously and run their solutions, "yet at the same time, give me a unified view "and make me feel comfortable "that I've been able to protect the data, "move the data, massage the data." >> Great. Talk to me, when I look at this show, I see a lot of customers are still doing things, I'm trying to think how to say it nicely. Kind of the old way, it's like, if you look at them five years ago, is like, okay, Windows 2019's there great. I'll get there in five years, you play with a lot of more modern applications. What do you hear from customers? What, what is the profile of a customer that is, taking advantage and being competitive in the world? And what do you advise companies that maybe are a little bit behind the eight ball. >> So, you're right, and there's a really big spectrum of where people are in the adoption curve. And the way we look at it, if people were waiting for it, you know, when somebody goes, "Yeah, we're looking at setting up a big data system", it's like, okay, we'll talk to you in a year once you get the basics set up. But I see kind of two types of things. There's, say, the smaller, more aggressive companies, who are willing to move forward and say, "I just got to create a product, I don't care how I do it, "I don't have legacy issues." And they've moved ahead, and they're starting to get to the point where they're like, "Okay, we're mature enough where we actually need to spend on data management." The more typical case though is, as I said earlier. It's like these these new apps, that larger companies might have bleeding edge groups. So it's not being driven centrally. And so my, you asked about advice, right? So if you're sitting in the top of large enterprise and say, "Well, how do we get there. "There's kind of the tops down, "I need somebody to help me figure out." But there's also, let 1,000 flowers and let there be some kind of anarchy, if you will. Breaking the model, breaking the mold. Let people go build stuff and then over time start to figure out how to assimilate. So that'd be the biggest single biggest advice is, Yeah, you want to do the top down, but you really want to do the bottoms up. Because those people really know how to use the technology to provide a solution. >> Yeah, absolutely. Guy Kawasaki let 1,000 flowers bloom out there and everything. All right. Help bring this in. What kind of customer conversations are you having this week? We talked to the top about, there's real good energy to this show. Definitely, I felt that. What would you share with your peers that haven't been at the show? >> So the topics here are typically around the migration. Whether it's like to like, moving an existing workload into Azure, or the transformation. We also announced the show cooperation with Microsoft on moving any of your NoSQL workloads to Cosmos DB. So most of the conversations here have been related to migration. Either of, if you will, within the same Hadoop family, or, you know, like to unlike. Going from something to Cosmos DB. And that goes back to your earlier point about people trying to figure out what to do. They know there's this imperative to move to the cloud, and they're trying to figure out how they do it in bite-sized chunks. Right and protect their business at the same time. >> Yeah, so you mentioned Cosmos DB. We had an interview earlier this week about Cosmos DB. I definitely heard some good buzz at the show, What is it about that is drawing customers to it and what's that enable for them? >> Two things that I'm aware of, that I've seen is, again, the global nature and the ability to just kind of deploy anywhere. But also, I've seen a little bit around the dynamic schemas and the ability to map between them as a very quick way to ingest data. So you can get up and running quickly, instead of doing a lot of manual work to start using it. So those are things that are going to win developers 'cause it makes their life easier. >> Alright, John I want to give you the final word. What should we look to see from Imanis over the next six to 12 months. >> So we're going to continue to push forward with our platform around data management. You've seen in some recent announcements that, where leveraging machine learning in a very concrete way to do anomaly detection around ransomware. And also for administrators to be able to basically set rules or set goals and have the software do it. And that really steams from the fact that we're using a big data platform and machine learning to solve the problem of well, if you're running a big data platform, how do you manage the data? So the whole DNA of the company is built around that, and from a go-to-market standpoint, you know, partnering with folks like Cohesity and others where you've already got people in market selling a broad solution but they're missing a piece. So the other thing you'll see from us, is more partner announcements as we go forward. Alright, well, John Mracek really appreciate all the updates on a Imanis Data. Congrats on the progress so far. And look forward to catching with you up at future show. >> Great, thank you. >> Alright, we'll be back with more coverage here. Day three of three days live coverage. Microsoft Ignite here in Orlando. I'm Stu Miniman and thanks for watching theCUBE.

>> Live from Orlando, Florida, it's theCUBE covering Microsoft Ignite. Brought to you by Cohesity and theCUBE's ecosystem partners. >> Welcome back everyone, we are wrapping up day three of Microsoft Ignite here in Orlando, Florida. CUBE's live coverage, I'm your host Rebecca Knight, along with Stu Miniman, my esteemed cohost for these past three days, it's been fun working with you, Stu. >> Rebecca, it's been a great show, real excited. Our first time at a Microsoft show and it's a big one. I mean, the crowds are phenomenal. Great energy at the show and yeah, it's been great breaking down this ecosystem with you. >> So, three days, what do we know, what did you learn, what is your big takeaway, what are you going to to go back to Boston with? >> You know, it's interesting, we've been all talking and people that I know that have been here a couple of years, I've talked to people that have been at this show for decades, this is a different show. There's actually a friend of mine said, he's like, "Well look, Windows pays the bills for a lot of companies." There's a lot of people that all the Windows components, that's their job. I mean, I think back through my career when I was on the vendor side, how many rollouts of Exchange and SharePoint and all these things we've done over the years. Office 365 been a massive wave that we watched. So Microsoft has a broad portfolio and they've got three anchor shows. I was talking with one of the partners here and he's like, "You know, there's not a lot of channel people "at this event, at VMworld there's a lot of channel people." I'm like, "Well yeah because there's a separate show "that Microsoft has for them." You and I were talking at an earlier analytics session with Patrick Moorhead and he said, "You know when I look at the buy versus build, "a lot of these people are buying and I don't "feel I have as many builders." Oh wait, what's that other show that they have in the Spring, it's called Microsoft Build. A lot of the developers have moved there so it's a big ecosystem, Microsoft has a lot of products. Everything from, my son's excited about a lot of the Xbox stuff that they have here. Heck, a bunch of our crew was pickin' up Xbox sweatshirts while they're here. But a lot has changed, as Tim Crawford said, this is a very, it feels like a different Microsoft, than it even was 12 or 24 months ago. They're innovating, so look at how fast Microsoft moves and some of these things. There's good energy, people are happy and it's still trying to, you know. It's interesting, I definitely learned a lot at this show even though it wasn't the most sparkly or shiny but that's not necessarily a bad thing. >> Right, I mean, I think as you made a great point about just how integral Microsoft is to all of our lives as consumers, as enterprise, the Xbox, the Windows, the data storage, there's just so much that Microsoft does that if we were to take away Microsoft, I can't even imagine what life would be like. What have been your favorite guests? I mean, we've had so many really, really interesting people. Customers, we've had partners, we're going to have a VC. What are some of the most exciting things you've heard? >> Yeah, it's interesting, we've had Jeffrey Snover on the program a couple of years ago and obviously a very smart person here. But at this show, in his ecosystem, I mean, he created PowerShell. And so many people is like, I built my career off of what he did and this product that he launched back in 2001. But we talked a little bit about PowerShell with him but then we were talking about Edge and the Edge Boxes and AI and all those things, it's like this is really awesome stuff. And help connecting the dots to where we hid. So obviously, big name guest star, always, and I always love talking to the customers. The thing I've been looking at the last couple of years is how all of these players fit into a multicloud world. And Microsoft, if you talk about digital transformation, and you talk about who will customers turn to to help them in this multicloud world. Well, I don't think there's any company that is closer to companies applications across the spectrum of options. Office 365 and other options in SaaS, all the private cloud things, you start with Windows Server, you've got Windows on the desktop, Windows on the server. Virtualization, they're starting to do hyperconversion everything, even deeper. As well as all the public cloud with Azure and developers. I talked to the Azure functions team while I was here. Such breadth and depth of offering that Microsoft is uniquely positioned to play in a lot of those areas even if, as I said, certain areas if the latest in data there might be some other company, Google, Amazon, well positioned there. We had a good discussion Bernard Golden, who's with Capital One, gave us some good commentary on where Alibaba fits in the global scheme. So, nice broad ecosystem, and I learned a lot and I know resonated with both of us, the "you want to be a learn it all, not a know it all." And I think people that are in that mindset, this was a great show for them. >> Well, you bring up the mindset, and that is something that Satya Nadella is really such a proponent of. He says that we need to have a growth mindset. This is off of the Carol Dweck and Angela Duckworth research that talks about how important that is, how important continual learning is for success. And that is success in life and success on the job and organization success and I think that that is something that we are also really picked up on. This is the vibe of Microsoft, this is a company, Satya Nadella's leadership, talking to so many of the employees, and these are employees who've been there for decades, these are people who are really making their career, and they said, "Yeah, I been here 20 years, if I had my way, "I'll be here another 30." But the point is that people have really recommitted to Microsoft, I feel. And that's really something interesting to see, especially in the tech industry where people, millennials especially, stay a couple years and then move on to the next shiny, new thing. >> Yeah, there was one of our first guests on for Microsoft said that, "Been there 20 years and what is different about "the Satya Nadella Microsoft to the others is "we're closer and listening even more to our customers." We talk about co-creation, talk about how do we engage. Microsoft is focusing even deeper on industries. So that's really interesting. An area that I wanted to learn a little bit more about is we've been talking about Azure Stack for a number of years, we've been talking about how people are modernizing their data center. I actually had something click with me this week because when I look at Azure Stack, it reminds me of solutions I helped build with converged infrastructure and I was a big proponent of the hyper-converged infrastructure wave. And what you heard over and over again, especially from Microsoft people, is I shouldn't think of Azure Stack in that continuum. Really, Azure Stack is not from the modernization out but really from the cloud in. This is the operating model of Azure. And of course it's in the name, it's Azure, but when I looked at it and said, "Oh, well I've got partners like "Lenovo and Dell and HPE and Sysco." Building this isn't this just the next generation of platform there? But really, it's the Azure model, it's the Azure operating stack, and that is what it has. And it's more, WSSD is their solution for the converged and then what they're doing with Windows Server 2019 is the hyper-converged. Those the models that we just simplify what was happening in the data center and it's similar but a little bit different when we go to things like Azure and Azure Stack and leads to something that I wanted to get your feedback on. You talk business productivity because when we talk to companies like Nutanix, we talk to companies like Cohesity who we really appreciate their support bringing us here, giving us this great thing right in the center of it, they talk about giving people back their nights and weekends, giving them back time, because they're an easy button for a lot of things, they help make the infrastructure invisible and allow that. Microsoft says we're going to try to give you five to ten percent back of your business productivity, going to allow you to focus on things like AI and your data rather than all the kind of underlying spaghetti underneath. What's your take on the business productivity piece of things? >> I mean, I'm in favor of it; it is a laudable goal. If I can have five to ten percent of my day back of just sort of not doing the boring admin stuff, I would love that. Is it going to work, I don't know. I mean, the fact of the matter is I really applaud what Cohesity said and the customers and the fact that people are getting, yes, time back in their day to focus on the more creative projects, the more stimulating challenges that they face, but also just time back in their lives to spend with their children and their spouse and doing whatever they want to do. So those are really critical things, and those are critical things to employee satisfaction. We know, a vast body of research shows, how much work life balance is important to employees coming to their office or working remotely and doing their best work. They need time to recharge and rest and so if Microsoft can pull that off, wow, more power to them. >> And the other thing I'll add to that is if you, say, if you want that work life balance and you want to be fulfilled in your job, a lot of times what we're getting rid of is some of those underlying, those menial tasks the stuff that you didn't love doing in the first place. And what you're going to have more time to do, and every end user that we talked to says, "By the way, I'm not getting put out of a job, "I've got plenty of other tasks I could do." And those new tasks are really tying back to what the business needs. Because business and IT, they need to tie together, they need to work together, it is a partnership there. Because if IT can't deliver what the business needs, there's other alternatives, that's what Stealth IT was and the public cloud could be. And Microsoft really positions things as we're going to help you work through that transition and get there to work on these environments. >> I want to bring up another priority of Microsoft's and that is diversity. So that is another track here, there's a lot of participants who are learning about diversity in tech. It's not a good place right now, we know that. The tech industry is way too male, way too white. And Satya Nadella, along with a lot of other tech industry leaders, has said we need more underrepresented minorities, we need more women, not only as employees but also in leadership positions. Bev Crair, who was on here yesterday, she's from Lenovo. She said that things are starting to change because women are buying a lot of the tech and so that is going to force changes. What do you think, do you buy it? >> And I do, and here's where I'd say companies like Lenovo and Microsoft, when you talk about who makes decisions and how are decisions made, these are global companies. Big difference between a multi-national company or a company that's headquartered in Silicon Valley or Seattle or anything versus a global company. You look at both of those companies, they have, they are working not just to localize but have development around the world, have their teams that are listening to requirements, understand what is needed in those environments. Going back to what we talked about before, different industries, different geographies, and different cultures, we need to be able to fit and work and have products that work in those environments, everything. I think it was Bev that talked about, even when we think about what color lights. Well, you know, oh well default will use green and red. Well, in different cultures, those have different meanings. So yeah, it is, it's something that definitely I've heard the last five to ten years of my career that people understand that, it's not just, in the United States, it can't just be the US or Silicon Valley creating great technology and delivering that device all the way around the world. It needs to be something that is globally developed, that co-creation, and more, and hopefully we're making progress on the diversity front. We definitely try to do all we can to bring in diverse voices. I was glad we had a gentleman from Italy shouting back to his daughters