(upbeat music) >> Hello, this is Dave Valente with theCUBE, and one of the most gratifying aspects of my role as a host of "theCUBE TV" is I get to cover a wide range of topics. And quite often, we're able to bring to our program a level of expertise that allows us to more deeply explore and unpack some of the topics that we cover throughout the year. And one of our favorite topics, of course, is data. Now, in 2021, after being in isolation for the better part of two years, a group of industry analysts met up at AWS re:Invent and started a collaboration to look at the trends in data and predict what some likely outcomes will be for the coming year. And it resulted in a very popular session that we had last year focused on the future of data management. And I'm very excited and pleased to tell you that the 2023 edition of that predictions episode is back, and with me are five outstanding market analyst, Sanjeev Mohan of SanjMo, Tony Baer of dbInsight, Carl Olofson from IDC, Dave Menninger from Ventana Research, and Doug Henschen, VP and Principal Analyst at Constellation Research. Now, what is it that we're calling you, guys? A data pack like the rat pack? No, no, no, no, that's not it. It's the data crowd, the data crowd, and the crowd includes some of the best minds in the data analyst community. They'll discuss how data management is evolving and what listeners should prepare for in 2023. Guys, welcome back. Great to see you. >> Good to be here. >> Thank you. >> Thanks, Dave. (Tony and Dave faintly speaks) >> All right, before we get into 2023 predictions, we thought it'd be good to do a look back at how we did in 2022 and give a transparent assessment of those predictions. So, let's get right into it. We're going to bring these up here, the predictions from 2022, they're color-coded red, yellow, and green to signify the degree of accuracy. And I'm pleased to report there's no red. Well, maybe some of you will want to debate that grading system. But as always, we want to be open, so you can decide for yourselves. So, we're going to ask each analyst to review their 2022 prediction and explain their rating and what evidence they have that led them to their conclusion. So, Sanjeev, please kick it off. Your prediction was data governance becomes key. I know that's going to knock you guys over, but elaborate, because you had more detail when you double click on that. >> Yeah, absolutely. Thank you so much, Dave, for having us on the show today. And we self-graded ourselves. I could have very easily made my prediction from last year green, but I mentioned why I left it as yellow. I totally fully believe that data governance was in a renaissance in 2022. And why do I say that? You have to look no further than AWS launching its own data catalog called DataZone. Before that, mid-year, we saw Unity Catalog from Databricks went GA. So, overall, I saw there was tremendous movement. When you see these big players launching a new data catalog, you know that they want to be in this space. And this space is highly critical to everything that I feel we will talk about in today's call. Also, if you look at established players, I spoke at Collibra's conference, data.world, work closely with Alation, Informatica, a bunch of other companies, they all added tremendous new capabilities. So, it did become key. The reason I left it as yellow is because I had made a prediction that Collibra would go IPO, and it did not. And I don't think anyone is going IPO right now. The market is really, really down, the funding in VC IPO market. But other than that, data governance had a banner year in 2022. >> Yeah. Well, thank you for that. And of course, you saw data clean rooms being announced at AWS re:Invent, so more evidence. And I like how the fact that you included in your predictions some things that were binary, so you dinged yourself there. So, good job. Okay, Tony Baer, you're up next. Data mesh hits reality check. As you see here, you've given yourself a bright green thumbs up. (Tony laughing) Okay. Let's hear why you feel that was the case. What do you mean by reality check? >> Okay. Thanks, Dave, for having us back again. This is something I just wrote and just tried to get away from, and this just a topic just won't go away. I did speak with a number of folks, early adopters and non-adopters during the year. And I did find that basically that it pretty much validated what I was expecting, which was that there was a lot more, this has now become a front burner issue. And if I had any doubt in my mind, the evidence I would point to is what was originally intended to be a throwaway post on LinkedIn, which I just quickly scribbled down the night before leaving for re:Invent. I was packing at the time, and for some reason, I was doing Google search on data mesh. And I happened to have tripped across this ridiculous article, I will not say where, because it doesn't deserve any publicity, about the eight (Dave laughing) best data mesh software companies of 2022. (Tony laughing) One of my predictions was that you'd see data mesh washing. And I just quickly just hopped on that maybe three sentences and wrote it at about a couple minutes saying this is hogwash, essentially. (laughs) And that just reun... And then, I left for re:Invent. And the next night, when I got into my Vegas hotel room, I clicked on my computer. I saw a 15,000 hits on that post, which was the most hits of any single post I put all year. And the responses were wildly pro and con. So, it pretty much validates my expectation in that data mesh really did hit a lot more scrutiny over this past year. >> Yeah, thank you for that. I remember that article. I remember rolling my eyes when I saw it, and then I recently, (Tony laughing) I talked to Walmart and they actually invoked Martin Fowler and they said that they're working through their data mesh. So, it takes a really lot of thought, and it really, as we've talked about, is really as much an organizational construct. You're not buying data mesh >> Bingo. >> to your point. Okay. Thank you, Tony. Carl Olofson, here we go. You've graded yourself a yellow in the prediction of graph databases. Take off. Please elaborate. >> Yeah, sure. So, I realized in looking at the prediction that it seemed to imply that graph databases could be a major factor in the data world in 2022, which obviously didn't become the case. It was an error on my part in that I should have said it in the right context. It's really a three to five-year time period that graph databases will really become significant, because they still need accepted methodologies that can be applied in a business context as well as proper tools in order for people to be able to use them seriously. But I stand by the idea that it is taking off, because for one thing, Neo4j, which is the leading independent graph database provider, had a very good year. And also, we're seeing interesting developments in terms of things like AWS with Neptune and with Oracle providing graph support in Oracle database this past year. Those things are, as I said, growing gradually. There are other companies like TigerGraph and so forth, that deserve watching as well. But as far as becoming mainstream, it's going to be a few years before we get all the elements together to make that happen. Like any new technology, you have to create an environment in which ordinary people without a whole ton of technical training can actually apply the technology to solve business problems. >> Yeah, thank you for that. These specialized databases, graph databases, time series databases, you see them embedded into mainstream data platforms, but there's a place for these specialized databases, I would suspect we're going to see new types of databases emerge with all this cloud sprawl that we have and maybe to the edge. >> Well, part of it is that it's not as specialized as you might think it. You can apply graphs to great many workloads and use cases. It's just that people have yet to fully explore and discover what those are. >> Yeah. >> And so, it's going to be a process. (laughs) >> All right, Dave Menninger, streaming data permeates the landscape. You gave yourself a yellow. Why? >> Well, I couldn't think of a appropriate combination of yellow and green. Maybe I should have used chartreuse, (Dave laughing) but I was probably a little hard on myself making it yellow. This is another type of specialized data processing like Carl was talking about graph databases is a stream processing, and nearly every data platform offers streaming capabilities now. Often, it's based on Kafka. If you look at Confluent, their revenues have grown at more than 50%, continue to grow at more than 50% a year. They're expected to do more than half a billion dollars in revenue this year. But the thing that hasn't happened yet, and to be honest, they didn't necessarily expect it to happen in one year, is that streaming hasn't become the default way in which we deal with data. It's still a sidecar to data at rest. And I do expect that we'll continue to see streaming become more and more mainstream. I do expect perhaps in the five-year timeframe that we will first deal with data as streaming and then at rest, but the worlds are starting to merge. And we even see some vendors bringing products to market, such as K2View, Hazelcast, and RisingWave Labs. So, in addition to all those core data platform vendors adding these capabilities, there are new vendors approaching this market as well. >> I like the tough grading system, and it's not trivial. And when you talk to practitioners doing this stuff, there's still some complications in the data pipeline. And so, but I think, you're right, it probably was a yellow plus. Doug Henschen, data lakehouses will emerge as dominant. When you talk to people about lakehouses, practitioners, they all use that term. They certainly use the term data lake, but now, they're using lakehouse more and more. What's your thoughts on here? Why the green? What's your evidence there? >> Well, I think, I was accurate. I spoke about it specifically as something that vendors would be pursuing. And we saw yet more lakehouse advocacy in 2022. Google introduced its BigLake service alongside BigQuery. Salesforce introduced Genie, which is really a lakehouse architecture. And it was a safe prediction to say vendors are going to be pursuing this in that AWS, Cloudera, Databricks, Microsoft, Oracle, SAP, Salesforce now, IBM, all advocate this idea of a single platform for all of your data. Now, the trend was also supported in 2023, in that we saw a big embrace of Apache Iceberg in 2022. That's a structured table format. It's used with these lakehouse platforms. It's open, so it ensures portability and it also ensures performance. And that's a structured table that helps with the warehouse side performance. But among those announcements, Snowflake, Google, Cloud Era, SAP, Salesforce, IBM, all embraced Iceberg. But keep in mind, again, I'm talking about this as something that vendors are pursuing as their approach. So, they're advocating end users. It's very cutting edge. I'd say the top, leading edge, 5% of of companies have really embraced the lakehouse. I think, we're now seeing the fast followers, the next 20 to 25% of firms embracing this idea and embracing a lakehouse architecture. I recall Christian Kleinerman at the big Snowflake event last summer, making the announcement about Iceberg, and he asked for a show of hands for any of you in the audience at the keynote, have you heard of Iceberg? And just a smattering of hands went up. So, the vendors are ahead of the curve. They're pushing this trend, and we're now seeing a little bit more mainstream uptake. >> Good. Doug, I was there. It was you, me, and I think, two other hands were up. That was just humorous. (Doug laughing) All right, well, so I liked the fact that we had some yellow and some green. When you think about these things, there's the prediction itself. Did it come true or not? There are the sub predictions that you guys make, and of course, the degree of difficulty. So, thank you for that open assessment. All right, let's get into the 2023 predictions. Let's bring