that were watching it. We had a number of diverse guests from a geography, from a gender, from ethnicity, on the program and always trying to give those various viewpoints on theCUBE. >> I want to ask you about the show itself: the 30,000 people from 5,000 different organizations around the globe have convened here at the Orange County Convention Center, what do you think? >> Yeah, so it was impressive. We go to a lot of shows, I've been to bigger shows. Amazon Reinvent was almost 50,000 last year. I've been to Oracle OpenWorld, it's like takes over San Francisco, 60 or 70,000. This convention center is so sprawling, it's not my favorite convention center, but at least the humidity is to make sure I don't get dried out like Las Vegas. But logistics have run really well, the food has not been a complaint, it's been good, the show floor has been bustling and sessions are going well. I was talking to a guy at breakfast this morning that was like, "Oh yeah, I'm a speaker, "I'm doing a session 12 times." I'm like, "You're not speaking on the same thing 12 times?" He's like, "No, no it's a demo and hands on lab." I'm like, "Oh, of course." So they make sure that you have lots of different times to be able to do what you want. There is so much that people want to see. The good news is that they can go watch the replays of almost all of them online. Even the demos are usually something that they're cloud enabled and they get on live. And of course we help to bring a lot of this back to them to give them a taste of what's there. All of our stuff's always available on the website of thecube.net. This one, actually, this interview goes up on a podcast we call theCUBE Insights. So please, our audience, we ask you, whether it's iTunes or your favorite podcast reader, go to Spotify, theCUBE Insights. You can get a key analysis from every show that we do, we put that up there and that's kind of a tease to let you go to thecube.net and see the hundreds and thousands of interviews that we do across all of our shows. >> Great, and I want to give a final, second shout out to Cohesity, it's been so fun having them, being in the Cohesity booth, and having a lot of great Cohesity people around. >> Yeah, absolutely, I mean, so much I wish we could spend a little more time even. AI, if we go back to the keynote analysis then, but you can watch that, I can talk about the research we've done, and said how the end user information that Microsoft can get access to to help people when you talk about what they have, the TouchPoint to Microsoft Office. And even things like Xbox, down to the consumer side, to understand, have a position in the marketplace that really is unparalleled if you look at kind of the breadth and depth that Microsoft has. So yeah, big thanks to Cohesity, our other sponsors of the program that help allow us to bring this great content out to our community, and big shout out I have to give out to the community too. First time we've done this show, I reached out to all my connections and the community reached back, helped bring us a lot of great guests. I learned a lot: Cosmos DB, all the SQL stuff, all the Office and Microsoft 365, so much. My brain's full leaving this show and it's been a real pleasure. >> Great, I agree, Stu, and thank you so much to Microsoft, thank you to the crew, this has been a really fun time. We will have more coming up from the Orange County Civic Center, Microsoft Ignite. I'm Rebecca Knight for Stu Miniman, we will see you in just a little bit. (digital music)

>> Live from Orlando, Florida. It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight along with my cohost Stu Miniman. We're joined by Joachim Hammer, he is the Principal Product Manager at Microsoft. Thanks so much for coming on the show. >> Sure, you're welcome. Happy to be here. >> So there's been a lot of news and announcements with Azure SQL, can you sort of walk our viewers through a little bit about what's happened here at Ignite this week? >> Oh sure thing, so first of all I think it's a great time to be a customer of Azure SQL Database. We have a lot of innovations, and the latest one that we're really proud of, and we're just announced GA is SQL Managed Instance. So our family of database offers had so far a single database and then a pool of databases where you could do resource sharing. What was missing was this one ability for enterprise customers to migrate their workloads into Azure and take advantage of Azure without having to do any rewriting or refactoring and Managed Instance does exactly this. It's a way for enterprise customers to take their workloads, migrate them, it has all the features that they are used to from sequel server on-prem including all the security, which is of course as you can imagine always a concern in the cloud where you need to have the same or better security that customers are used to from on-prem, and with Managed Instance we have the security isolation, we have private IPV nets, we have all the intelligent protection that we have in Azure so it's a real package. And so this is a big deal for us, and the general purpose went GA yesterday actually, so I heard. >> Security's really interesting 'cause of course database is at the core of so many customer's businesses. You've been in this industry for a while, what do you see from customers as to the drivers and the differences of going to public cloud deployments versus really owning their database in-house and are security meeting the needs of what customers need now? >> Yeah sure, so, you're right, security is probably the most important topic or one of the most important topics that comes up when you discuss the cloud. And what customers want is they want a trust, they want this trust relationship that we do the right thing and doing the right thing means we have all the compliances, we adhere to all the privacy standards, but then we also offer them state of the art security so that they can rely on Microsoft on Azure for the next however many years they want to use the cloud to develop customer leading-edge security. And we do this for example with our encryption technology with Always Encrypted. This is one of those technologies that helps you protect your database against attacks by encrypting sensitive data and the data remains encrypted even though we process queries against it. So we protect against third-party attacks on the database, so Always Encrypted is one of those technologies that may not be for everybody today but customers get the sense that yes, Microsoft is thinking ahead, they're developing this security offering, and I can trust them that they continue to do this, keep my data safe and secure. >> Trust is so fundamental to this whole entire enterprise. How do you build trust with your customers, I mean you have the reputation, but how do you really go about getting your customers to say "Okay, I'm going to board your train?" >> That's a good question, Rebecca. I think as I said it starts with the portfolio of compliance requirements that we have and that we provide for Azure's SQL Database and all the other Azure services as well. But it also goes beyond that, it goes, for example, we have this right to audit capability in Azure where a company can come to us and says we want to look behind the scenes, we want to see what auditors see so that we can really believe that you are doing all the things you're saying. You're updating your virus protection, you're patching and you have all the right administrative workflows. So this is one way for us to say our doors are open if you want to come and see what we do, then you can come and peek behind the scenes so to speak. And then the other, the third part is by developing features like we do that help customers, first of all make it easy to secure the database, and help them understand vulnerabilities, and help them understand the configurations of their database and then implement the security strategy that they feel comfortable with and then letting them move that strategy into the cloud and implement it, and I think that's what we do in Azure, and that's why we've had so much success so far. >> Earlier this week we interviewed one of your peers, talked about Cosmos DB. >> Okay. >> There's a certain type of scale we talk about there. Scale means different things to different sized customers. What does scale mean in your space? >> Yeah so you're right, scale can mean a lot of different things, and actually thank you for bringing this up so we have another announcement that we made on namely Hyper-Scale architecture. So far in Azure SQL DB, we were pretty much constrained in terms of space by the underlying hardware, how much storage comes on these VMs, and thanks to our re-architectured hardware, sorry software, we now have the ability to scale way beyond four terabytes which is the current scale of Azure SQL DB. So we can go to 64 terabytes, 100 terabytes. And we can, not only does that free up, free us from the limitations, but it also keeps it simple for customers. So customers don't have to go and build a complicated scale out architecture to take advantage of this. They can just turn a knob in a portal, and then we give them as much horsepower as they need to include in the storage. And in order for this to happen, we had to do a lot of work. So it doesn't just mean, we didn't just re-architect storage but we also have to make fail-over's faster. We have to continue to invest in online operations like online index rebuild and create to make those resumable, pause and resumable, so that with bigger and bigger databases, you can actually do all those activities that you used to do ya know, without getting in the way of your workloads. So lot of work, but we have Hyper-Scale now in Azure SQL DB and so I think this is another sort of something that customers will be really excited about. >> Sounds like that could have been a real pain point for a lot of DBA's out there, and I'm wondering, I'm sure, as a PM, you get lots of feedback from customers. What are the biggest challenges they're facing? What are some of the things they're excited about that Microsoft's helping them with these days? >> So you're right, this was a big pain point, because if you go to a big enterprise customer and say, hey bring your workload to Azure, and then they say oh yeah great, we've got this big telemetry database, what's your size limit? And you have to say four terabytes, that doesn't go too well. So that's one thing, we've removed that blocker thankfully. Other pain points I think we have by and large, I think the large pain points are we've removed, I think we have small ones where we're still working on making our deployments less painful for some customers. There's customers who are really, really sensitive to disconnects or latent variations in latency. And sometimes when we do deployments, worldwide deployments, we are impacting somebody's customer, so this is a pain point that we're currently working on. Security, as you said, is always a pain point, so this is something that will stay with us, and we just have to make sure that we're keeping up with the security demands from customers. And then, another pain point, or has been a pain point for customers, especially customers sequel server on-prem is the performance tuning. When you have to be a really, really good DBA to tune your workloads well, and so this is something that we are working on in Azure SQL DB with our intelligence performance tuning. This is a paint point that we are removing. We've removed a lot of it already. There's still, occasionally, there's still customers who complaining about performance and that's understood. And this is something that we're also trying to help them with, make it easier, give 'em insights into what their workload is doing, where are the weights, where are the slow queries, and then help them diffuse that. >> So thinking about these announcements and the changes that you've made to improve functionality and increase, not have size limits be such a road block, when you're thinking ahead to making the database more intelligent, what are some of the things you're most excited about that are still in progress right now, still in development, that we'll be talking about at next year's Ignite? >> Yeah, so personally for me on the security side, what's really exciting to me is the, so security's a very complicated topic, and not all of our customers are fully comfortable figuring out what is my security strategy and how do I implement it, and is my data really secure. So understanding threats, understanding all this technology, so I think one of the visions that gets me excited about the potential of the cloud, is that we can make security in the future hopefully as easy as we were able to make query processing with the invention of the relational model, where we made this leap from having to write code to access your data to basically a declarative SQL type language where you say this is what I want and I don't care how to database system returns it to me. If you translate that to security, what would be ideal the sort of the North Star, is to tell it to have customers in some sort of declarative policy based manner, say I have some data that I don't trust to the cloud please find the sensitive information here, and then protect it so that I'm meeting ISO or I'm meeting HIPPA requirements or that I'm meeting my internal ya know, every company has internal policies about how data needs to be secured and handled. And so if you could translate that into a declarative policy and then upload that to us, and we figure out behind the scenes these are the things we need, you need to turn on auditing, these are where the audit events have to go, and this is where the data has to be protected. But before all that, we actually identify all the sensitive data for you, we'll tag it and so forth. That to me has been a tremendous, sort of untapped potential of the cloud. That's where I think this intelligence could go potentially. >> Yeah, great. >> Who knows, maybe. >> (laughs) Well, we shall see at next year's Ignite. >> We are making handholds there. We have a classification engine that helps customers find sensitive data. We have a vulnerability assessment, a rules engine that allows you to basically test the configuration of your database against potential vulnerabilities, and we have threat detection. So we have a lot of the pieces, and I think the next step for us is to put these all together into something that can then be much more automated so that a customer doesn't have to think technology anymore. They can they business. They can think about the kinds of compliances they have to meet. They can think about, based on these compliances, this data can go this month, this data can go maybe next year, or ya know, in that kind of terms. So I think, that to me is exciting. >> Well Joachim, thank you so much for coming on theCUBE. It was a pleasure having you here. >> It was my pleasure too. Thank you. >> I'm Rebecca Knight for Stu Miniman, we'll have more from theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (upbeat music)