up the predictions. Sanjeev, you're going first. You've got a prediction around unified metadata. What's the prediction, please? >> So, my prediction is that metadata space is currently a mess. It needs to get unified. There are too many use cases of metadata, which are being addressed by disparate systems. For example, data quality has become really big in the last couple of years, data observability, the whole catalog space is actually, people don't like to use the word data catalog anymore, because data catalog sounds like it's a catalog, a museum, if you may, of metadata that you go and admire. So, what I'm saying is that in 2023, we will see that metadata will become the driving force behind things like data ops, things like orchestration of tasks using metadata, not rules. Not saying that if this fails, then do this, if this succeeds, go do that. But it's like getting to the metadata level, and then making a decision as to what to orchestrate, what to automate, how to do data quality check, data observability. So, this space is starting to gel, and I see there'll be more maturation in the metadata space. Even security privacy, some of these topics, which are handled separately. And I'm just talking about data security and data privacy. I'm not talking about infrastructure security. These also need to merge into a unified metadata management piece with some knowledge graph, semantic layer on top, so you can do analytics on it. So, it's no longer something that sits on the side, it's limited in its scope. It is actually the very engine, the very glue that is going to connect data producers and consumers. >> Great. Thank you for that. Doug. Doug Henschen, any thoughts on what Sanjeev just said? Do you agree? Do you disagree? >> Well, I agree with many aspects of what he says. I think, there's a huge opportunity for consolidation and streamlining of these as aspects of governance. Last year, Sanjeev, you said something like, we'll see more people using catalogs than BI. And I have to disagree. I don't think this is a category that's headed for mainstream adoption. It's a behind the scenes activity for the wonky few, or better yet, companies want machine learning and automation to take care of these messy details. We've seen these waves of management technologies, some of the latest data observability, customer data platform, but they failed to sweep away all the earlier investments in data quality and master data management. So, yes, I hope the latest tech offers, glimmers that there's going to be a better, cleaner way of addressing these things. But to my mind, the business leaders, including the CIO, only want to spend as much time and effort and money and resources on these sorts of things to avoid getting breached, ending up in headlines, getting fired or going to jail. So, vendors bring on the ML and AI smarts and the automation of these sorts of activities. >> So, if I may say something, the reason why we have this dichotomy between data catalog and the BI vendors is because data catalogs are very soon, not going to be standalone products, in my opinion. They're going to get embedded. So, when you use a BI tool, you'll actually use the catalog to find out what is it that you want to do, whether you are looking for data or you're looking for an existing dashboard. So, the catalog becomes embedded into the BI tool. >> Hey, Dave Menninger, sometimes you have some data in your back pocket. Do you have any stats (chuckles) on this topic? >> No, I'm glad you asked, because I'm going to... Now, data catalogs are something that's interesting. Sanjeev made a statement that data catalogs are falling out of favor. I don't care what you call them. They're valuable to organizations. Our research shows that organizations that have adequate data catalog technologies are three times more likely to express satisfaction with their analytics for just the reasons that Sanjeev was talking about. You can find what you want, you know you're getting the right information, you know whether or not it's trusted. So, those are good things. So, we expect to see the capabilities, whether it's embedded or separate. We expect to see those capabilities continue to permeate the market. >> And a lot of those catalogs are driven now by machine learning and things. So, they're learning from those patterns of usage by people when people use the data. (airy laughs) >> All right. Okay. Thank you, guys. All right. Let's move on to the next one. Tony Bear, let's bring up the predictions. You got something in here about the modern data stack. We need to rethink it. Is the modern data stack getting long at the tooth? Is it not so modern anymore? >> I think, in a way, it's got almost too modern. It's gotten too, I don't know if it's being long in the tooth, but it is getting long. The modern data stack, it's traditionally been defined as basically you have the data platform, which would be the operational database and the data warehouse. And in between, you have all the tools that are necessary to essentially get that data from the operational realm or the streaming realm for that matter into basically the data warehouse, or as we might be seeing more and more, the data lakehouse. And I think, what's important here is that, or I think, we have seen a lot of progress, and this would be in the cloud, is with the SaaS services. And especially you see that in the modern data stack, which is like all these players, not just the MongoDBs or the Oracles or the Amazons have their database platforms. You see they have the Informatica's, and all the other players there in Fivetrans have their own SaaS services. And within those SaaS services, you get a certain degree of simplicity, which is it takes all the housekeeping off the shoulders of the customers. That's a good thing. The problem is that what we're getting to unfortunately is what I would call lots of islands of simplicity, which means that it leads it (Dave laughing) to the customer to have to integrate or put all that stuff together. It's a complex tool chain. And so, what we really need to think about here, we have too many pieces. And going back to the discussion of catalogs, it's like we have so many catalogs out there, which one do we use? 'Cause chances are of most organizations do not rely on a single catalog at this point. What I'm calling on all the data providers or all the SaaS service providers, is to literally get it together and essentially make this modern data stack less of a stack, make it more of a blending of an end-to-end solution. And that can come in a number of different ways. Part of it is that we're data platform providers have been adding services that are adjacent. And there's some very good examples of this. We've seen progress over the past year or so. For instance, MongoDB integrating search. It's a very common, I guess, sort of tool that basically, that the applications that are developed on MongoDB use, so MongoDB then built it into the database rather than requiring an extra elastic search or open search stack. Amazon just... AWS just did the zero-ETL, which is a first step towards simplifying the process from going from Aurora to Redshift. You've seen same thing with Google, BigQuery integrating basically streaming pipelines. And you're seeing also a lot of movement in database machine learning. So, there's some good moves in this direction. I expect to see more than this year. Part of it's from basically the SaaS platform is adding some functionality. But I also see more importantly, because you're never going to get... This is like asking your data team and your developers, herding cats to standardizing the same tool. In most organizations, that is not going to happen. So, take a look at the most popular combinations of tools and start to come up with some pre-built integrations and pre-built orchestrations, and offer some promotional pricing, maybe not quite two for, but in other words, get two products for the price of two services or for the price of one and a half. I see a lot of potential for this. And it's to me, if the class was to simplify things, this is the next logical step and I expect to see more of this here. >> Yeah, and you see in Oracle, MySQL heat wave, yet another example of eliminating that ETL. Carl Olofson, today, if you think about the data stack and the application stack, they're largely separate. Do you have any thoughts on how that's going to play out? Does that play into this prediction? What do you think? >> Well, I think, that the... I really like Tony's phrase, islands of simplification. It really says (Tony chuckles) what's going on here, which is that all these different vendors you ask about, about how these stacks work. All these different vendors have their own stack vision. And you can... One application group is going to use one, and another application group is going to use another. And some people will say, let's go to, like you go to a Informatica conference and they say, we should be the center of your universe, but you can't connect everything in your universe to Informatica, so you need to use other things. So, the challenge is how do we make those things work together? As Tony has said, and I totally agree, we're never going to get to the point where people standardize on one organizing system. So, the alternative is to have metadata that can be shared amongst those systems and protocols that allow those systems to coordinate their operations. This is standard stuff. It's not easy. But the motive for the vendors is that they can become more active critical players in the enterprise. And of course, the motive for the customer is that things will run better and more completely. So, I've been looking at this in terms of two kinds of metadata. One is the meaning metadata, which says what data can be put together. The other is the operational metadata, which says basically where did it come from? Who created it? What's its current state? What's the security level? Et cetera, et cetera, et cetera. The good news is the operational stuff can actually be done automatically, whereas the meaning stuff requires some human intervention. And as we've already heard from, was it Doug, I think, people are disinclined to put a lot of definition into meaning metadata. So, that may be the harder one, but coordination is key. This problem has been with us forever, but with the addition of new data sources, with streaming data with data in different formats, the whole thing has, it's been like what a customer of mine used to say, "I understand your product can make my system run faster, but right now I just feel I'm putting my problems on roller skates. (chuckles) I don't need that to accelerate what's already not working." >> Excellent. Okay, Carl, let's stay with you. I remember in the early days of the big data movement, Hadoop movement, NoSQL was the big thing. And I remember Amr Awadallah said to us in theCUBE that SQL is the killer app for big data. So, your prediction here, if we bring that up is SQL is back. Please elaborate. >> Yeah. So, of course, some people would say, well, it never left. Actually, that's probably closer to true, but in the perception of the marketplace, there's been all this noise about alternative ways of storing, retrieving data, whether it's in key value stores or document databases and so forth. We're getting a lot of messaging that for a while had persuaded people that, oh, we're not going to do analytics in SQL anymore. We're going to use Spark for everything, except that only a handful of people know how to use Spark. Oh, well, that's a problem. Well, how about, and for ordinary conventional business analytics, Spark is like an over-engineered solution to the problem. SQL works just great. What's happened in the past couple years, and what's going to continue to happen is that SQL is insinuating itself into everything we're seeing. We're seeing all the major data lake providers offering SQL support, whether it's Databricks or... And of course, Snowflake is loving this, because that is what they do, and their success is certainly points to the success of SQL, even MongoDB. And we were all, I think, at the