>> Live from Orlando, Florida. It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back to the CUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight. Along with my co-host Stu Miniman. We're joined by Andrew Liu. He is the senior product manager at Azure Cosmos DB. Thanks so much for coming on the show Andrew. >> Oh, thank you for hosting. >> You're a first timer, so this will be a lot of fun. So, talk to me a little bit. Azure Cosmos DB is a database for building blazing fast planet scale applications. Can you tell our viewers a little bit about what you do and about the history of Azure Cosmos? >> Sure, so Azure Cosmos DB started with, about eight years ago, where we were also outgrowing a lot of our own database needs with what we had previously built. And a lot of the challenges that we had was really around partitioning, replication, and resource governance. So, I'll talk a little bit about each one. Partitioning is really about solving the problem of scale. Right? I have so much data, doesn't fit on a single machine, and I have so many requests per second. Also doesn't, can't be served out of a single machine. So how do I go and build a system, a database that can elastically scale over a cluster of machines, so I don't have to manually shard, and as a user have to shard a database across many, many instances. This way I really want to be able to scale just seamlessly. The velocity problem is, we also wanted to build something that, can respond in a very fast manner, in terms of latency. So, it's great and all that we can serve lots of request per second, but, what is the response time of each one of those requests? And the resource governance was there to really actually build this as a cloud native database in which we wanted to exploit the properties of our cloud. We wanted to use the economies of scale that we can have basically data centers built all around the world, and build this as a multi, truly multi-tenant service. And by doing so we can also afford the total cost of ownership for us, as well as, a guaranteed predictable performance for the tenants. Now we did this, for initially our first party tenants at Microsoft, where we have made a bet on everything from our Microsoft live platform, to Office, to Azure itself as built on Azure Cosmos DB. And about four years ago we found that hey, this is not really just a Microsoft problem that we're solving, but it's an everybody problem, it's become universal, and so we've launched it out to the open. >> Yeah, Andrew that's, great point, and I want you to help unpack that for us a little bit because you know, we've been saying on theCUBE for many years, distributed architectures are some of the toughest challenges of our time, but, if I'm a Facebook, or a Google, or a Microsoft, I understand some of the challenges, and I understand why I need it, but, when you talk about scale, well, scale means a lot of different things to a lot of different people. So, how does Cosmos? What does that mean to your users, end users, why do they need this? You know, haven't they just felt some microservices architecture? And they'll just leverage, ya know what's in Azure. And things like that. How does this global scale impact the typical user? >> So I'm actually seeing this come in different types of patterns for different types of industries. So for example, in manufacturing we're commonly seeing Cosmos DB used really for that scalability for the write scalability, and having many, many concurrent writes per second. Typically this is done in an IoT telemetry, or an IoT device registry case. So let's use one of our customers for example, Toyota. Each year they're shipping millions of vehicles on the road, and they're building a big connected car platform. The connected car platform allows you to do things like, whenever it alerts an airbag gets deployed, they can go and make sure and call their driver, hey, I saw the airbag was deployed are you okay? And if the user doesn't pick up their phone, immediately notify emergency services. But the challenge here is if each year I'm shipping millions of vehicles on the road, and each of 'em has a heartbeat every second, I'm dealing with millions of writes per second, and I need a database that can scale to that. In contrast, in retail I'm actually seeing very different use cases. They're using more of the replication side of our stock where they have a global user base, and they're trying to expand an eCommerce shop. So for example ASOS is a big fashion retailer, they ship to 200 different countries globally, and they want to make sure that they can deliver real-time experiences like real-time personalization, and based off of who the user is recommended set of products that is tailored to that user. Well now what I need is a data set that can expand to my shoppers across two different hundred, 200 countries around the globe, and deliver that with very, very low latency so that my web experience is also very robust. So what they use is our global distribution, and our multi-mastering technology. Where we can actually have a database presence, similar to like what a CDN does for static content, we're doing for our dynamic evolving content. So in a database your work load, typically your data set is evolving, and you want to be able to run queries with consistency over that. As opposed to in CDN you're typically serving static assets. Well here we can actually support those dynamic content, and then build these low latency experiences to users all around the globe. The other area we see a lot of usage is in ISV's for mission critical workloads. And the replication actually gets us two awesome properties, right? One is the low latency by shipping data closer to where the user is, but the other property you get is a lot of redundancy, and so we actually also offer industry leading SLA's where we guarantee five nines of availability, and the way we're able to do so is, with a highly redundant architecture you don't care if let's say a machine were to bomb out at any given time, because we have multiple redundant copies in different parts of the globe. You're guaranteed that your workload is always online. >> So my question for you is, when you have these, you just described some really, really interesting customer use cases in manufacturing, in retail, do you then create products and services for each of these industries? Or do you say hey other retail customers, we've noticed this really works for this customer over here, how do you go out to the community with what you're selling? >> Ah, got it. So we actually have found that this can be a challenging space for some of our customers today, 'cause we have so many products. The way we kind of view it is we want to have a portfolio, so that you can always choose the right tool for the right job. And I think a lot of how Microsoft has evolved as a business actually is around this. Previously we would sell a hammer, and we'd tell you don't worry everything's a nail, even if it looks like a screw let's just pretend it's a nail and whack it down. But today we've built this big vast toolbox, and you can think of Cosmos DB as just one of many tools in our vast toolbox. So if you have a screw maybe you pickup a screwdriver, and screw that in. And the way Azure works is then if we have a very comprehensive toolbox, depending on what precise scenario you have, you can kind of mix and match the tools that fit your problem. So think of them as like individual Lego blocks, and whether you're building like a death star, or an x-wing, you can go, and assemble the right pieces for your application. >> Andrew, some news at the show around Cosmos DB. Share us what the updates are. >> Oh sure, so we're really excited to launch a few new features. The highlights are multi-master, and Cassandra API. So multi-master really exploits the replicated nature of our database. Before multi-master what we would do is, we would allow you to have a globally distributed database in which you can have write requests go to single region, and reads being served out of any of these other locations. With multi-master we've actually made it so that each of those replicas we've deployed around the globe can also accept write requests. What that translates to from a user point of view is number one, your write requests are a lot faster, they're super low latency, single-digit millisecond latency in fact. No matter where the user is around the globe. And number two, you also get much higher write availability. So even if let's say, we're having a natural disaster, we had a nasty hurricane as you know pass through on the east coast last week, but with a globally distributed database the nice thing is even if you have, let's say, a power disruption in one region of the world, it doesn't matter cause you can then just fail over, and talk to another data center, where you have a live replica already located. So we just came out with multi-master. The short summary is low latency writes, as well as high available writes. The other feature that we launched is Cassandra API, and as you know this is a multi-model, multi-API database. What that means is, what we're trying to do is also meet our users where they are. As opposed to pushing our proprietary software on them, and we take the whole concept of vendor lock-in very, very seriously. Which is why we make such a big bet on the open source ecosystem. If you already have, let's say a MongoDB application, or a Cassandra application, but you'd really love to be able to take advantage of some of the novel properties that we've built with building a fully managed multi-master database. Well, what we've done is we've implemented this as a wire level protocol on the server side. So it can take an existing application, not change a single line of code, and point it to Cosmos DB as a back-end, and then take advantage of Cosmos DB as your database. >> One of the interesting things if you look at the kind of changing face of databases, it's how users are being able to leverage their data. You talk about everything from you know, I think Cassandra back, and some of the big data discussions, today everything's AI which I know is near and dear to Microsoft's heart. Satya Nadella I'm talking about, how do you think of the role of data in this solution set? >> Sorry, can you say that one more time? >> So, how customers think about leveraging data, how things like Cosmos allow them to really extract the value out of data, not just be some database that kind of stuck in the back-end somewhere. >> Yeah, yeah. I mean a lot of it is the new novel experiences people are building. So for example, like the connected car platform, I'm seeing people actually build this, and take advantage of new novel territories that a traditional automobile manufacturer used to not do. Not only are they building experiences around, how do they provide value to their end users? Like the air bag scenario, but they're also using this as a way of building value for their business, and how to make sure that, hey when, next time you're up for an oil change that they can send a helpful reminder, and say hey I noticed you're due for an oil change in terms of mileage. Why don't I just go set up an appointment, just up for you, as well as other experiences for things, like when they want to do fleet management, and do partnerships with either ride sharing companies like Uber, and Lyft, or rental car companies like Avis, Hertz, et cetera. I've also seen people take advantage of, taking kind of new novel experiences through databases, through AI, and machine learning. So for example, the product recommendations. This was something that historically, when I wanted to do recommendations a decade ago, maybe I have some big beefy data lake running somewhere in the back-end, it might take a week to munch through that data, but that's okay, a week later once I'm ready, I'll send out some mail, maybe some email to you, but today when I want to actually show live right when the user is browsing my website, my website has to load fast right? If my goal is to increase conversions on sales, having a slow running website is the fastest way for my user to click the back button. But if I want to build real-time personalization, and want to generate let's say a recommendation within 200 millisecond latency, well now that I have databases that can guarantee me single-digit millisecond latency, it gives me ample time to actually improve the business logic for those recommendations. >> I want to ask you a question about culture, because you are based at the mothership in Redmond, Washington. So we heard Satya Nadella on the main stage today talk about tech intensiveness, tech intensity, sorry, this idea that we need to not only be adopting technology, but also building the latest, and greatest. I'm curious about, how that translates at Microsoft's campus, and sort of how, how this idea is, infuses how you work with your colleagues, and then also how you work with your customers and partners? >> I think some of the biggest positive changes I've seen over the last decade has been how much more of a customer focus we have today then ever. And i think a lot of things have led to that. One is, just the ability to ship much faster. As we move to Cloud services we're no longer in these big box product release cycles of building a product, and waiting like one or two years to ship it to our users. But now we can actually get some real-time feedback. So as we go, and ship, and deploy software, we actually deploy even on a weekly cadence over here. What that allows us to do is actually experiment a lot more, and get real-time feedback, so if we have an idea, and rather than having to go through a long lengthy vetting process, spending years building, and hoping that it really pays off. What we can do is we can just go talk to our users, and say hey, ya know, we have an idea for our future. We'd love to get your feedback, or a lot of times honestly our customers actually come to us, where we're so tightly engaged these days, that when, users even come to us, and say like hey, what do you think about this idea? It would really add a lot of value to my scenario. We go, and try to root cause that, really get an idea of what exactly that they need. But then we can turn that around in blazing fast time. And I think a lot of the shift to Cloud services, and being able to avoid the overhead of well we got to wait for this ship train, and then wait for the right operation personnel to go and deploy the updates. Now that we can control our own destiny, and just ship on a very, very fast cadence, we're closer to our users, and we experiment a lot more, and I think it's a beautiful thing. >> Great, well Andrew thank you so much for coming on theCUBE, it was fun talking to you. >> Oh yeah, thank you for hosting. >> I'm Rebecca Knight, for Stu Miniman, we will have more from theCUBE's live coverage of Microsoft Ignite coming up just after this. (techno music)

[Music] [Music] [Music] [Music] ladies and gentlemen please welcome Red Hat senior vice president of engineering Matt Hicks [Music] welcome back I hope you're enjoying your first day of summit you know for us it is a lot of work throughout the year to get ready to get here but I love the energy walking into someone on that first opening day now this morning we kick off with Paul's keynote and you saw this morning just how evolved every aspect of open hybrid cloud has become based on an open source innovation model that opens source the power and potential of open source so we really brought me to Red Hat but at the end of the day the real value comes when were able to make customers like yourself successful with open source and as much passion and pride as we put into the open source community that requires more than just Red Hat given the complexity of your various businesses the solution set you're building that requires an entire technology ecosystem from system integrators that can provide the skills your domain expertise to software vendors that are going to provide the capabilities for your solutions even to the public cloud providers whether it's on the hosting side or consuming their services you need an entire technological ecosystem to be able to support you and your goals and that is exactly what we are gonna talk about this afternoon the technology ecosystem we work with that's ready to help you on your journey now you know this year's summit we talked about earlier it is about ideas worth exploring and we want to make sure you have all of the expertise you need to make those ideas a reality so with that let's talk about our first partner we have him today and that first partner is IBM when I talk about IBM I have a little bit of a nostalgia and that's because 16 years ago I was at IBM it was during my tenure at IBM where I deployed my first copy of Red Hat Enterprise Linux for a customer it's actually where I did my first professional Linux development as well you and that work on Linux it really was the spark that I had that showed me the potential that open source could have for enterprise customers now iBM has always been a steadfast supporter of Linux and a great Red Hat partner in fact this year we are celebrating 20 years of partnership with IBM but even after 20 years two decades I think we're working on some of the most innovative work that we ever have before so please give a warm welcome to Arvind Krishna from IBM to talk with us about what we are working on Arvind [Applause] hey my pleasure to be here thank you so two decades huh that's uh you know I think anything in this industry to going for two decades is special what would you say that that link is made right Hatton IBM so successful look I got to begin by first seeing something that I've been waiting to say for years it's a long strange trip it's been and for the San Francisco folks they'll get they'll get the connection you know what I was just thinking you said 16 it is strange because I probably met RedHat 20 years ago and so that's a little bit longer than you but that was out in Raleigh it was a much smaller company and when I think about the connection I think look IBM's had a long long investment and a long being a long fan of open source and when I think of Linux Linux really lights up our hardware and I think of the power box that you were showing this morning as well as the mainframe as well as all other hardware Linux really brings that to life and I think that's been at the root of our relationship yeah absolutely now I alluded to a little bit earlier we're working on some new stuff and this time it's a little bit higher in the software stack and we have before so what do you what would you say spearheaded that right so we think of software many people know about some people don't realize a lot of the words are called critical systems you know like reservation systems ATM systems retail banking a lot of the systems run on IBM software and when I say IBM software names such as WebSphere and MQ and db2 all sort of come to mind as being some of that software stack and really when I combine that with some of what you were talking about this morning along hybrid and I think this thing called containers you guys know a little about combining the two we think is going to make magic yeah and I certainly know containers and I think for myself seeing the rise of containers from just the introduction of the technology to customers consuming at mission-critical capacities it's been probably one of the fastest technology cycles I've ever seen before look we completely agree with that when you think back to what Paul talks about this morning on hybrid and we think about it we are made of firm commitment to containers all of our software will run on containers and all of our software runs Rell and you put those two together and this belief on hybrid and containers giving you their hybrid motion so that you can pick where you want to run all the software is really I think what has brought us together now even more than before yeah and the best part I think I've liked we haven't just done the product in downstream alignment we've been so tied in our technology approach we've been aligned all the way to the upstream communities absolutely look participating upstream participating in these projects really bringing all the innovation to bear you know when I hear all of you talk about