MongoDB conference where on one day, we hear SQL is dead. They're not teaching SQL in schools anymore, and this kind of thing. And then, a couple days later at the same conference, they announced we're adding a new analytic capability-based on SQL. But didn't you just say SQL is dead? So, the reality is that SQL is better understood than most other methods of certainly of retrieving and finding data in a data collection, no matter whether it happens to be relational or non-relational. And even in systems that are very non-relational, such as graph and document databases, their query languages are being built or extended to resemble SQL, because SQL is something people understand. >> Now, you remember when we were in high school and you had had to take the... Your debating in the class and you were forced to take one side and defend it. So, I was was at a Vertica conference one time up on stage with Curt Monash, and I had to take the NoSQL, the world is changing paradigm shift. And so just to be controversial, I said to him, Curt Monash, I said, who really needs acid compliance anyway? Tony Baer. And so, (chuckles) of course, his head exploded, but what are your thoughts (guests laughing) on all this? >> Well, my first thought is congratulations, Dave, for surviving being up on stage with Curt Monash. >> Amen. (group laughing) >> I definitely would concur with Carl. We actually are definitely seeing a SQL renaissance and if there's any proof of the pudding here, I see lakehouse is being icing on the cake. As Doug had predicted last year, now, (clears throat) for the record, I think, Doug was about a year ahead of time in his predictions that this year is really the year that I see (clears throat) the lakehouse ecosystems really firming up. You saw the first shots last year. But anyway, on this, data lakes will not go away. I've actually, I'm on the home stretch of doing a market, a landscape on the lakehouse. And lakehouse will not replace data lakes in terms of that. There is the need for those, data scientists who do know Python, who knows Spark, to go in there and basically do their thing without all the restrictions or the constraints of a pre-built, pre-designed table structure. I get that. Same thing for developing models. But on the other hand, there is huge need. Basically, (clears throat) maybe MongoDB was saying that we're not teaching SQL anymore. Well, maybe we have an oversupply of SQL developers. Well, I'm being facetious there, but there is a huge skills based in SQL. Analytics have been built on SQL. They came with lakehouse and why this really helps to fuel a SQL revival is that the core need in the data lake, what brought on the lakehouse was not so much SQL, it was a need for acid. And what was the best way to do it? It was through a relational table structure. So, the whole idea of acid in the lakehouse was not to turn it into a transaction database, but to make the data trusted, secure, and more granularly governed, where you could govern down to column and row level, which you really could not do in a data lake or a file system. So, while lakehouse can be queried in a manner, you can go in there with Python or whatever, it's built on a relational table structure. And so, for that end, for those types of data lakes, it becomes the end state. You cannot bypass that table structure as I learned the hard way during my research. So, the bottom line I'd say here is that lakehouse is proof that we're starting to see the revenge of the SQL nerds. (Dave chuckles) >> Excellent. Okay, let's bring up back up the predictions. Dave Menninger, this one's really thought-provoking and interesting. We're hearing things like data as code, new data applications, machines actually generating plans with no human involvement. And your prediction is the definition of data is expanding. What do you mean by that? >> So, I think, for too long, we've thought about data as the, I would say facts that we collect the readings off of devices and things like that, but data on its own is really insufficient. Organizations need to manipulate that data and examine derivatives of the data to really understand what's happening in their organization, why has it happened, and to project what might happen in the future. And my comment is that these data derivatives need to be supported and managed just like the data needs to be managed. We can't treat this as entirely separate. Think about all the governance discussions we've had. Think about the metadata discussions we've had. If you separate these things, now you've got more moving parts. We're talking about simplicity and simplifying the stack. So, if these things are treated separately, it creates much more complexity. I also think it creates a little bit of a myopic view on the part of the IT organizations that are acquiring these technologies. They need to think more broadly. So, for instance, metrics. Metric stores are becoming much more common part of the tooling that's part of a data platform. Similarly, feature stores are gaining traction. So, those are designed to promote the reuse and consistency across the AI and ML initiatives. The elements that are used in developing an AI or ML model. And let me go back to metrics and just clarify what I mean by that. So, any type of formula involving the data points. I'm distinguishing metrics from features that are used in AI and ML models. And the data platforms themselves are increasingly managing the models as an element of data. So, just like figuring out how to calculate a metric. Well, if you're going to have the features associated with an AI and ML model, you probably need to be managing the model that's associated with those features. The other element where I see expansion is around external data. Organizations for decades have been focused on the data that they generate within their own organization. We see more and more of these platforms acquiring and publishing data to external third-party sources, whether they're within some sort of a partner ecosystem or whether it's a commercial distribution of that information. And our research shows that when organizations use external data, they derive even more benefits from the various analyses that they're conducting. And the last great frontier in my opinion on this expanding world of data is the world of driver-based planning. Very few of the major data platform providers provide these capabilities today. These are the types of things you would do in a spreadsheet. And we all know the issues associated with spreadsheets. They're hard to govern, they're error-prone. And so, if we can take that type of analysis, collecting the occupancy of a rental property, the projected rise in rental rates, the fluctuations perhaps in occupancy, the interest rates associated with financing that property, we can project forward. And that's a very common thing to do. What the income might look like from that property income, the expenses, we can plan and purchase things appropriately. So, I think, we need this broader purview and I'm beginning to see some of those things happen. And the evidence today I would say, is more focused around the metric stores and the feature stores starting to see vendors offer those capabilities. And we're starting to see the ML ops elements of managing the AI and ML models find their way closer to the data platforms as well. >> Very interesting. When I hear metrics, I think of KPIs, I think of data apps, orchestrate people and places and things to optimize around a set of KPIs. It sounds like a metadata challenge more... Somebody once predicted they'll have more metadata than data. Carl, what are your thoughts on this prediction? >> Yeah, I think that what Dave is describing as data derivatives is in a way, another word for what I was calling operational metadata, which not about the data itself, but how it's used, where it came from, what the rules are governing it, and that kind of thing. If you have a rich enough set of those things, then not only can you do a model of how well your vacation property rental may do in terms of income, but also how well your application that's measuring that is doing for you. In other words, how many times have I used it, how much data have I used and what is the relationship between the data that I've used and the benefits that I've derived from using it? Well, we don't have ways of doing that. What's interesting to me is that folks in the content world are way ahead of us here, because they have always tracked their content using these kinds of attributes. Where did it come from? When was it created, when was it modified? Who modified it? And so on and so forth. We need to do more of that with the structure data that we have, so that we can track what it's used. And also, it tells us how well we're doing with it. Is it really benefiting us? Are we being efficient? Are there improvements in processes that we need to consider? Because maybe data gets created and then it isn't used or it gets used, but it gets altered in some way that actually misleads people. (laughs) So, we need the mechanisms to be able to do that. So, I would say that that's... And I'd say that it's true that we need that stuff. I think, that starting to expand is probably the right way to put it. It's going to be expanding for some time. I think, we're still a distance from having all that stuff really working together. >> Maybe we should say it's gestating. (Dave and Carl laughing) >> Sorry, if I may- >> Sanjeev, yeah, I was going to say this... Sanjeev, please comment. This sounds to me like it supports Zhamak Dehghani's principles, but please. >> Absolutely. So, whether we call it data mesh or not, I'm not getting into that conversation, (Dave chuckles) but data (audio breaking) (Tony laughing) everything that I'm hearing what Dave is saying, Carl, this is the year when data products will start to take off. I'm not saying they'll become mainstream. They may take a couple of years to become so, but this is data products, all this thing about vacation rentals and how is it doing, that data is coming from different sources. I'm packaging it into our data product. And to Carl's point, there's a whole operational metadata associated with it. The idea is for organizations to see things like developer productivity, how many releases am I doing of this? What data products are most popular? I'm actually in right now in the process of formulating this concept that just like we had data catalogs, we are very soon going to be requiring data products catalog. So, I can discover these data products. I'm not just creating data products left, right, and center. I need to know, do they already exist? What is the usage? If no one is using a data product, maybe I want to retire and save cost. But this is a data product. Now, there's a associated thing that is also getting debated quite a bit called data contracts. And a data contract to me is literally just formalization of all these aspects of a product. How do you use it? What is the SLA on it, what is the quality that I am prescribing? So, data product, in my opinion, shifts the conversation to the consumers or to the business people. Up to this point when, Dave, you're talking about data and all of data discovery curation is a very data producer-centric. So, I think, we'll see a shift more into the consumer space. >> Yeah. Dave, can I just jump in there just very quickly there, which is that what Sanjeev has been saying there, this is really central to what Zhamak has been talking about. It's basically about making, one, data products are about the lifecycle management of data. Metadata is just elemental to that. And essentially, one of the things that she calls for is making data products discoverable. That's exactly what Sanjeev was talking about. >> By the way, did everyone just no notice how Sanjeev just snuck in another prediction there? So, we've got- >> Yeah. (group laughing) >> But you- >> Can we also say that he snuck in, I think, the term that we'll remember today, which is metadata museums. >> Yeah, but- >> Yeah. >> And also comment to, Tony, to your last year's prediction, you're really talking about it's not something that you're going to buy from a vendor. >> No. >> It's very specific >> Mm-hmm. >> to an organization, their own data product. So, touche on that one. Okay, last prediction. Let's bring them up. Doug Henschen, BI analytics is headed to embedding. What does that mean? >> Well, we all know that conventional BI dashboarding reporting is really commoditized from a vendor perspective. It never enjoyed truly mainstream adoption. Always that 25% of employees are really using these things. I'm seeing rising interest in embedding concise analytics at the point of decision or better still, using analytics as triggers for automation and workflows, and not even necessitating human interaction with visualizations, for example, if we have confidence in the analytics. So, leading companies are pushing for next generation applications, part of this low-code, no-code movement we've seen. And they want to build that decision support right into the app. So, the analytic is right there. Leading enterprise apps vendors, Salesforce, SAP, Microsoft, Oracle, they're all building smart apps with the analytics predictions, even recommendations built into these applications. And I think, the progressive BI analytics vendors are supporting this idea of driving insight to action, not necessarily necessitating humans interacting with it if there's confidence. So, we want prediction, we want embedding, we want automation. This low-code, no-code development movement is very important to bringing the analytics to where people are doing their work. We got to move beyond the, what I call swivel chair integration, between where people do their work and going off to separate reports and dashboards, and having to interpret and analyze before you can go back and do take action. >> And Dave Menninger, today, if you want, analytics or you want to absorb what's happening in the business, you typically got to go ask an expert, and then wait. So, what are your thoughts on Doug's prediction? >> I'm in total agreement with Doug. I'm going to say that collectively... So, how did we get here? I'm going to say collectively as an industry, we made a mistake. We made BI and analytics separate from the operational systems. Now, okay, it wasn't really a mistake. We were limited by the technology available at the time. Decades ago, we had to separate these two systems, so that the analytics didn't impact the operations. You don't want the operations preventing you from being able to do a transaction. But we've gone beyond that now. We can bring these two systems and worlds together and organizations recognize that need to change. As Doug said, the majority of the workforce and the majority of organizations doesn't have access to analytics. That's wrong. (chuckles) We've got to change that. And one of the ways that's going to change is with embedded analytics. 2/3 of organizations recognize that embedded analytics are important and it even ranks higher in importance than AI and ML in those organizations. So, it's interesting. This is a really important topic to the organizations that are consuming these technologies. The good news is it works. Organizations that have embraced embedded analytics are more comfortable with self-service than those that have not, as opposed to turning somebody loose, in the wild with the data. They're given a guided path to the data. And the research shows that 65% of organizations that have adopted embedded analytics are comfortable with self-service compared with just 40% of organizations that are turning people loose in an ad hoc way with the data. So, totally behind Doug's predictions. >> Can I just break in with something here, a comment on what Dave said about what Doug said, which (laughs) is that I totally agree with what you said about embedded analytics. And at IDC, we made a prediction in our future intelligence, future of intelligence service three years ago that this was going to happen. And the thing that we're waiting for is for developers to build... You have to write the applications to work that way. It just doesn't happen automagically. Developers have to write applications that reference analytic data and apply it while they're running. And that could involve simple things like complex queries against the live data, which is through something that I've been calling analytic transaction processing. Or it could be through something more sophisticated that involves AI operations as Doug has been suggesting, where the result is enacted pretty much automatically unless the scores are too low and you need to have a human being look at it. So, I think that that is definitely something we've been watching for. I'm not sure how soon it will come, because it seems to take a long time for people to change their thinking. But I think, as Dave was saying, once they do and they apply these principles in their application development, the rewards are great. >> Yeah, this is very much, I would say, very consistent with what we were talking about, I was talking about before, about basically rethinking the modern data stack and going into more of an end-to-end solution solution. I think, that what we're talking about clearly here is operational analytics. There'll still be a need for your data scientists to go offline just in their data lakes to do all that very exploratory and that deep modeling. But clearly, it just makes sense to bring operational analytics into where people work into their workspace and further flatten that modern data stack. >> But with all this metadata and all this intelligence, we're talking about injecting AI into applications, it does seem like we're entering a new era of not only data, but new era of apps. Today, most applications are about filling forms out or codifying processes and require a human input. And it seems like there's enough data now and enough intelligence in the system that the system can actually pull data from, whether it's the transaction system, e-commerce, the supply chain, ERP, and actually do something with that data without human involvement, present it to humans. Do you guys see this as a new frontier? >> I think, that's certainly- >> Very much so, but it's going to take a while, as Carl said. You have to design it, you have to get the prediction into the system, you have to get the analytics at the point of decision has to be relevant to that decision point. >> And I also recall basically a lot of the ERP vendors back like 10 years ago, we're promising that. And the fact that we're still looking at the promises shows just how difficult, how much of a challenge it is to get to what Doug's saying. >> One element that could be applied in this case is (indistinct) architecture. If applications are developed that are event-driven rather than following the script or sequence that some programmer or designer had preconceived, then you'll have much more flexible applications. You can inject decisions at various points using this technology much more easily. It's a completely different way of writing applications. And it actually involves a lot more data, which is why we should all like it. (laughs) But in the end (Tony laughing) it's more stable, it's easier to manage, easier to maintain, and it's actually more efficient, which is the result of an MIT study from about 10 years ago, and still, we are not seeing this come to fruition in most business applications. >> And do you think it's going to require a new type of data platform database? Today, data's all far-flung. We see that's all over the clouds and at the edge. Today, you cache- >> We need a super cloud. >> You cache that data, you're throwing into memory. I mentioned, MySQL heat wave. There are other examples where it's a brute force approach, but maybe we need new ways of laying data out on disk and new database architectures, and just when we thought we had it all figured out. >> Well, without referring to disk, which to my mind, is almost like talking about cave painting. I think, that (Dave laughing) all the things that have been mentioned by all of us today are elements of what I'm talking about. In other words, the whole improvement of the data mesh, the improvement of metadata across the board and improvement of the ability to track data and judge its freshness the way we judge the freshness of a melon or something like that, to determine whether we can still use it. Is it still good? That kind of thing. Bringing together data from multiple sources dynamically and real-time requires all the things we've been talking about. All the predictions that we've talked about today add up to elements that can make this happen. >> Well, guys, it's always tremendous to get these wonderful minds together and get your insights, and I love how it shapes the outcome here of the predictions, and let's see how we did. We're going to leave it there. I want to thank Sanjeev, Tony, Carl, David, and Doug. Really appreciate the collaboration and thought that you guys put into these sessions. Really, thank you. >> Thank you. >> Thanks, Dave. >> Thank you for having us. >> Thanks. >> Thank you. >> All right, this is Dave Valente for theCUBE, signing off for now. Follow these guys on social media. Look for coverage on siliconangle.com, theCUBE.net. Thank you for watching. (upbeat music)

(vibrant electronic music) >> Welcome back to SuperCloud2. You know, this event, and the Super Cloud initiative in general, it's an open industry-wide collaboration. Last August at SuperCloud22, we really honed in on the definition, which of course we've published. And there's this shared doc, which folks are still adding to and refining, in fact, just recently, Dr. Nelu Mihai added some critical points that really advanced some of the community's initial principles, and today at SuperCloud2, we're digging further into the topic with input from real world practitioners, and we're exploring that intersection of data, data mesh, and cloud, and importantly, the realities and challenges of deploying technology to drive new business capability, and I'm pleased to welcome Ash Naseer to the program. He's a Senior Director of Data Engineering at Warner Bros. Discovery. Ash, great to see you again, thanks so much for taking time with us. >> It's great to be back, these conversations are always very fun. >> I was so excited when we met last spring, I guess, so before we get started I wanted to play a clip from that conversation, it was June, it was at the Snowflake Summit in Las Vegas. And it's a comment that you made about your company but also data mesh. Guys, roll the clip. >> Yeah, so, when people think of Warner Bros., you always think of the movie studio. But we're more than that, right, I mean, you think of HBO, you think of TNT, you think of CNN. We have 30 plus brands in our portfolio, and each have their own needs. So the idea of a data mesh really helps us because what we can do is we can federate access across the company, so that CNN can work at their own pace, you know, when there's election season, they can ingest their own data. And they don't have to bump up against, as an example, HBO, if Game of Thrones is goin' on. >> So-- Okay, so that's pretty interesting, so you've got these sort of different groups that have different data requirements inside of your organization. Now data mesh, it's a relatively new concept, so you're kind of ahead of the curve. So Ash, my question is, when you think about getting value from data, and how that's changed over the past decade, you've had pre-Hadoop, Hadoop, what do you see that's changed, now you got the cloud coming in, what's changed? What had to be sort of fixed? What's working now, and where do you see it going? >> Yeah, so I feel like in the last decade, we've gone through quite a maturity curve. I actually like to say that we're in the golden age of data, because the tools and technology in the data space, particularly and then broadly in the cloud, they allow us to do things that we couldn't do way back when, like you suggested, back in the Hadoop era or even before that. So there's certainly a lot of maturity, and a lot of technology that has come about. So in terms of the good, bad, and ugly, so