you can't just be in a single company you got to tap into the world of innovation and everybody should contribute we firmly believe that instead of helping to do that is kind of why we're here yeah absolutely now the best part we're not just going to tell you about what we're doing together we're actually going to show you so how every once you tell the audience a little bit more about what we're doing I will go get the demo team ready in the back so you good okay so look we're doing a lot here together we're taking our software and we are begging to put it on top of Red Hat and openshift and really that's what I'm here to talk about for a few minutes and then we go to show it to you live and the demo guard should be with us so it'll hopefully go go well so when we look at extending our partnership it's really based on three fundamental principles and those principles are the following one it's a hybrid world every enterprise wants the ability to span across public private and their own premise world and we got to go there number two containers are strategic to both of us enterprise needs the agility you need a way to easily port things from place to place to place and containers is more than just wrapping something up containers give you all of the security the automation the deploy ability and we really firmly believe that and innovation is the path forward I mean you got to bring all the innovation to bear whether it's around security whether it's around all of the things we heard this morning around going across multiple infrastructures right the public or private and those are three firm beliefs that both of us have together so then explicitly what I'll be doing here number one all the IBM middleware is going to be certified on top of openshift and rel and through cloud private from IBM so that's number one all the middleware is going to run in rental containers on OpenShift on rail with all the cloud private automation and deployability in there number two we are going to make it so that this is the complete stack when you think about from hardware to hypervisor to os/2 the container platform to all of the middleware it's going to be certified up and down all the way so that you can get comfort that this is certified against all the cyber security attacks that come your way three because we do the certification that means a complete stack can be deployed wherever OpenShift runs so that way you give the complete flexibility and you no longer have to worry about that the development lifecycle is extended all the way from inception to production and the management plane then gives you all of the delivery and operation support needed to lower that cost and lastly professional services through the IBM garages as well as the Red Hat innovation labs and I think that this combination is really speaks to the power of both companies coming together and both of us working together to give all of you that flexibility and deployment capabilities across one can't can't help it one architecture chart and that's the only architecture chart I promise you so if you look at it right from the bottom this speaks to what I'm talking about you begin at the bottom and you have a choice of infrastructure the IBM cloud as well as other infrastructure as a service virtual machines as well as IBM power and IBM mainframe as is the infrastructure choices underneath so you choose what what is best suited for the workload well with the container service with the open shift platform managing all of that environment as well as giving the orchestration that kubernetes gives you up to the platform services from IBM cloud private so it contains the catalog of all middle we're both IBM's as well as open-source it contains all the deployment capability to go deploy that and it contains all the operational management so things like come back up if things go down worry about auto scaling all those features that you want come to you from there and that is why that combination is so so powerful but rather than just hear me talk about it I'm also going to now bring up a couple of people to talk about it and what all are they going to show you they're going to show you how you can deploy an application on this environment so you can think of that as either a cloud native application but you can also think about it as how do you modernize an application using micro services but you don't want to just keep your application always within its walls you also many times want to access different cloud services from this and how do you do that and I'm not going to tell you which ones they're going to come and tell you and how do you tackle the complexity of both hybrid data data that crosses both from the private world to the public world and as well as target the extra workloads that you want so that's kind of the sense of what you're going to see through through the demonstrations but with that I'm going to invite Chris and Michael to come up I'm not going to tell you which one's from IBM which runs from Red Hat hopefully you'll be able to make the right guess so with that Chris and Michael [Music] so so thank you Arvind hopefully people can guess which ones from Red Hat based on the shoes I you know it's some really exciting stuff that we just heard there what I believe that I'm I'm most excited about when I look out upon the audience and the opportunity for customers is with this announcement there are quite literally millions of applications now that can be modernized and made available on any cloud anywhere with the combination of IBM cloud private and OpenShift and I'm most thrilled to have mr. Michael elder a distinguished engineer from IBM here with us today and you know Michael would you maybe describe for the folks what we're actually going to go over today absolutely so when you think about how do I carry forward existing applications how do I build new applications as well you're creating micro services that always need a mixture of data and messaging and caching so this example application shows java-based micro services running on WebSphere Liberty each of which are then leveraging things like IBM MQ for messaging IBM db2 for data operational decision manager all of which is fully containerized and running on top of the Red Hat open chip container platform and in fact we're even gonna enhance stock trader to help it understand how you feel but okay hang on so I'm a little slow to the draw sometimes you said we're gonna have an application tell me how I feel exactly exactly you think about your enterprise apps you want to improve customer service understanding how your clients feel can't help you do that okay well this I'd like to see that in action all right let's do it okay so the first thing we'll do is we'll actually take a look at the catalog and here in the IBM cloud private catalog this is all of the content that's available to deploy now into this hybrid solution so we see workloads for IBM will see workloads for other open source packages etc each of these are packaged up as helm charts that are deploying a set of images that will be certified for Red Hat Linux and in this case we're going to go through and start with a simple example with a node out well click a few actions here we'll give it a name now do you have your console up over there I certainly do all right perfect so we'll deploy this into the new old namespace and will deploy notate okay alright anything happening of course it's come right up and so you know what what I really like about this is regardless of if I'm used to using IBM clout private or if I'm used to working with open shift yeah the experience is well with the tool of whatever I'm you know used to dealing with on a daily basis but I mean you know I got to tell you we we deployed node ourselves all the time what about and what about when was the last time you deployed MQ on open shift you never I maybe never all right let's fix that so MQ obviously is a critical component for messaging for lots of highly transactional systems here we'll deploy this as a container on the platform now I'm going to deploy this one again into new worlds I'm gonna disable persistence and for my application I'm going to need a queue manager so I'm going to have it automatically setup my queue manager as well now this will deploy a couple of things what do you see I see IBM in cube all right so there's your stateful set running MQ and of course there's a couple of other components that get stood up as needed here including things like credentials and secrets and the service etc but all of this is they're out of the box ok so impressive right but that's the what I think you know what I'm really looking at is maybe how a well is this running you know what else does this partnership bring when I look at IBM cloud private windows inches well so that's a key reason about why it's not just about IBM middleware running on open shift but also IBM cloud private because ultimately you need that common management plane when you deploy a container the next thing you have to worry about is how do I get its logs how do I manage its help how do I manage license consumption how do I have a common security plan right so cloud private is that enveloping wrapper around IBM middleware to provide those capabilities in a common way and so here we'll switch over to our dashboard this is our Griffin and Prometheus stack that's deployed also now on cloud private running on OpenShift and we're looking at a different namespace we're looking at the stock trader namespace we'll go back to this app here momentarily and we can see all the different pieces what if you switch over to the stock trader workspace on open shipped yeah I think we might be able to do that here hey there it is alright and so what you're gonna see here all the different pieces of this op right there's d b2 over here I see the portfolio Java microservice running on Webster Liberty I see my Redis cash I see MQ all of these are the components we saw in the architecture picture a minute ago ya know so this is really great I mean so maybe let's take a look at the actual application I see we have a fine stock trader app here now we mentioned understanding how I feel exactly you know well I feel good that this is you know a brand new stock trader app versus the one from ten years ago that don't feel like we used forever so the key thing is this app is actually all of those micro services in addition to things like business rules etc to help understand the loyalty program so one of the things we could do here is actually enhance it with a a AI service from Watson this is tone analyzer it helps me understand how that user actually feels and will be able to go through and submit some feedback to understand that user ok well let's see if we can take a look at that so I tried to click on youth clearly you're not very happy right now here I'll do one quick thing over here go for it we'll clear a cache for our sample lab so look you guys don't actually know as Michael and I just wrote this no js' front end backstage while Arvin was actually talking with Matt and we deployed it real-time using continuous integration and continuous delivery that we have available with openshift well the great thing is it's a live demo right so we're gonna do it all live all the time all right so you mentioned it'll tell me how I'm feeling right so if we look at so right there it looks like they're pretty angry probably because our cache hadn't been cleared before we started the demo maybe well that would make me angry but I should be happy because I mean I have a lot of money well it's it's more than I get today for sure so but you know again I don't want to remain angry so does Watson actually understand southern I know it speaks like eighty different languages but well you know I'm from South Carolina to understand South Carolina southern but I don't know about your North Carolina southern alright well let's give it a go here y'all done a real real know no profanity now this is live I've done a real real nice job on this here fancy demo all right hey all right likes me now all right cool and the key thing is just a quick note right it's showing you've got a free trade so we can integrate those business rules and then decide to I do put one trade if you're angry give me more it's all bringing it together into one platform all running on open show yeah and I can see the possibilities right of we've not only deployed services but getting that feedback from our customers to understand well how well the services are being used and are people really happy with what they have hey listen Michael this was amazing I read you joining us today I hope you guys enjoyed this demo as well so all of you know who this next company is as I look out through the crowd based on what I can actually see with the sun shining down on me right now I can see their influence everywhere you know Sports is in our everyday lives and these guys are equally innovative in that space as they are with hybrid cloud computing and they use that to help maintain and spread their message throughout the world of course I'm talking about Nike I think you'll enjoy this next video about Nike and their brand and then we're going to hear directly from my twitting about what they're doing with Red Hat technology new developments in the top story of the day the world has stopped turning on its axis top scientists are currently racing to come up with a solution everybody going this way [Music] the wrong way [Music] please welcome Nike vice president of infrastructure engineering Mike witig [Music] hi everybody over the last five years at Nike we have transformed our technology landscape to allow us to connect more directly to our consumers through our retail stores through Nike comm and our mobile apps the first step in doing that was redesigning our global network to allow us to have direct connectivity into both Asia and AWS in Europe in Asia and in the Americas having that proximity to those cloud providers allows us to make decisions about application workload placement based on our strategy instead of having design around latency concerns now some of those workloads are very elastic things like our sneakers app for example that needs to burst out during certain hours of the week there's certain moments of the year when we have our high heat product launches and for those type of workloads we write that code ourselves and we use native cloud services but being hybrid has allowed us to not have to write everything that would go into that app but rather just the parts that are in that application consumer facing experience and there are other back-end systems certain core functionalities like order management warehouse management finance ERP and those are workloads that are third-party applications that we host on relevent over the last 18 months we have started to deploy certain elements of those core applications into both Azure and AWS hosted on rel and at first we were pretty cautious that we started with development environments and what we realized after those first successful deployments is that are the impact of those cloud migrations on our operating model was very small and that's because the tools that we use for monitoring for security for performance tuning didn't change even though we moved those core applications into Azure in AWS because of rel under the covers and getting to the point where we have that flexibility is a real enabler as an infrastructure team that allows us to just be in the yes business and really doesn't matter where we want to deploy different workload if either cloud provider or on-prem anywhere on the planet it allows us to move much more quickly and stay much more directed to our consumers and so having rel at the core of our strategy is a huge enabler for that flexibility and allowing us to operate in this hybrid model thanks very much [Applause] what a great example it's really nice to hear an IQ story of using sort of relish that foundation to enable their hybrid clout enable their infrastructure and there's a lot that's the story we spent over ten years making that possible for rel to be that foundation and we've learned a lot in that but let's circle back for a minute to the software vendors and what kicked off the day today with IBM IBM s one of the largest software portfolios on the planet but we learned through our journey on rel that you need thousands of vendors to be able to sport you across all of your different industries solve any challenge that you might have and you need those vendors aligned with your technology direction this is doubly important when the technology direction is changing like with containers we saw that two years ago bread had introduced our container certification program now this program was focused on allowing you to identify vendors that had those shared technology goals but identification by itself wasn't enough in this fast-paced world so last year we introduced trusted content we introduced our container health index publicly grading red hats images that form the foundation for those vendor images and that was great because those of you that are familiar with containers know that you're taking software from vendors you're combining that with software from companies like Red Hat and you are putting those into a single container and for you to run those in a mission-critical capacity you have to know that we can both stand by and support those deployments but even trusted content wasn't enough so this year I'm excited that we are extending once again to introduce trusted operations now last week we announced that cube con kubernetes conference the kubernetes operator SDK the goal of the kubernetes operators is to allow any software provider on kubernetes to encode how that software should run this is a critical part of a container ecosystem not just being able to find the vendors that you want to work with not just knowing that you can trust what's inside the container but knowing that you can efficiently run that software now the exciting part is because this is so closely aligned with the upstream technology that today we already have four partners that have functioning operators specifically Couchbase dynaTrace crunchy and black dot so right out of the gate you have security monitoring data store options available to you these partners are really leading the charge in terms of what it means to run their software on OpenShift but behind these four we have many more in fact this morning we announced over 60 partners that are committed to building operators they're taking their domain expertise and the software that they wrote that they know and extending that into how you are going to run that on containers in environments like OpenShift this really brings the power of being able to find the vendors being able to trust what's inside and know that you can run their software as efficiently as anyone else on the planet but instead of just telling you about this we actually want to show you this in action so why don't we bring back up the demo team to give you a little tour of what's possible with it guys thanks Matt so Matt talked about the concept of operators and when when I think about operators and what they do it's taking OpenShift based services and making them even smarter giving you insight into how they do things for example have we had an operator for the nodejs service that I was running earlier it would have detected the problem and fixed itself but when we look at it what really operators do when I look at it from an ecosystem perspective is for ISVs it's going to be a catalyst that's going to allow them to make their services as manageable and it's flexible and as you know maintainable as any public cloud service no matter where OpenShift is running and to help demonstrate this I've got my buddy Rob here Rob are we ready on the demo front we're ready awesome now I notice this screen looks really familiar to me but you know I think we want to give folks here a dev preview of a couple of things well we want to show you is the first substantial integration of the core OS tectonic technology with OpenShift and then the other thing is we are going to dive in a little bit more into operators and their usefulness so Rob yeah so what we're looking at here is the service catalog that you know and love and openshift and we've got a few new things in here we've actually integrated operators into the Service Catalog and I'm going to take this filter and give you a look at some of them that we have today so you can see we've got a list of operators exposed and this is the same way that your developers are already used to integrating with products they're right in your catalog and so now these are actually smarter services but how can we maybe look at that I mentioned that there's maybe a new view I'm used to seeing this as a developer but I hear we've got some really cool stuff if I'm the administrator of the console yeah so we've got a whole new side of the console for cluster administrators to get a look at under the infrastructure versus this dev focused view that we're looking at today today so let's go take a look at it so the first thing you see here is we've got a really rich set of monitoring and health status so we can see that we've got some alerts firing our control plane is up and we can even do capacity planning anything that you need to do to maintenance your cluster okay so it's it's not only for the the services in the cluster and doing things that you know I may be normally as a human operator would have to do but this this console view also gives me insight into the infrastructure itself right like maybe the nodes and maybe handling the security context is that true yes so these are new capabilities that we're bringing to open shift is the ability to do node management things like drain and unscheduled nodes to do day-to-day maintenance and then as well as having security constraints and things like role bindings for example and the exciting thing about this is this is a view that you've never been able to see before it's cross-cutting across namespaces so here we've got a number of admin bindings and we can see that they're connected to a number of namespaces and these would represent our engineering teams all the groups that are using the cluster and we've never had this view before this is a perfect way to audit your security you know it actually is is pretty exciting I mean I've been fortunate enough to be on the up and shift team since day one and I know that operations view is is something that we've you know strived for and so it's really exciting to see that we can offer that now but you know really this was a we want to get into what operators do and what they can do for us and so maybe you show us what the operator console looks like yeah so let's jump on over and see all the operators that we have installed on the cluster you can see that these mirror what we saw on the Service Catalog earlier now what we care about though is this Couchbase operator and we're gonna jump into the demo namespace as I said you can share a number of different teams on a cluster so it's gonna jump into this namespace okay cool so now what we want to show you guys when we think about operators you know we're gonna have a scenario here where there's going to be multiple replicas of a Couchbase service running in the cluster and then we're going to have a stateful set and what's interesting is those two things are not enough if I'm really trying to run this as a true service where it's highly available in persistent there's things that you know as a DBA that I'm normally going to have to do if there's some sort of node failure and so what we want to demonstrate to you is where operators combined with the power that was already within OpenShift are now coming together to keep this you know particular database service highly available and something that we can continue using so Rob what have you got there yeah so as you can see we've got our couch based demo cluster running here and we can see that it's up and running we've got three members we've got an off secret this is what's controlling access to a UI that we're gonna look at in a second but what really shows the power of the operator is looking at this view of the resources that it's managing you can see that we've got a service that's doing load balancing into the cluster and then like you said we've got our pods that are actually running the software itself okay so that's cool so maybe for everyone's benefit so we can show that this is happening live could we bring up the the Couchbase console please and keep up the openshift console both sides so what we see there we go so what we see on the on the right hand side is obviously the same console Rob was working in on the left-hand side as you can see by the the actual names of the pods that are there the the couch based services that are available and so Rob maybe um let's let's kill something that's always fun to do on stage yeah this is the power of the operator it's going to recover it so let's browse on over here and kill node number two so we're gonna forcefully kill this and kick off the recovery and I see right away that because of the integration that we have with operators the Couchbase console immediately picked up that something has changed in the environment now why is that important normally a human being would have to get that alert right and so with operators now we've taken that capability and we've realized that there has been a new event within the environment this is not something that you know kubernetes or open shipped by itself would be able to understand now I'm presuming we're gonna end up doing something else it's not just seeing that it failed and sure enough there we go remember when you have a stateful application rebalancing that data and making it available is just as important as ensuring that the disk is attached so I mean Rob thank you so much for you know driving this for us today and being here I mean you know not only Couchbase but as was mentioned by matt we also have you know crunchy dynaTrace and black duck I would encourage you all to go visit their booths out on the floor today and understand what they have available which are all you know here with a dev preview and then talk to the many other partners that we have that are also looking at operators so again rub thank you for joining us today Matt come on out okay this is gonna make for an exciting year of just what it means to consume container base content I think containers change how customers can get that I believe operators are gonna change how much they can trust running that content let's circle back to one more partner this next partner we have has changed the landscape of computing specifically with their work on hardware design work on core Linux itself you know in fact I think they've become so ubiquitous with computing that we often overlook the technological marvels that they've been able to overcome now for myself I studied computer engineering so in the late 90s I had the chance to study processor design I actually got to build one of my own processors now in my case it was the most trivial processor that you could imagine it was an 8-bit subtractor which means it can subtract two numbers 256 or smaller but in that process I learned the sheer complexity that goes into processor design things like wire placements that are so close that electrons can cut through the insulation in short and then doing those wire placements across three dimensions to multiple layers jamming in as many logic components as you possibly can and again in my case this was to make a processor that could subtract two numbers but once I was done with this the second part of the course was studying the Pentium processor now remember that moment forever because looking at what the Pentium processor was able to accomplish it was like looking at alien technology and the incredible thing is that Intel our next partner has been able to keep up that alien like pace of innovation twenty years later so we're excited have Doug Fisher here let's hear a little bit more from Intel for business wide open skies an open mind no matter the context the idea of being open almost only suggests the potential of infinite possibilities and that's exactly the power of open source whether it's expanding what's possible in business the science and technology or for the greater good which is why-- open source requires the involvement of a truly diverse community of contributors to scale and succeed creating infinite possibilities for technology and more importantly what we do with it [Music] you know what Intel one of our core values is risk-taking and I'm gonna go just a bit off script for a second and say I was just backstage and I saw a gentleman that looked a lot like Scott Guthrie who runs all of Microsoft's cloud enterprise efforts wearing a red shirt talking to Cormier I'm just saying I don't know maybe I need some more sleep but that's what I saw as we approach Intel's 50th anniversary these words spoken by our co-founder Robert Noyce are as relevant today as they were decades ago don't be encumbered by history this is about breaking boundaries in technology and then go off and do something wonderful is about innovation and driving innovation in our industry and Intel we're constantly looking to break boundaries to advance our technology in the cloud in enterprise space that is no different so I'm going to talk a bit about some of the boundaries we've been breaking and innovations we've been driving at Intel starting with our Intel Xeon platform Orion Xeon scalable platform we launched several months ago which was the biggest and mark the most advanced movement in this technology in over a decade we were able to drive critical performance capabilities unmatched agility and added necessary and sufficient security to that platform I couldn't be happier with the work we do with Red Hat and ensuring that those hero features that we drive into our platform they fully expose to all of you to drive that innovation to go off and do something wonderful well there's taking advantage of the performance features or agility features like our advanced vector extensions or avx-512 or Intel quick exist those technologies are fully embraced by Red Hat Enterprise Linux or whether it's security technologies like txt or trusted execution technology are fully incorporated and we look forward to working with Red Hat on their next release to ensure that our advancements continue to be exposed and their platform and all these workloads that are driving the need for us to break boundaries and our technology are driving more and more need for flexibility and computing and that's why we're excited about Intel's family of FPGAs to help deliver that additional flexibility for you to build those capabilities in your environment we have a broad set of FPGA capabilities from our power fish at Mac's product line all the way to our performance product line on the 6/10 strat exten we have a broad set of bets FPGAs what i've been talking to customers what's really exciting is to see the combination of using our Intel Xeon scalable platform in combination with FPGAs in addition to the acceleration development capabilities we've given to software developers combining all that together to deliver better and better solutions whether it's helping to accelerate data compression well there's pattern recognition or data encryption and decryption one of the things I saw in a data center recently was taking our Intel Xeon scalable platform utilizing the capabilities of FPGA to do data encryption between servers behind the firewall all the while using the FPGA to do that they preserve those precious CPU cycles to ensure they delivered the SLA to the customer yet provided more security for their data in the data center one of the edges in cyber security is innovation and route of trust starts at the hardware we recently renewed our commitment to security with our security first pledge has really three elements to our security first pledge first is customer first urgency we have now completed the release of the micro code updates for protection on our Intel platforms nine plus years since launch to protect against things like the side channel exploits transparent and timely communication we are going to communicate timely and openly on our Intel comm website whether it's about our patches performance or other relevant information and then ongoing security assurance we drive security into every one of our products we redesigned a portion of our processor to add these partition capability which is adding additional walls between applications and user level privileges to further secure that environment from bad actors I want to pause for a second and think everyone in this room involved in helping us work through our security first pledge this isn't something we do on our own it takes everyone in this room to help us do that the partnership and collaboration was next to none it's the most amazing thing I've seen since I've been in this industry so thank you we don't stop there we continue to advance our security capabilities cross-platform solutions we recently had a conference discussion at RSA where we talked about Intel Security Essentials where we deliver a framework of capabilities and the end that are in our silicon available for those to innovate our customers and the security ecosystem to innovate on a platform in a consistent way delivering that assurance that those capabilities will be on that platform we also talked about things like our security threat technology threat detection technology is something that we believe in and we launched that at RSA incorporates several elements one is ability to utilize our internal graphics to accelerate some of the memory scanning capabilities we call this an accelerated memory scanning it allows you to use the integrated graphics to scan memory again preserving those precious cycles on the core processor Microsoft adopted this and are now incorporated into their defender product and are shipping it today we also launched our threat SDK which allows partners like Cisco to utilize telemetry information to further secure their environments for cloud workloads so we'll continue to drive differential experiences into our platform for our ecosystem to innovate and deliver more and more capabilities one of the key aspects you have to protect is data by 2020 the projection is 44 zettabytes of data will be available 44 zettabytes of data by 2025 they project that will grow to a hundred and eighty s data bytes of data massive amount of data and what all you want to do is you want to drive value from that data drive and value from that data is absolutely critical and to do that you need to have that data closer and closer to your computation this is why we've been working Intel to break the boundaries in memory technology with our investment in 3d NAND we're reducing costs and driving up density in that form factor to ensure we get warm data closer to the computing we're also innovating on form factors we have here what we call our ruler form factor this ruler form factor is designed to drive as much dense as you can in a 1u rack we're going to continue to advance the capabilities to drive one petabyte of data at low power consumption into this ruler form factor SSD form factor so our innovation continues the biggest breakthrough