let me kind of start with the good, right? In terms of bringing value from the data, I really feel like we're in this place where the folks that are charged with unlocking that value from the data, they're actually spending the majority of their time actually doing that. And what do I mean by that? If you think about it, 10 years ago, the data scientist was the person that was going to sort of solve all of the data problems in a company. But what happened was, companies asked these data scientists to come in and do a multitude of things. And what these data scientists found out was, they were spending most of their time on, really, data wrangling, and less on actually getting the value out of the data. And in the last decade or so, I feel like we've made the shift, and we realize that data engineering, data management, data governance, those are as important practices as data science, which is sort of getting the value out of the data. And so what that has done is, it has freed up the data scientist and the business analyst and the data analyst, and the BI expert, to really focus on how to get value out of the data, and spend less time wrangling data. So I really think that that's the good. In terms of the bad, I feel like, there's a lot of legacy data platforms out there, and I feel like there's going to be a time where we'll be in that hybrid mode. And then the ugly, I feel like, with all the data and all the technology, creates another problem of itself. Because most companies don't have arms around their data, and making sure that they know who's using the data, what they're using for, and how can the company leverage the collective intelligence. That is a bigger problem to solve today than 10 years ago. And that's where technologies like the data mesh come in. >> Yeah, so when I think of data mesh, and I say, you're an early practitioner of data mesh, you mentioned legacy technology, so the concept of data mesh is inclusive. In theory anyway, you're supposed to be including the legacy technologies. Whether it's a data lake or data warehouse or Oracle or Snowflake or whatever it is. And when you think about Jamak Dagani's principles, it's domain-centric ownership, data as product. And that creates challenges around self-serve infrastructure and automated governance, and then when you start to combine these different technologies. You got legacy, you got cloud. Everything's different. And so you have to figure out how to deal with that, so my question is, how have you dealt with that, and what role has the cloud played in solving those problems, in particular, that self-serve infrastructure, and that automated governance, and where are we in terms of solving that problem from a practitioner's standpoint? >> Yeah, I always like to say that data is a team sport, and we should sort of think of it as such, and that's, I feel like, the key of the data mesh concept, is treating it as a team sport. A lot of people ask me, they're like, "Oh hey, Ash, I've heard about this thing called data mesh. "Where can I buy one?" or, "what's the technology that I use to get a data mesh? And the reality is that there isn't one technology, you can't really buy a data mesh. It's really a way of life, it's how organizations decide to approach data, like I said, back to a team sport analogy, making sure that everyone has the seat on the table, making sure that we embrace the fact that we have a lot of data, we have a lot of data problems to solve. And the way we'll be successful is to make everyone inclusive. You know, you think about the old days, Data silos or shadow IT, some might call it. That's been around for decades. And what hasn't changed was this notion that, hey, everything needs to be sort of managed centrally. But with the cloud and with the technologies that we have today, we have the right technology and the tooling to democratize that data, and democratize not only just the access, but also sort of building building blocks and sort of taking building blocks which are relevant to your product or your business. And adding to the overall data mesh. We've got all that technology. The challenge is for us to really embrace it, and make sure that we implement it from an organizational standpoint. >> So, thinking about super cloud, there's a layer that lives above the clouds and adds value. And you think about your brands you got 30 brands, you mentioned shadow IT. If, let's say, one of those brands, HBO or TNT, whatever. They want to go, "Hey, we really like Google's analytics tools," and they maybe go off and build something, I don't know if that's even allowed, maybe it's not. But then you build this data mesh. My question is around multi-cloud, cross cloud, super cloud if you will. Is that a advantage for you as a practitioner, or does that just make things more complicated? >> I really love the idea of a multi-cloud. I think it's great, I think that it should have been the norm, not the exception, I feel like people talk about it as if it's the exception. That should have been the case. I will say, though, I feel like multi-cloud should evolve organically, so back to your point about some of these different brands, and, you know, different brands or different business units. Or even in a merger and acquisitions situation, where two different companies or multiple different companies come together with different technology stacks. You know, I feel like that's an organic evolution, and making sure that we use the concepts and the technologies around the multi-cloud to bring everyone together. That's where we need to be, and again, it talks to the fact that each of those business units and each of those groups have their own unique needs, and we need to make sure that we embrace that and we enable that, rather than stifling everything. Now where I have a little bit of a challenge with the multi-cloud is when technology leaders try to build it by design. So there's a notion there that, "Hey, you need to sort of diversify "and don't put all your eggs in one basket." And so we need to have this multi-cloud thing. I feel like that is just sort of creating more complexity where it doesn't need to be, we can all sort of simplify our lives, but where it evolves organically, absolutely, I think that's the right way to go. >> But, so Ash, if it evolves organically don't you need some kind of cloud interpreter, to create a common experience across clouds, does that exist today? What are your thoughts on that? >> There is a lot of technology that exists today, and that helps go between these different clouds, a lot of these sort of cloud agnostic technologies that you talked about, the Snowflakes and the Databricks and so forth of the world, they operate in multiple clouds, they operate in multiple regions, within a given cloud and multiple clouds. So they span all of that, and they have the tools and technology, so, I feel like the tooling is there. There does need to be more of an evolution around the tooling and I think the market's need are going to dictate that, I feel like the market is there, they're asking for it, so, there's definitely going to be that evolution, but the technology is there, I think just making sure that we embrace that and we sort of embrace that as a challenge and not try to sort of shut all of that down and box everything into one. >> What's the biggest challenge, is it governance or security? Or is it more like you're saying, adoption, cultural? >> I think it's a combination of cultural as well as governance. And so, the cultural side I've talked about, right, just making sure that we give these different teams a seat at the table, and they actually bring that technology into the mix. And we use the modern tools and technologies to make sure that everybody sort of plays nice together. That is definitely, we have ways to go there. But then, in terms of governance, that is another big problem that most companies are just starting to wrestle with. Because like I said, I mean, the data silos and shadow IT, that's been around there, right? The only difference is that we're now sort of bringing everything together in a cloud environment, the collective organization has access to that. And now we just realized, oh we have quite a data problem at our hands, so how do we sort of organize this data, make sure that the quality is there, the trust is there. When people look at that data, a lot of those questions are now coming to the forefront because everything is sort of so transparent with the cloud, right? And so I feel like, again, putting in the right processes, and the right tooling to address that is going to be critical in the next years to come. >> Is sharing data across clouds, something that is valuable to you, or even within a single cloud, being able to share data. And my question is, not just within your organization, but even outside your organization, is that something that has sort of hit your radar or is it mature or is that something that really would add value to your business? >> Data sharing is huge, and again, this is another one of those things which isn't new. You know, I remember back in the '90s, when we had to share data externally, with our partners or our vendors, they used to physically send us stacks of these tapes, or physical media on some truck. And we've evolved since then, right, I mean, it went from that to sharing files online and so forth. But data sharing as a concept and as a concept which is now very frictionless, through these different technologies that we have today, that is very new. And that is something, like I said, it's always been going on. But that needs to be really embraced more as well. We as a company heavily leverage data sharing between our own different brands and business units, that helps us make that data mesh, so that when CNN, as an example, builds their own data model based on election data and the kinds of data that they need, compare that with other data in the rest of the company, sports, entertainment, and so forth and so on. Everyone has their unique data, but that data sharing capability brings it together wherever there is a need. So you think about having a Tiger Woods documentary, as an example, on HBO Max and making sure that you reach the audiences that are interested in golf and interested in sports and so forth, right? That all comes through the magic of data sharing, so, it's really critical, internally, for us. And then externally as well, because just understanding how our products are doing on our partners' networks and different distribution channels, that's important, and then just understanding how our consumers are consuming it off properties, right, I mean, we have brands that transcend just the screen, right? We have a lot of physical merchandise that you can buy in the store. So again, understanding who's buying the Batman action figures after the Batman movie was released, that's another critical insight. So it all gets enabled through data sharing, and something we rely heavily on. >> So I wanted to get your perspective on this. So I feel like the nirvana of data mesh is if I want to use Google BigQuery, an Oracle database, or a Microsoft database, or Snowflake, Databricks, Amazon, whatever. That that's a node on the mesh. And in the perfect world, you can share that data, it can be governed, I don't think we're quite there today, so. But within a platform, maybe it's within Google or within Amazon or within Snowflake or Databricks. If you're in that world, maybe even Oracle. You actually can do some levels of data sharing, maybe greater with some than others. Do you mandate as an organization that you have to use this particular data platform, or are you saying "Hey, we are architecting a data mesh for the future "where we believe the technology will support that," or maybe you've invented some technology that supports that today, can you help us understand that? >> Yeah, I always feel like mandate is a strong area, and it breeds the shadow IT and the data silos. So we don't mandate, we do make sure that there's a consistent set of governance rules, policies, and tooling that's there, so that everyone is on the same