and memory technology in the last 25 years in memory media technology was done by Intel we call this our 3d crosspoint technology and our 3d crosspoint technology is now going to be driven into SSDs as well as in a persistent memory form factor to be on the memory bus giving you the speed of memory characteristics of memory as well as the characteristics of storage given a new tier of memory for developers to take full advantage of and as you can see Red Hat is fully committed to integrating this capability into their platform to take full advantage of that new capability so I want to thank Paul and team for engaging with us to make sure that that's available for all of you to innovate on and so we're breaking boundaries and technology across a broad set of elements that we deliver that's what we're about we're going to continue to do that not be encumbered by the past your role is to go off and doing something wonderful with that technology all ecosystems are embracing this and driving it including open source technology open source is a hub of innovation it's been that way for many many years that innovation that's being driven an open source is starting to transform many many businesses it's driving business transformation we're seeing this coming to light in the transformation of 5g driving 5g into the networked environment is a transformational moment an open source is playing a pivotal role in that with OpenStack own out and opie NFV and other open source projects were contributing to and participating in are helping drive that transformation in 5g as you do software-defined networks on our barrier breaking technology we're also seeing this transformation rapidly occurring in the cloud enterprise cloud enterprise are growing rapidly and innovation continues our work with virtualization and KVM continues to be aggressive to adopt technologies to advance and deliver more capabilities in virtualization as we look at this with Red Hat we're now working on Cube vert to help move virtualized workloads onto these platforms so that we can now have them managed at an open platform environment and Cube vert provides that so between Intel and Red Hat and the community we're investing resources to make certain that comes to product as containers a critical feature in Linux becomes more and more prevalent across the industry the growth of container elements continues at a rapid rapid pace one of the things that we wanted to bring to that is the ability to provide isolation without impairing the flexibility the speed and the footprint of a container with our clear container efforts along with hyper run v we were able to combine that and create we call cotta containers we launched this at the end of last year cotta containers is designed to have that container element available and adding elements like isolation both of these events need to have an orchestration and management capability Red Hat's OpenShift provides that capability for these workloads whether containerized or cube vert capabilities with virtual environments Red Hat openshift is designed to take that commercial capability to market and we've been working with Red Hat for several years now to develop what we call our Intel select solution Intel select solutions our Intel technology optimized for downstream workloads as we see a growth in a workload will work with a partner to optimize a solution on Intel technology to deliver the best solution that could be deployed quickly our effort here is to accelerate the adoption of these type of workloads in the market working with Red Hat's so now we're going to be deploying an Intel select solution design and optimized around Red Hat OpenShift we expect the industry's start deploying this capability very rapidly I'm excited to announce today that Lenovo is committed to be the first platform company to deliver this solution to market the Intel select solution to market will be delivered by Lenovo now I talked about what we're doing in industry and how we're transforming businesses our technology is also utilized for greater good there's no better example of this than the worked by dr. Stephen Hawking it was a sad day on March 14th of this year when dr. Stephen Hawking passed away but not before Intel had a 20-year relationship with dr. Hawking driving breakthrough capabilities innovating with him driving those robust capabilities to the rest of the world one of our Intel engineers an Intel fellow which is the highest technical achievement you can reach at Intel got to spend 10 years with dr. Hawking looking at innovative things they could do together with our technology and his breakthrough innovative thinking so I thought it'd be great to bring up our Intel fellow Lema notch Minh to talk about her work with dr. Hawking and what she learned in that experience come on up Elina [Music] great to see you Thanks something going on about the breakthrough breaking boundaries and Intel technology talk about how you use that in your work with dr. Hawking absolutely so the most important part was to really make that technology contextually aware because for people with disability every single interaction takes a long time so whether it was adapting for example the language model of his work predictor to understand whether he's gonna talk to people or whether he's writing a book on black holes or to even understand what specific application he might be using and then making sure that we're surfacing only enough actions that were relevant to reduce that amount of interaction so the tricky part is really to make all of that contextual awareness happen without totally confusing the user because it's constantly changing underneath it so how is that your work involving any open source so you know the problem with assistive technology in general is that it needs to be tailored to the specific disability which really makes it very hard and very expensive because it can't utilize the economies of scale so basically with the system that we built what we wanted to do is really enable unleashing innovation in the world right so you could take that framework you could tailor to a specific sensor for example a brain computer interface or something like that where you could actually then support a different set of users so that makes open-source a perfect fit because you could actually build and tailor and we you spoke with dr. Hawking what was this view of open source is it relevant to him so yeah so Stephen was adamant from the beginning that he wanted a system to benefit the world and not just himself so he spent a lot of time with us to actually build this system and he was adamant from day one that he would only engage with us if we were commit to actually open sourcing the technology that's fantastic and you had the privilege of working with them in 10 years I know you have some amazing stories to share so thank you so much for being here thank you so much in order for us to scale and that's what we're about at Intel is really scaling our capabilities it takes this community it takes this community of diverse capabilities it takes two births thought diverse thought of dr. Hawking couldn't be more relevant but we also are proud at Intel about leading efforts of diverse thought like women and Linux women in big data other areas like that where Intel feels that that diversity of thinking and engagement is critical for our success so as we look at Intel not to be encumbered by the past but break boundaries to deliver the technology that you all will go off and do something wonderful with we're going to remain committed to that and I look forward to continue working with you thank you and have a great conference [Applause] thank God now we have one more customer story for you today when you think about customers challenges in the technology landscape it is hard to ignore the public cloud these days public cloud is introducing capabilities that are driving the fastest rate of innovation that we've ever seen in our industry and our next customer they actually had that same challenge they wanted to tap into that innovation but they were also making bets for the long term they wanted flexibility and providers and they had to integrate to the systems that they already have and they have done a phenomenal job in executing to this so please give a warm welcome to Kerry Pierce from Cathay Pacific Kerry come on thanks very much Matt hi everyone thank you for giving me the opportunity to share a little bit about our our cloud journey let me start by telling you a little bit about Cathay Pacific we're an international airline based in Hong Kong and we serve a passenger and a cargo network to over 200 destinations in 52 countries and territories in the last seventy years and years seventy years we've made substantial investments to develop Hong Kong as one of the world's leading transportation hubs we invest in what matters most to our customers to you focusing on our exemplary service and our great product and it's both on the ground and in the air we're also investing and expanding our network beyond our multiple frequencies to the financial districts such as Tokyo New York and London and we're connecting Asia and Hong Kong with key tech hubs like San Francisco where we have multiple flights daily we're also connecting Asia in Hong Kong to places like Tel Aviv and our upcoming destination of Dublin in fact 2018 is actually going to be one of our biggest years in terms of network expansion and capacity growth and we will be launching in September our longest flight from Hong Kong direct to Washington DC and that'll be using a state-of-the-art Airbus a350 1000 aircraft so that's a little bit about Cathay Pacific let me tell you about our journey through the cloud I'm not going to go into technical details there's far smarter people out in the audience who will be able to do that for you just focus a little bit about what we were trying to achieve and the people side of it that helped us get there we had a couple of years ago no doubt the same issues that many of you do I don't think we're unique we had a traditional on-premise non-standardized fragile infrastructure it didn't meet our infrastructure needs and it didn't meet our development needs it was costly to maintain it was costly to grow and it really inhibited innovation most importantly it slowed the delivery of value to our customers at the same time you had the hype of cloud over the last few years cloud this cloud that clouds going to fix the world we were really keen on making sure we didn't get wound up and that so we focused on what we needed we started bottom up with a strategy we knew we wanted to be clouded Gnostic we wanted to have active active on-premise data centers with a single network and fabric and we wanted public clouds that were trusted and acted as an extension of that environment not independently we wanted to avoid single points of failure and we wanted to reduce inter dependencies by having loosely coupled designs and finally we wanted to be scalable we wanted to be able to cater for sudden surges of demand in a nutshell we kind of just wanted to make everything easier and a management level we wanted to be a broker of services so not one size fits all because that doesn't work but also not one of everything we want to standardize but a pragmatic range of services that met our development and support needs and worked in harmony with our public cloud not against it so we started on a journey with red hat we implemented Red Hat cloud forms and ansible to manage our hybrid cloud we also met implemented Red Hat satellite to maintain a manager environment we built a Red Hat OpenStack on crimson vironment to give us an alternative and at the same time we migrated a number of customer applications to a production public cloud open shift environment but it wasn't all Red Hat you love heard today that the Red Hat fits within an overall ecosystem we looked at a number of third-party tools and services and looked at developing those into our core solution I think at last count we had tried and tested somewhere past eight different tools and at the moment we still have around 62 in our environment that help us through that journey but let me put the technical solution aside a little bit because it doesn't matter how good your technical solution is if you don't have the culture and the people to get it right as a group we needed to be aligned for delivery and we focused on three core behaviors we focused on accountability agility and collaboration now I was really lucky we've got a pretty fantastic team for whom that was actually pretty easy but but again don't underestimate the importance of getting the culture and the people right because all the technology in the world doesn't matter if you don't have that right I asked the team what did we do differently because in our situation we didn't go out and hire a bunch of new people we didn't go out and hire a bunch of consultants we had the staff that had been with us for 10 20 and in some cases 30 years so what did we do differently it was really simple we just empowered and supported our staff we knew they were the smart ones they were the ones that were dealing with a legacy environment and they had the passion to make the change so as a team we encouraged suggestions and contributions from our overall IT community from the bottom up we started small we proved the case we told the story and then we got by him and only did did we implement wider the benefits the benefit through our staff were a huge increase in staff satisfaction reduction and application and platform outage support incidents risk free and failsafe application releases work-life balance no more midnight deployments and our application and infrastructure people could really focus on delivering customer value not on firefighting and for our end customers the people that travel with us it was really really simple we could provide a stable service that allowed for faster releases which meant we could deliver value faster in terms of stats we migrated 16 production b2c applications to a public cloud OpenShift environment in 12 months we decreased provisioning time from weeks or occasionally months we were waiting for hardware two minutes and we had a hundred percent availability of our key customer facing systems but most importantly it was about people we'd built a culture a culture of innovation that was built on a foundation of collaboration agility and accountability and that permeated throughout the IT organization not those just those people that were involved in the project everyone with an IT could see what good looked like and to see what it worked what it looked like in terms of working together and that was a key foundation for us the future for us you will have heard today everything's changing so we're going to continue to develop our open hybrid cloud onboard more public cloud service providers continue to build more modern applications and leverage the emerging technology integrate and automate everything we possibly can and leverage more open source products with the great support from the open source community so there you have it that's our journey I think we succeeded by not being over awed and by starting with the basics the technology was key obviously it's a cool component but most importantly it was a way we approached our transition we had a clear strategy that was actually developed bottom-up by the people that were involved day to day and we empowered those people to deliver and that provided benefits to both our staff and to our customers so thank you for giving the opportunity to share and I hope you enjoy the rest of the summer [Applause] I got one thanks what a great story would a great customer story to close on and we have one more partner to come up and this is a partner that all of you know that's Microsoft Microsoft has gone through an amazing transformation they've we've built an incredibly meaningful partnership with them all the way from our open source collaboration to what we do in the business side we started with support for Red Hat Enterprise Linux on hyper-v and that was truly just the beginning today we're announcing one of the most exciting joint product offerings on the market today let's please give a warm welcome to Paul correr and Scott Scott Guthrie to tell us about it guys come on out you know Scot welcome welcome to the Red Hat summer thanks for coming really appreciate it great to be here you know many surprises a lot of people when we you know published a list of speakers and then you rock you were on it and you and I are on stage here it's really really important and exciting to us exciting new partnership we've worked together a long time from the hypervisor up to common support and now around hybrid hybrid cloud maybe from your perspective a little bit of of what led us here well you know I think the thing that's really led us here is customers and you know Microsoft we've been on kind of a transformation journey the last several years where you know we really try to put customers at the center of everything that we do and you know as part of that you quickly learned from customers in terms of I'm including everyone here just you know you've got a hybrid of state you know both in terms of what you run on premises where it has a lot of Red Hat software a lot of Microsoft software and then really is they take the journey to the cloud looking at a hybrid of state in terms of how do you run that now between on-premises and a public cloud provider and so I think the thing that both of us are recognized and certainly you know our focus here at Microsoft has been you know how do we really meet customers with where they're at and where they want to go and