page. However, at the same time our focus is really operating in a federated way, that's been our solution, right? Is to make sure that we work within a common set of tooling, which may be different technologies, which in some cases may be different clouds. Although we're not that multi-cloud. So what we're trying to do is making sure that everyone who has that technology already built, as long as it sort of follows certain standards, it's modern, it has the capabilities that will eventually allow us to be successful and eventually allow for that data sharing, amongst those different nodes, as you put it. As long as that's the case, and as long as there's a governance layer, a master governance layer, where we know where all that data is and who has access to what and we can sort of be really confident about the quality of the data, as long as that case, our approach to that is really that federated approach. >> Sorry, did I hear you correctly, you're not multi-cloud today? >> Yeah, that's correct. There are certain spots where we use that, but by and large, we rely on a particular cloud, and that's just been, like I said, it's been the evolution, it was our evolution. We decided early on to focus on a single cloud, and that's the direction we've been going in. >> So, do you want to go to a multi-cloud, or, you mentioned organic before, if a business unit wants to go there, as long as they're adhering to those standards that you put out, maybe recommendations, that that's okay? I guess my question is, does that bring benefit to your business that you'd like to tap, or do you feel like it's not necessary? >> I'll go back to the point of, if it happens organically, we're going to be open about it. Obviously we'll have to look at every situations, not all clouds are created equal as well, so there's a number of different considerations. But by and large, when it happens organically, the key is time to value, right? How do you quickly bring those technologies in, as long as you could share the data, they're interconnected, they're secured, they're governed, we are confident on the quality, as long as those principles are met, we could definitely go in that direction. But by and large, we're sort of evolving in a singular direction, but even within a singular cloud, we're a global company. And we have audiences around the world, so making sure that even within a single cloud, those different regions interoperate as one, that's a bigger challenge that we're having to solve as well. >> Last question is kind of to the future of data and cloud and how it's going to evolve, do you see a day when companies like yours are increasingly going to be offering data, their software, services, and becoming more of a technology company, sort of pointing your tooling and your proprietary knowledge at the external world, as an opportunity, as a business opportunity? >> That's a very interesting concept, and I know companies have done that, and some of them have been extremely successful, I mean, Amazon is the biggest example that comes to mind, right-- >> Yeah. >> When they launched AWS, something that they had that expertise they had internally, and they offered it to the world as a product. But by and large, I think it's going to be far and few between, especially, it's going to be focused on companies that have technology as their DNA, or almost like in the technology sector, building technology. Most other companies have different markets that they are addressing. And in my opinion, a lot of these companies, what they're trying to do is really focus on the problems that we can solve for ourselves, I think there are more problems than we have people and expertise. So my guess is that most large companies, they're going to focus on solving their own problems. A few, like I said, more tech-focused companies, that would want to be in that business, would probably branch out, but by and large, I think companies will continue to focus on serving their customers and serving their own business. >> Alright, Ash, we're going to leave it there, Ash Naseer. Thank you so much for your perspectives, it was great to see you, I'm sure we'll see you face-to-face later on this year. >> This is great, thank you for having me. >> Ah, you're welcome, alright. Keep it right there for more great content from SuperCloud2. We'll be right back. (gentle percussive music)

>>The Cube presents Ignite 22, brought to you by Palo Alto Networks. >>Welcome back everyone. We're so glad that you're still with us. It's the Cube Live at the MGM Grand. This is our second day of coverage of Palo Alto Networks Ignite. This is takeaways from Ignite 22. Lisa Martin here with two really smart guys, Dave Valante. Dave, we're joined by one of our cube alumni, a friend, a friend of the, we say friend of the Cube. >>Yeah, otc. A friend of the Cube >>Karala joined us. Guys, it's great to have you here. It's been an exciting show. A lot of cybersecurity is one of my favorite topics to talk about. But I'd love to get some of the big takeaways from both of you. Dave, we'll start with you. >>A breathing room from two weeks ago. Yeah, that was, that was really pleasant. You know, I mean, I know was, yes, you sat in the analyst program, interested in what your takeaways were from there. But, you know, coming into this, we wrote a piece, Palo Alto's Gold Standard, what they need to do to, to keep that, that status. And we hear it a lot about consolidation. That's their big theme now, which is timely, right? Cause people wanna save money, they wanna do more with less. But I'm really interested in hearing zeus's thoughts on how that's playing in the market. How customers, how easy is it to just say, oh, hey, I'm gonna consolidate. I wanna get into that a little bit with you, how well the strategy's working. We're gonna get into some of the m and a activity and really bring your perspectives to the table. Well, >>It's, it's not easy. I mean, people have been calling for the consolidation of security for decades, and it's, it's, they're the first company that's actually made it happen. Right? And, and I think this is what we're seeing here is the culmination of this long term strategy, this company trying to build more of a platform. And they, you know, they, they came out as a firewall vendor. And I think it's safe to say they're more than firewall today. That's only about two thirds of their revenue now. So down from 80% a few years ago. And when I think of what Palo Alto has become, they're really a data company. Now, if you look at, you know, unit 42 in Cortex, the, the, the Cortex Data Lake, they've done an excellent job of taking telemetry from their products and from the acquisitions they have, right? And bringing that together into one big data lake. >>And then they're able to use that to, to do faster threat notification, forensics, things like that. And so I think the old model of security of create signatures for known threats, it's safe to say it never really worked and it wasn't ever gonna work. You had too many day zero exploits and things. The only way to fight security today is with a AI and ML based analytics. And they have, they're the gold standard. I think the one thing about your post that I would add the gold standard from a data standpoint, and that's given them this competitive advantage to go out and become a platform for a security. Which, like I said, the people have tried to do that for years. And the first one that's actually done it, well, >>We've heard this from some of the startups, like Lacework will say, oh, we treat security as a data problem. Of course there's a startup, Palo Alto's got, you know, whatever, 10, 15 years of, of, of history. But one of the things I wanted to explore with you coming into this was the notion of can you be best of breed and develop a suite? And we, we've been hearing a consistent answer to that question, which is, and, and do you need to, and the answer is, well, best of breed in security requires that full spectrum, that full view. So here's my question to you. So, okay, let's take Esty win relatively new for these guys, right? Yeah. Okay. And >>And one of the few products are not top two, top three in, right? Exactly. >>Yeah. So that's why I want to take that. Yeah. Because in bakeoffs, they're gonna lose on a head-to-head best of breed. And so the customer's gonna say, Hey, you know, I love your, your consolidation play, your esty win's. Just, okay, how about a little discount on that? And you know, these guys are premium priced. Yes. So, you know, are they in essentially through their pricing strategies, sort of creating that stuff, fighting that, is that friction for them where they've got, you know, the customer says, all right, well forget it, we're gonna go stove pipe with the SD WAN will consolidate some of the stuff. Are you seeing that? >>Yeah, I, I, I still think the sales model is that way. And I think that's something they need to work on changing. If they get into a situation where they have to get down into a feature battle of my SD WAN versus your SD wan, my firewall versus your firewall, frankly they've already lost, you know, because their value prop is the suite and, and is the platform. And I was talking to the CISO here that told me, he realizes now that you don't need best of breed everywhere to have best in class threat protection. In fact, best of breed everywhere leads to suboptimal threat protection. Cuz you have all these data data sets that are in silos, right? And so from a data scientist standpoint, right, there's the good data leads to good insights. Well, partial data leads to fragmented insights and that's, that's what the best, best of breed approach gives you. And so I was talking with Palo about this, can they have this vision of being best of breed and platform? I don't really think you can maintain best of breed everywhere across this portfolio this big, but you don't need to. >>That was my second point of my >>Question. That's the point. >>Yeah. And so, cuz cuz because you know, we've talked about this, that that sweets always win in the long run, >>Sweets >>Win. Yeah. But here's the thing, I, I wonder to your your point about, you know, the customer, you know, understanding that that that, that this resonates with them. I, my guess is a lot of customers, you know, at that mid-level and the fat middle are like still sort of wed, you know, hugging that, that tool. So there's, there's work to be done here, but I think they, they, they got it right Because if they devolve, to your point, if they devolve down to that speeds and feeds, eh, what's the point of that? Where's their valuable? >>You do not wanna get into a knife fight. And I, and I, and I think for them the, a big challenge now is convincing customers that the suite, the suite approach does work. And they have to be able to do that in actual customer examples. And so, you know, I I interviewed a bunch of customers here and the ones that have bought into XDR and xor and even are looking at their sim have told me that the, the, so think of soc operations, the old way heavily manually oriented, right? You have multiple panes of glass and you know, and then you've got, so there's a lot of people work before you bring the tools in, right? If done correctly with AI and ml, the machines would do all the heavy lifting and then you'd bring people in at the end to clean up the little bits that were missed, right? >>And so you, you moved to, from something that was very people heavy to something that's machine heavy and machines can work a lot faster than people. And the, and so the ones that I've talked that have, that have done that have said, look, our engineers have moved on to a lot different things. They're doing penetration testing, they're, you know, helping us with, with strategy and they're not fighting that, that daily fight of looking through log files. And the only proof point you need, Dave, is look at every big breach that we've had over the last five years. There's some SIM vendor up there that says, we caught it. Yeah. >>Yeah. We we had the data. >>Yeah. But, but, but the security team missed it. Well they missed it because you're, nobody can look at that much data manually. And so the, I I think their approach of relying heavily on machines to fight the fight is actually the right way. >>Is that a differentiator for them versus, we were talking before we went live that you and I first hit our very first segment back in 2017 at Fort Net. Is that, where do the two stand in your >>Yeah, it's funny cuz if you talk to the two vendors, they don't really see each other in a lot of accounts because Fort Net's more small market mid-market. It's the same strategy to some degree where Fort Net relies heavily on in-house development and Palo Alto relies heavily on acquisition. Yeah. And so I think from a consistently feature set, you know, Fort Net has an advantage there because it, it's all run off their, their their silicon. Where, where Palo's able to innovate very quickly. The, it it requires a lot of work right? To, to bring the front end and back ends together. But they're serving different markets. So >>Do you see that as a differentiator? The integration strategy that Palo Alto has as a differentiator? We talk to so many companies who have an a strong m and a strategy and, and execution arm. But the challenge is always integrating the technology so that the customer to, you know, ultimately it's the customer. >>I actually think they're, they're underrated as a, an acquirer. In fact, Dave wrote a post to a prior on Silicon Angle prior to Accelerate and he, he on, you put it on Twitter and you asked people to rank 'em as an acquirer and they were in the middle of the pack, >>Right? It was, it was. So it was Oracle, VMware, emc, ibm, Cisco, ServiceNow, and Palo Alto. Yeah. Or Oracle got very high marks. It was like 8.5 out of, you know, 10. Yeah. VMware I think was 6.5. Nice. Era was high emc, big range. IBM five to seven. Cisco was three to eight. Yeah. Yeah, right. ServiceNow was a seven. And then, yeah, Palo Alto was like a five. And I, which I think it was unfair. >>Well, and I think it depends on how you look at it. And I, so I think a lot of the acquisitions Palo Altos made, they've done a good job of integrating their backend data and they've almost ignored the front end. And so when you buy some of the products, it's a little clunky today. You know, if you work with Prisma Cloud, it could be a little bit cleaner. And even with, you know, the SD wan that took 'em a long time to bring CloudGenix in and stuff. But I think the approach is right. I don't, I don't necessarily believe you should integrate the front end until you've integrated the back end. >>That's >>The hard part, right? Because UL ultimately what you're gonna get, you're gonna get two panes of glass and one pane of glass and it might look pretty all mush together, but ultimately you're not solving the bigger problem, right. Of, of being able to create that big data like the, the fight security. And so I think, you know, the approach they've taken is the right one. I think from a user standpoint, maybe it doesn't show up as neatly because you don't see the frontend integration, but the way they're doing it is the right way to do it. And I'm glad they're doing it that way versus caving to the pressures of what, you know, the industry might want >>Showed up in the performance of the company. I mean, this company was basically gonna double revenues to 7 billion from 2020 to >>2023. Three. Think about that at that, that >>Make a, that's unbelievable, right? I mean, and then and they wanna double again. Yeah. You know, so, well >>What did, what did Nikesh was quoted as saying they wanna be the first cyber company that's a hundred billion dollars. He didn't give a timeline market cap. >>Right. >>Market cap, right. Do what I wanna get both of your opinions on what you saw and heard and felt this week. What do you think the likelihood is? And and do you have any projections on how, you know, how many years it's gonna take for them to get there? >>Well, >>Well I think so if they're gonna get that big, right? And, and we were talking about this pre-show, any company that's becoming a big company does it through ecosystem >>Bingo. >>Right? And that when you look around the show floor, it's not that impressive. And if that, if there's an area they need to focus on, it's building that ecosystem. And it's not with other security vendors, it's with application vendors and it's with the cloud companies and stuff. And they've got some relationships there, but they need to do more. I actually challenge 'em on that. One of the analyst sessions. They said, look, we've got 800 cortex partners. Well where are they? Right? Why isn't there a cortex stand here with a bunch of the small companies here? So I do think that that is an area they need to focus on. If they are gonna get to that, that market caps number, they will do so do so through ecosystem. Because every company that's achieved that has done it through ecosystem. >>A hundred percent agree. And you know, if you look at CrowdStrike's ecosystem, it's pretty similar. Yeah. You know, it doesn't really, you know, make much, much, not much different from this, but I went back and just looked at some, you know, peak valuations during the pandemic and shortly thereafter CrowdStrike was 70 billion. You know, that's what their roughly their peak Palo Alto was 56, fortune was 59 for the actually diverged. Right. And now Palo Alto has taken the, the top mantle, you know, today it's market cap's 52. So it's held 93% of its peak value. Everybody else is tanking. Even Okta was 45 billion. It's been crushed as you well know. But, so Palo Alto wasn't always, you know, the number one in terms of market cap. But I guess my point is, look, if CrowdStrike could got to 70 billion during Yeah. During the frenzy, I think it's gonna take, to answer your question, I think it's gonna be five years. Okay. Before they get back there. I think this market's gonna be tough for a while from a valuation standpoint. I think generally tech is gonna kind of go up and down and sideways for a good year and a half, maybe even two years could be even longer. And then I think there's gonna be some next wave of productivity innovation that that hits. And then you're gonna, you're almost always gonna exceed the previous highs. It's gonna take a while. Yeah, >>Yeah, yeah. But I think their ability to disrupt the SIM market actually is something I, I believe they're gonna do. I've been calling for the death of the sim for a long time and I know some people at Palo Alto are very cautious about saying that cuz the Splunks and the, you know, they're, they're their partners. But I, I think the, you know, it's what I said before, the, the tools are catching them, but they're, it's not in a way that's useful for the IT pro and, but I, I don't think the SIM vendors have that ecosystem of insight across network cloud endpoint. Right. Which is what you need in order to make a sim useful. >>CISO at an ETR roundtable said, if, if it weren't for my regulators, I would chuck my sim. >>Yes. >>But that's the only reason that, that this person was keeping it. So, >>Yeah. And I think the, the fact that most of those companies have moved to a perpetual MO or a a recurring revenue model actually helps unseat them. Typically when you pour a bunch of money into something, you remember the old computer associate days, nobody ever took it out cuz the sunk dollars you spent to do it. But now that you're paying an annual recurring fee, it's actually makes it easier to take out. So >>Yeah, it's it's an ebb and flow, right? Yeah. Because the maintenance costs were, you know, relatively low. Maybe it was 20% of the total. And then, you know, once every five years you had to do a refresh and you were still locked into the sort of maintenance and, and so yeah, I think you're right. The switching costs with sas, you know, in theory anyway, should be less >>Yeah. As long as you can migrate the data over. And I think they've got a pretty good handle on that. So, >>Yeah. So guys, I wanna get your perspective as a whole bunch of announcements here. We've only been here for a couple days, not a big conference as, as you can see from behind us. What Zs in your opinion was Palo Alto's main message and and what do you think about it main message at this event? And then same question for you. >>Yeah, I, I think their message largely wrapped around disruption, right? And, and they, in The's keynote already talked about that, right? And where they disrupted the firewall market by creating a NextGen firewall. In fact, if you look at all the new services they added to their firewall, you, you could almost say it's a NextGen NextGen firewall. But, but I do think the, the work they've done in the area of cloud and cortex actually I think is, is pretty impressive. And I think that's the, the SOC is ripe for disruption because it's for, for the most part, most socks still, you know, run off legacy playbooks. They run off legacy, you know, forensic models and things and they don't work. It's why we have so many breaches today. The, the dirty little secret that nobody ever wants to talk about is the bad guys are using machine learning, right? And so if you're using a signature based model, all they're do is tweak their model a little bit and it becomes, it bypasses them. So I, I think the only way to fight the the bad guys today is with you gotta fight fire with fire. And I think that's, that's the path they've, they've headed >>Down and the bad guys are hiding in plain sight, you know? >>Yeah, yeah. Well it's, it's not hard to do now with a lot of those legacy tools. So >>I think, I think for me, you know, the stat that we threw out earlier, I think yesterday at our keynote analysis was, you know, the ETR data shows that are, that are that last survey around 35% of the respondents said we are actively consolidating, sorry, 44%, sorry, 35 says we're actively consolidating vendors, redundant vendors today. That number's up to 44%. Yeah. It's by far the number one cost optimization technique. That's what these guys are pitching. And I think it's gonna resonate with people and, and I think to your point, they're integrating at the backend, their beeps are technical, right? I mean, they can deal with that complexity. Yeah. And so they don't need eye candy. Eventually they, they, they want to have that cuz it'll allow 'em to have deeper market penetration and make people more productive. But you know, that consolidation message came through loud and clear. >>Yeah. The big change in this industry too is all the new startups are all cloud native, right? They're all built on Amazon or Google or whatever. Yeah. And when your cloud native and you buy a cloud native integration is fast. It's not like having to integrate this big monolithic software stack anymore. Right. So I I think their pace of integration will only accelerate from here because everything's now cloud native. >>If a customer comes to you or when a customer comes to you and says, Zs help us with this cyber transformation we have, our board isn't necessarily with our executives in terms of execution of a security strategy. How do you advise them where Palo Alto is concerned? >>Yeah. You know, a lot, a lot of this is just fighting legacy mindset. And I've, I was talking with some CISOs here from state and local governments and things and they're, you know, they can't get more budget. They're fighting the tide. But what they did find is through the use of automation technology, they're able to bring their people costs way down. Right. And then be able to use that budget to invest in a lot of new projects. And so with that, you, you have to start with your biggest pain points, apply automation where you can, and then be able to use that budget to reinvest back in your security strategy. And it's good for the IT pros too, the security pros, my advice to, to it pros is if you're doing things today that aren't resume building, stop doing them. Right? Find a way to automate the money your job. And so if you're patching systems and you're looking through log files, there's no reason machines can't do that. And you go do something a lot more interesting. >>So true. It's like