make them successful in that journey and you know it's been fantastic working with Paul and the Red Hat team over the last two years in particular we spend a lot of time together and you know really excited about the journey ahead so um maybe you can share a bit more about the announcement where we're about to make today yeah so it's it's it's a really exciting announcement it's and really kind of I think first of its kind in that we're delivering a Red Hat openshift on Azure service that we're jointly developing and jointly managing together so this is different than sort of traditional offering where it's just running inside VMs and it's sort of two vendors working this is really a jointly managed service that we're providing with full enterprise support with a full SLA where the you know single throat to choke if you will although it's collectively both are choke the throats in terms of making sure that it works well and it's really uniquely designed around this hybrid world and in that it supports will support both Windows and Linux containers and it role you know it's the same open ship that runs both in the public cloud on Azure and on-premises and you know it's something that we hear a lot from customers I know there's a lot of people here that have asked both of us for this and super excited to be able to talk about it today and we're gonna show off the first demo of it just a bit okay well I'm gonna ask you to elaborate a bit more about this how this fits into the bigger Microsoft picture and I'll get out of your way and so thanks again thank you for coming here we go thanks Paul so I thought I'd spend just a few minutes talking about wouldn't you know that some of the work that we're doing with Microsoft Asher and the overall Microsoft cloud I didn't go deeper in terms of the new offering that we're announcing today together with red hat and show demo of it actually in action in a few minutes you know the high level in terms of you know some of the work that we've been doing at Microsoft the last couple years you know it's really been around this this journey to the cloud that we see every organization going on today and specifically the Microsoft Azure we've been providing really a cloud platform that delivers the infrastructure the application and kind of the core computing needs that organizations have as they want to be able to take advantage of what the cloud has to offer and in terms of our focus with Azure you know we've really focused we deliver lots and lots of different services and features but we focused really in particular on kind of four key themes and we see these four key themes aligning very well with the journey Red Hat it's been on and it's partly why you know we think the partnership between the two companies makes so much sense and you know for us the thing that we've been really focused on has been with a or in terms of how do we deliver a really productive cloud meaning how do we enable you to take advantage of cutting-edge technology and how do we kind of accelerate the successful adoption of it whether it's around the integration of managed services that we provide both in terms of the application space in the data space the analytic and AI space but also in terms of just the end-to-end management and development tools and how all those services work together so that teams can basically adopt them and be super successful yeah we deeply believe in hybrid and believe that the world is going to be a multi cloud and a multi distributed world and how do we enable organizations to be able to take the existing investments that they already have and be able to easily integrate them in a public cloud and with a public cloud environment and get immediate ROI on day one without how to rip and replace tons of solutions you know we're moving very aggressively in the AI space and are looking to provide a rich set of AI services both finished AI models things like speech detection vision detection object motion etc that any developer even at non data scientists can integrate to make application smarter and then we provide a rich set of AI tooling that enables organizations to build custom models and be able to integrate them also as part of their applications and with their data and then we invest very very heavily on trust Trust is sort of at the core of a sure and we now have more compliant certifications than any other cloud provider we run in more countries than any other cloud provider and we really focus around unique promises around data residency data sovereignty and privacy that are really differentiated across the industry and terms of where Iser runs today we're in 50 regions around the world so our region for us is typically a cluster of multiple data centers that are grouped together and you can see we're pretty much on every continent with the exception of Antarctica today and the beauty is you're going to be able to take the Red Hat open shift service and run it on ashore in each of these different locations and really have a truly global footprint as you look to build and deploy solutions and you know we've seen kind of this focus on productivity hybrid intelligence and Trust really resonate in the market and about 90 percent of Fortune 500 companies today are deployed on Azure and you heard Nike talked a little bit earlier this afternoon about some of their journeys as they've moved to a dot public cloud this is a small logo of just a couple of the companies that are on ashore today and what I do is actually even before we dive into the open ship demo is actually just show a quick video you know one of the companies thing there are actually several people from that organization here today Deutsche Bank who have been working with both Microsoft and Red Hat for many years Microsoft on the other side Red Hat both on the rel side and then on the OpenShift side and it's just one of these customers that have helped bring the two companies together to deliver this managed openshift service on Azure and so I'm just going to play a quick video of some of the folks that Deutsche Bank talking about their experiences and what they're trying to get out of it so we could roll the video that'd be great technology is at the absolute heart of Deutsche Bank we've recognized that the cost of running our infrastructure was particularly high there was a enormous amount of under utilization we needed a platform which was open to polyglot architecture supporting any kind of application workload across the various business lines of the third we analyzed over 60 different vendor products and we ended up with Red Hat openshift I'm super excited Microsoft or supporting Linux so strongly to adopting a hybrid approach we chose as here because Microsoft was the ideal partner to work with on constructs around security compliance business continuity as you as in all the places geographically that we need to be we have applications now able to go from a proof of concept to production in three weeks that is already breaking records openshift gives us given entities and containers allows us to apply the same sets of processes automation across a wide range of our application landscape on any given day we run between seven and twelve thousand containers across three regions we start see huge levels of cost reduction because of the level of multi-tenancy that we can achieve through containers open ship gives us an abstraction layer which is allows us to move our applications between providers without having to reconfigure or recode those applications what's really exciting for me about this journey is the way they're both Red Hat and Microsoft have embraced not just what we're doing but what each other are doing and have worked together to build open shift as a first-class citizen with Microsoft [Applause] in terms of what we're announcing today is a new fully managed OpenShift service on Azure and it's really the first fully managed service provided end-to-end across any of the cloud providers and it's jointly engineer operated and supported by both Microsoft and Red Hat and that means again sort of one service one SLA and both companies standing for a link firmly behind it really again focusing around how do we make customers successful and as part of that really providing the enterprise-grade not just isolates but also support and integration testing so you can also take advantage of all your rel and linux-based containers and all of your Windows server based containers and how can you run them in a joint way with a common management stack taking the advantage of one service and get maximum density get maximum code reuse and be able to take advantage of a containerized world in a better way than ever before and make this customer focus is very much at the center of what both companies are really centered around and so what if I do be fun is rather than just talk about openshift as actually kind of show off a little bit of a journey in terms of what this move to take advantage of it looks like and so I'd like to invite Brendan and Chris onstage who are actually going to show off a live demo of openshift on Azure in action and really walk through how to provision the service and basically how to start taking advantage of it using the full open ship ecosystem so please welcome Brendan and Chris we're going to join us on stage for a demo thanks God thanks man it's been a good afternoon so you know what we want to get into right now first I'd like to think Brandon burns for joining us from Microsoft build it's a busy week for you I'm sure your own stage there a few times as well you know what I like most about what we just announced is not only the business and technical aspects but it's that operational aspect the uniqueness the expertise that RedHat has for running OpenShift combined with the expertise that Microsoft has within Azure and customers are going to get this joint offering if you will with you know Red Hat OpenShift on Microsoft Azure and so you know kind of with that again Brendan I really appreciate you being here maybe talk to the folks about what we're going to show yeah so we're going to take a look at what it looks like to deploy OpenShift on to Azure via the new OpenShift service and the real selling point the really great part of this is the the deep integration with a cloud native app API so the same tooling that you would use to create virtual machines to create disks trade databases is now the tooling that you're going to use to create an open chip cluster so to show you this first we're going to create a resource group here so we're going to create that resource group in East us using the AZ tool that's the the azure command-line tooling a resource group is sort of a folder on Azure that holds all of your stuff so that's gonna come back into the second I've created my resource group in East us and now we're gonna use that exact same tool calling into into Azure api's to provision an open shift cluster so here we go we have AZ open shift that's our new command line tool putting it into that resource group I'm gonna get into East us alright so it's gonna take a little bit of time to deploy that open shift cluster it's doing a bunch of work behind the scenes provisioning all kinds of resources as well as credentials to access a bunch of different as your API so are we actually able to see this to you yeah so we can cut over to in just a second we can cut over to that resource group in a reload so Brendan while relating the beauty of what you know the teams have been doing together already is the fact that now open shift is a first-class citizen as it were yeah absolutely within the agent so I presume not only can I do a deployment but I can do things like scale and check my credentials and pretty much everything that I could do with any other service with that that's exactly right so we can anything that you you were used to doing via the my computer has locked up there we go the demo gods are totally with me oh there we go oh no I hit reload yeah that was that was just evil timing on the house this is another use for operators as we talked about earlier today that's right my dashboard should be coming up do I do I dare click on something that's awesome that was totally it was there there we go good job so what's really interesting about this I've also heard that it deploys you know in as little as five to six minutes which is really good for customers they want to get up and running with it but all right there we go there it is who managed to make it see that shows that it's real right you see the sweat coming off of me there but there you can see the I feel it you can see the various resources that are being created in order to create this openshift cluster virtual machines disks all of the pieces provision for you automatically via that one single command line call now of course it takes a few minutes to to create the cluster so in order to show the other side of that integration the integration between openshift and Azure I'm going to cut over to an open shipped cluster that I already have created alright so here you can see my open shift cluster that's running on Microsoft Azure I'm gonna actually log in over here and the first sign you're gonna see of the integration is it's actually using my credentials my login and going through Active Directory and any corporate policies that I may have around smart cards two-factor off anything like that authenticate myself to that open chef cluster so I'll accept that it can access my and now we're gonna load up the OpenShift web console so now this looks familiar to me oh yeah so if anybody's used OpenShift out there this is the exact same console and what we're going to show though is how this console via the open service broker and the open service broker implementation for Azure integrates natively with OpenShift all right so we can go down here and we can actually see I want to deploy a database I'm gonna deploy Mongo as my key value store that I'm going to use but you know like as we talk about management and having a OpenShift cluster that's managed for you I don't really want to have to manage my database either so I'm actually going to use cosmos DB it's a native Azure service it's a multilingual database that offers me the ability to access my data in a variety of different formats including MongoDB fully managed replicated around the world a pretty incredible service so I'm going to go ahead and create that so now Brendan what's interesting I think to me is you know we talked about the operational aspects and clearly it's not you and I running the clusters but you do need that way to interface with it and so when customers are able to deploy this all of this is out of the box there's no additional contemporary like this is what you get when you create when you use that tool to create that open chef cluster this is what you get with all of that integration ok great step through here and go ahead don't have any IP ranges there we go all right and we create that binding all right and so now behind the scenes openshift is integrated with the azure api's with all of my credentials to go ahead and create that distributed database once it's done provisioning actually all of the credentials necessary to access the database are going to be automatically populated into kubernetes available for me inside of OpenShift via service discovery to access from my application without any further work so I think that really shows not only the power of integrating openshift with an azure based API but actually the power of integrating a Druze API is inside of OpenShift to make a truly seamless experience for managing and deploying your containers across a variety of different platforms yeah hey you know Brendan this is great I know you've got a flight to catch because I think you're back onstage in a few hours but you know really appreciate you joining us today absolutely I look forward to seeing what else we do yeah absolutely thank you so much thanks guys Matt you want to come back on up thanks a lot guys if you have never had the opportunity to do a live demo in front of 8,000 people it'll give you a new appreciation for standing up there and doing it and that was really good you know every time I get the chance just to take a step back and think about the technology that we have at our command today I'm in awe just the progress over the last 10 or 20 years is incredible on to think about what might come in the next 10 or 20 years really is unthinkable you even forget 10 years what might come in the next five years even the next two years but this can create a lot of uncertainty in the environment of what's going to be to come but I believe I am certain about one thing and that is if ever there was a time when any idea is achievable it is now just think about what you've seen today every aspect of open hybrid cloud you have the world's infrastructure at your fingertips and it's not stopping you've heard about this the innovation of open source how fast that's evolving and improving this capability you've heard this afternoon from an entire technology ecosystem that's ready to help you on this journey and you've heard from customer after customer that's already started their journey in the successes that they've had you're one of the neat parts about this afternoon you will aren't later this week you will actually get to put your hands on all of this technology together in our live audience demo you know this is what some it's all about for us it's a chance to bring together the technology experts that you can work with to help formulate how to pull off those ideas we have the chance to bring together technology experts our customers and our partners and really create an environment where everyone can experience the power of open source that same spark that I talked about when I was at IBM where I understood the but intial that open-source had for enterprise customers we want to create the environment where you can have your own spark you can have that same inspiration let's make this you know in tomorrow's keynote actually you will hear a story about how open-source is changing medicine as we know it and literally saving lives it is a great example of expanding the ideas it might be possible that we came into this event with so let's make this the best summit ever thank you very much for being here let's kick things off right head down to the Welcome Reception in the expo hall and please enjoy the summit thank you all so much [Music] [Music]

>> Announcer: Live from Midtown Manhattan it's theCUBE. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> OK, welcome back everyone we're here in New York City it's theCUBE's exclusive coverage of Big Data NYC, in conjunction with Strata Data going on right around the corner. It's out third day talking to all the influencers, CEO's, entrepreneurs, people making it happen in the Big Data world. I'm John Furrier co-host of theCUBE, with my co-host here Jim Kobielus who is the Lead Analyst at Wikibon Big Data. Nenshad Bardoliwalla. >> Bar-do-li-walla. >> Bardo. >> Nenshad Bardoliwalla. >> That guy. >> Okay, done. Of Paxata, Co-Founder & Chief Product Officer it's a tongue twister, third day, being from Jersey, it's hard with our accent, but thanks for being patient with me. >> Happy to be here. >> Pranav Rastogi, Product Manager, Microsoft Azure. Guys, welcome back to theCUBE, good to see you. I apologize for that, third day blues here. So Paxata, we had your partner on Prakash. >> Prakash. >> Prakash. Really a success story, you guys have done really well launching theCUBE fun to watch you guys from launching to the success. Obviously your relationship with Microsoft super important. Talk about the relationship because I think this is really people can start connecting the dots. >> Sure, maybe I'll start and I'LL be happy to get Pranav's point of view as well. Obviously Microsoft is one of the leading brands in the world and there are many aspects of the way that Microsoft has thought about their product development journey that have really been critical to the way that we have thought about Paxata as well. If you look at the number one tool that's used by analysts the world over it's Microsoft Excel. Right, there isn't even anything that's a close second. And if you look at the the evolution of what Microsoft has done in many layers of the stack, whether it's the end user computing paradigm that Excel provides to the world. Whether it's all of their recent innovation in both hybrid cloud technologies as well as the big data technologies that Pranav is part of managing. We just see a very strong synergy between trying to combine the usage by business consumers of being able to take advantage of these big data technologies in a hybrid cloud environment. So there's a very natural resonance between the 2 companies. We're very privileged to have Microsoft Ventures as an investor in Paxata and so the opportunity for us to work with one of the great brands of all time in our industry was really a privilege for us. Yeah, and that's the corporate sides so that wasn't actually part of it. So it's a different part of Microsoft which is great. You have also business opportunity with them. >> Nenshad : We do. >> Obviously data science problem that we're seeing is that they need to get the data faster. All that prep work, seems to be the big issue. >> It does and maybe we can get Pranav's point of view from the Microsoft angle. >> Yeah so to sort of continue what Nenshad was saying, you know the data prep in general is sort of a key core competence which is problematic for lots of users, especially around the knowledge that you need to have in terms of the different tools you can use. Folks who are very proficient will do ETL or data preparation like scenarios using one of the computing engines like Hive or Spark. That's good, but there's this big audience out there who like Excel-like interface, which is easy to use a very visually rich graphical interface where you can drag and drop and can click through. And the idea behind all of this is how quickly can I get insights from my data faster. Because in a big data space, it's volume, variety and velocity. So data is coming at a very fast rate. It's changing it's growing. And if you spend lot of time just doing data prep you're losing the value of data, or the value of data would change over time. So what we're trying to do would sort of enabling Paxata or HDInsight is enabling these users to use Paxata, get insights from data faster by solving key problems of doing data prep. >> So data democracy is a term that we've been kicking around, you guys have been talking about as well. What is actually mean, because we've been teasing out first two days here at theCUBE and BigData NYC is. It's clear the community aspect of data is growing, almost on a similar path as you're seeing with open source software. That genie's out the bottle. Open source software, tier one, it won, it's only growing exponentially. That same paradigm is moving into the data world where the collaboration is super important, in this data democracy, what is that actually mean and how does that relate to you guys? >> So the perspective we have is that first something that one of our customers said, that is there is no democracy without certain degrees of governance. We all live in a in a democracy. And yet we still have rules that we have to abide by. There are still policies that society needs to follow in order for us to be successful citizens. So when when a lot of folks hear the term democracy they really think of the wild wild west, you know. And a lot of the analytic work in the enterprise does have that flavor to it, right, people download stuff to their desktop, they do a little bit of massaging of the data. They email that to their friend, their friend then makes some changes and next thing you know we have what what some folks affectionately call spread mart hell. But if you really want to democratize the technology you have to wrap not only the user experience, like Pranav described, into something that's consumable by a very large number of folks in the enterprise. You have to wrap that with the governance and collaboration capabilities so that multiple people can work off the same data set. That you can apply the permissions so that people, who is allowed to share with each other and under what circumstances are they allowed to share. Under what circumstances are you allowed to promote data from one environment to another? It may be okay for someone like me to work in a sandbox but I cannot push that to a database or HDFS or Azure BLOB storage unless I actually have the right permissions to do so. So I think what you're seeing is that, in general, technology is becoming a, always goes on this trend, towards democratization. Whether it's the phone, whether it's the television, whether it's the personal computer and the same thing is happening with data technologies and certainly companies like. >> Well, Pranav, we're talking about this when you were on theCUBE yesterday. And I want to get your thoughts on this. The old way to solve the governance problem was to put data in silos. That was easy, I'll just put it in a silo and take care of it and access control was different. But now the value of the data is about cross-pollinating and make it freely available, horizontally scalable, so that it can be used. But the same time and you need to have a new governance paradigm. So, you've got to democratize the data by making it available, addressable and use for apps. The same time there's also the concerns on how do you make sure it doesn't get in the wrong hands and so on and so forth. >> Yeah and which is also very sort of common regarding open source projects in the cloud is a how do you ensure that the user authorized to access this open source project or run it has the right credentials is authorized and stuff. So, the benefit that you sort of get in the cloud is there's a centralized authentication system. There's Azure Active Directory, so you know most enterprise would have Active Directory users. Who are then authorized to either access maybe this cluster, or maybe this workload and they can run this job and that sort of further that goes down to the data layer as well. Where we have active policies which then describe what user can access what files and what folders. So if you think about the entrance scenario there is authentication and authorization happening and for the entire system when what user can access what data. And part of what Paxata brings in the picture is like how do you visualize this governance flow as data is coming from various sources, how do you make sure that the person who has access to data does have access data, and the one who doesn't cannot access data. >> Is that the problem with data prep is just that piece of it? What is the big problem with data prep, I mean, that seems to be, everyone keeps coming back to the same problem. What is causing all this data prep. >> People not buying Paxata it's very simple. >> That's a good one. Check out Paxata they're going to solve your problems go. But seriously, there seems to be the same hole people keep digging themselves into. They gather their stuff then next thing they're in the in the same hole they got to prepare all this stuff. >> I think the previous paradigms for doing data preparation tie exactly to the data democracy themes that we're talking about here. If you only have a very silo'd group of people in the organization with very deep technical skills but don't have the business context for what they're actually trying to accomplish, you have this impedance mismatch in the organization between the people who know what they want and the people who have the tools to do it. So what we've tried to do, and again you know taking a page out of the way that Microsoft has approached solving these problems you know both in the past in the present. Is to say look we can actually take the tools that once were only in the hands of the, you know, shamans who know how to utter the right incantations and instead move that into the the common folk who actually. >> The users. >> The users themselves who know what they want to do with the data. Who understand what those data elements mean. So if you were to ask the Paxata point of view, why have we had these data prep problems? Because we've separated the people who had the tools from the people who knew what they wanted to do with it. >> So it sounds to me, correct me if this is the wrong term, that what you offer in your partnership is it basically a broad curational environment for knowledge workers. You know, to sift and sort and annotating shared data with the lineage of the data preserved in essentially a system of record that can follow the data throughout its natural life. Is that a fair characterization? >> Pranav: I would think so yeah. >> You mention, Pranav, the whole issue of how one visualizes or should visualize this entire chain of custody, as it were, for the data, is there is there any special visualization paradigm that you guys offer? Now Microsoft, you've made a fairly significant investment in graph technology throughout your portfolio. I was at Build back in May and Sacha and the others just went to town on all things to do with Microsoft Graph, will that technology be somehow at some point, now or in the future, be reflected in this overall capability that you've established here with your partner here Paxata? >> I am not sure. So far, I think what you've talked about is some Graph capabilities introduced from the Microsoft Graph that's sort of one extreme. The other side of Graph exists today as a developer you can do some Graph based queries. So you can go to Cosmos DB which had a Gremlin API. For Graph based query, so I don't know how. >> I'll get right to the question. What's the Paxata benefits of with HDInsight? How does that, just quickly, explain for the audience. What is that solution, what are the benefits? >> So the the solution is you get a one click install of installing Paxata HDInsight and the benefit is as a benefit for a user persona who's not, sort of, used to big data or Hadoop they can use a very familiar GUI-based experience to get their insights from data faster without having any knowledge of how Spark works or Hadoop works. >> And what does the Microsoft relationship bring to the table for Paxata? >> So I think it's a couple of things. One is Azure is clearly growing at an extremely fast pace. And a lot of the enterprise customers that we work with are moving many of their workloads to Azure and and these cloud based environments. Especially for us, the unique value proposition of a partner who truly understands the hybrid nature of the world. The idea that everything is going to move to the cloud or everything is going to stay on premise is too simplistic. Microsoft understood that from day one. That data would be in it and all of those different places. And they've provided enabling technologies for vendors like us. >> I'll just say it to maybe you're too coy to say it, but the bottom line is you have an Excel-like interface. They have Office 365 they're user's going to instantly love that interface because it's an easy to use interface an Excel-like it's not Excel interface per se. >> Similar. >> Metaphor, graphical user interface. >> Yes it is. >> It's clean and it's targeted at the analyst role or user. >> That's right. >> That's going to resonate in their install base. >> And combined with a lot of these new capabilities that Microsoft is rolling out from a big data perspective. So HDInsight has a very rich portfolio of runtime engines and capabilities. They're introducing new data storage layers whether it's ADLS or Azure BLOB storage, so it's really a nice way of us working together to extract and unlock a lot of the value that Microsoft. >> So, here's the tough question for you, open source projects I see Microsoft, comments were hell froze because LINUX is now part of their DNA, which was a comment I saw at the even this week in Orlando, but they're really getting behind open source. From open compute, it's just clearly new DNA's. They're they're into it. How are you guys working together in open source and what's the impact to developers because now that's only one cloud, there's other clouds out there so data's going to be an important part of it. So open source, together, you guys working together on that and what's the role for the data? >> From an open source perspective, Microsoft plays a big role in embracing open source technologies and making sure that it runs reliably in the cloud. And part of that value prop that we provide in sort of Azure HDInsight is being sure that you can run these open source big data workloads reliably in the cloud. So you can run open source like Apache, Spark, Hive, Storm, Kafka, R Server. And the hard part about running open source technology in the cloud is how do you fine tune it, and how do you configure it, how do you run it reliably. And that's what sort of what we bring in from a cloud perspective. And we also contribute back to the community based on sort of what learned by running these workloads in the cloud. And we believe you know in the broader ecosystem customers will sort of have a mixture of these combinations and their solution They'll be using some of the Microsoft solutions some open source solutions some solutions from ecosystem that's how we see our customer solution sort of being built today. >> What's the big advantage you guys have at Paxata? What's the key differentiator for why someone should work with you guys? Is it the automation? What's the key secret sauce to you guys? >> I think it's a couple of dimensions. One is I think we have come the closest in the industry to getting a user experience that matches the Excel target user. A lot of folks are attempting to do the same but the feedback we consistently get is that when the Excel user uses our solution they just, they get it. >> Was there a design criteria, was that from the beginning how you were going to do this? >> From day one. >> So you engineer everything to make it as simple as like Excel. >> We want people to use our system they shouldn't be coding, they shouldn't be writing scripts. They just need to be able. >> Good Excel you just do good macros though. >> That's right. >> So simple things like that right. >> But the second is being able to interact with the data at scale. There are a lot of solutions out there that make the mistake in our opinion of sampling very tiny amounts of data and then asking you to draw inferences and then publish that to batch jobs. Our whole approach is to smash the batch paradigm and actually bring as much into the interactive world as possible. So end users can actually point and click on 100 million rows of data, instead of the million that you would get in Excel, and get an instantaneous response. Verses designing a job in a batch paradigm and then pushing it through the the batch. >> So it's interactive data profiling over vast corpuses of data in the cloud. >> Nenshad: Correct. >> Nenshad Bardoliwalla thanks for coming on theCUBE appreciate it, congratulations on Paxata and Microsoft Azure, great to have you. Good job on everything you do with Azure. I want to give you guys props, with seeing the growth in the market and the investment's been going well, congratulations. Thanks for sharing, keep coverage here in BigData NYC more coming after this short break.