storage guys 10 years ago, provisioning loans. Yes. It's like, stop doing that. Yeah. You're gonna be outta a job. And so who, last question I have is, is who do you see as the big competitors, the horses on the track question, right? So obviously Cisco kind of service has led for a while and you know, big portfolio company, CrowdStrike coming at it from end point. You know who, who, who do you see as the real players going for that? You know, right now the market's three to 4%. The leader has three, three 4% of the market. You know who they're all going for? 10, 15, maybe 20% of the market. Who, who are the likely candidates? Yeah, >>I don't know if CrowdStrike really has the breadth of portfolio to compete long term though. I I think they've had a nice run, but I, we might start to see the follow 'em. I think Microsoft is gonna be for middle. They've laid down the gauntlet, right? They are a security vendor, right? We, we were at Reinvent and a AWS is the platform for security vendors. Yes. Middle, somewhere in the middle. But Microsoft make no mistake, they're in security. They've got some good products. I think a lot of 'em are kind of good enough and they, they tie it to the licensing and I'm not sure that works in security, but they've certainly got the ear of a lot of it pros. >>It might work in smb. >>Yeah. Yeah. It, it might. And, and I do like Zscaler. I, I know these guys poo poo the proxy model, but they've, they've done about as much with proxies as you can. And I, I think it's, it's a battle of, I love the, the, the near, you know, proxies are dead and Jay's model, you know, Jay over at c skater throw 'em back at 'em. So I, it's good to see that kind of fight going on between the two. >>Oh, it's great. Well, and, and again, ZScaler's coming at it from their cloud security angle. CrowdStrike's coming at it from endpoint. I, I do think CrowdStrike has an opportunity to build out the portfolio through m and a and maybe ecosystem. And then obviously, you know, Palo Alto's getting it done. How about Cisco? >>Yeah. Cisco's interesting. And I, I think if Cisco can make the network matter in security and it should, right? We're talking about how a lot of you need a lot of forensics to fight security today. Well, they're gonna see things long before anybody else because they have all that network data. If they can tie network security, I, I mean they could really have that business take off. But we've been saying that about Cisco for 20 years. >>But big install based though. Yeah. It's hard for a company, any company to just say, okay, hey Cisco customer sweep the floor and come with us. That's, that's >>A tough thing. They have a lot of good peace parts, right? And like duo's a good product and umbrella's a good product. They've, they've not done a good job. >>They're the opposite of these guys. >>They've not done a good job of the backend integration that, that's where Cisco needs to, to focus. And I do think g G two Patel there fixed the WebEx group and I think he's now, in fact when you talk to him, he's doing very little on WebEx that that group's running itself and he's more focused in security. So I, I think we could see a resurgence there. But you know, they have a, from a revenue perspective, it's a little misleading cuz they have this big legacy base that's in decline while they're moving to cloud and stuff. So, but they, but they, there's a lot of work there're trying to, to tie to network. >>Right. Lots of fuel for conversation. We're gonna have to carry this on, on Silicon angle.com guys. Yes. And Wikibon, lets do see us. Thank you so much for joining Dave and me giving us your insights as to this event. Where are you gonna be next? Are you gonna be on vacation? >>There's nothing more fun than mean on the cube, so, right. What's outside of that though? Yeah, you know, Christmas coming up, I gotta go see family and do the obligatory, although for me that's a lot of travel, so I guess >>More planes. Yeah. >>Hopefully not in Vegas. >>Not in Vegas. >>Awesome. Nothing against Vegas. Yeah, no, >>We love it. We >>Love it. Although I will say my year started off with ces. Yeah. And it's finishing up with Palo Alto here. The bookends. Yeah, exactly. In Vegas bookends. >>Well thanks so much for joining us. Thank you Dave. Always a pleasure to host a show with you and hear your insights. Reading your breaking analysis always kicks off my prep for show and it's always great to see, but predictions come true. So thank you for being my co-host bet. All right. For Dave Valante Enz as Carla, I'm Lisa Martin. You've been watching The Cube, the leader in live, emerging and enterprise tech coverage. Thanks for watching.

(upbeat music) >> The cloud is evolving. You know, it's no longer a set of remote services somewhere off in the cloud, in the distance. It's expanding. It's moving to on-prem. On-prem workloads are connecting to the cloud. They're spanning clouds in a way that hides the plumbing and simplifies deployment, management, security, and governance. So hybrid multicloud is the next big thing in infrastructure, and at the recent Nutanix .NEXT conference, we got a major dose of that theme, and with me to talk about what we heard at that event, what we learned, why it matters, and what it means to customers are Monica Kumar, who's the senior vice president of marketing and cloud go-to-market at Nutanix, and Tarkan Maner, who's the chief commercial officer at Nutanix. Guys, great to see you again. Welcome to the theCUBE. >> Great to be back here. >> Great to see you, Dave. >> Okay, so you just completed another .NEXT. As an analyst, I like to evaluate the messaging at an event like this, drill into the technical details to try to understand if you're actually investing in the things that you're promoting in your keynotes, and then talk to customers to see how real it is. So with that as a warning, you guys are all in on hybrid multicloud, and I have my takeaways that I'd be happy to share, but, Tarkan, what were your impressions, coming out of the event? >> Look, you had a great entry. Our goal, as Monica is going to outline, too, cloud is not a destination. It's an operating model. Our customers are basically using cloud as a business model, as an operating model. It's not just a bunch of techno mumbo-jumbo, as, kind of, you outlined. We want to make sure we make cloud invisible to the customer so they can focus on what they need to focus on as a business. So as part of that, we want to make sure the workloads, the apps, they can run anywhere the way the customer wants. So in that context, you know, our entire story was bringing customer workloads, use-cases, partner ecosystem with ISVs and cloud providers and service providers and ISPs we're working with like Citrix on end user computing, like Red Hat on cloud native, and also bringing the right products, both in terms of infrastructure capability and management capability for both operators and application developers. So bringing all these pieces together and make it simple for the customer to use the cloud as an operating model. That was the biggest goal here. >> Great, thank you. Monica, anything you'd add in terms of your takeaways? >> Well, I think Tarkan said it right. We are here to make cloud complexity invisible. This was our big event to get thousands of our customers, partners, our supporters together and unveil our product portfolio, which is much more simplified, now. It's a cloud platform. And really have a chance to show them how we are building an ecosystem around it, and really bringing to life the whole notion of hybrid multicloud computing. >> So, Monica, could you just, for our audience, just summarize the big news that came out of .NEXT? >> Yeah, we actually made four different announcements, and most of them were focused around, obviously, our product portfolio. So the first one was around enhancements to our cloud platform to help customers build modern, software-defined data centers to speed their hybrid multicloud deployments while supporting their business-critical applications, and that was really about the next version of our flagship, AOS six, availability. We announced the general availability of that, and key features really included things like built-in virtual networking, disaster recovery enhancements, security enhancements that otherwise would need a lot of specialized hardware, software, and skills are now built into our platform. And, most importantly, all of this functionality being managed through a single interface, right? Which significantly decreases the operational overhead. So that was one announcement. The second announcement was focused around data services and really making it easy for customers to simplify data management, also optimize big data and database workloads. We announced capability that now improves performances of database workloads by 2x, big data workloads by 3x, so lots of great stuff there. We also announced a new service called Nutanix Data Lens, which is a new unstructured data governance service. So, again, I don't want to go into a lot of details here. Maybe we can do it later. That was our second big announcement. The third announcement, which is really around partnerships, and we'll talk more about that, is with Microsoft. We announced the preview of Nutanix Clusters and Azure, and that's really taking our entire flagship Nutanix platform and running it on Azure. And so, now, we are in preview on that one, and we're super excited about that. And then, last but not least, and I know Tarkan is going to go into a lot more detail, is we announced a strategic partnership with Citrix around the whole future of hybrid work. So lots of big news coming out of it. I just gave you a quick summary. There's a lot more around this, as well. >> Okay. Now, I'd like to give you my honest take, if you guys don't mind, and, Tarkan, I'll steal one of your lines. Don't hate me, okay? So the first thing I'm going to say is I think, Nutanix, you have the absolute right vision. There's no question in my mind. But what you're doing is not trivial, and I think it's going to play out. It's going to take a number of years. To actually build an abstraction layer, which is where you're going, as I take it, as a platform that can exploit all the respective cloud native primitives and run virtually any workload in any cloud. And then what you're doing, as I see it, is abstracting that underlying technology complexity and bringing that same experience on-prem, across clouds, and as I say, that's hard. I will say this: the deep dives that I got at the analyst event, it convinced me that you're committed to this vision. You're spending real dollars on focused research and development on this effort, and, very importantly, you're sticking to your true heritage of making this simple. Now, you're not alone. All the non-hyperscalers are going after the multicloud opportunity, which, again, is really challenging, but my assessment is you're ahead of the game. You're certainly focused on your markets, but, from what I've seen, I believe it's one of the best examples of a true hybrid multicloud-- you're on that journey-- that I've seen to date. So I would give you high marks there. And I like the ecosystem-building piece of it. So, Tarkan, you could course-correct anything that I've said, and I'd love for you to pick up on your comments. It takes a village, you know, you're sort of invoking Hillary Clinton, to bring the right solution to customers. So maybe you could talk about some of that, as well. >> Look, actually, you hit all the right points, and I don't hate you for that. I love you for that, as you know. Look, at the end of the day, we started this journey about 10 years ago. The last two years with Monica, with the great executive team, and overall team as a whole, big push to what you just suggested. We're not necessarily, you know, passionate about cloud. Again, it's a business model. We're passionate about customer outcomes, and some of those outcomes sometimes are going to also be on-prem. That's why we focus on this terminology, hybrid multicloud. It is not multicloud, it's not just private cloud or on-prem and non-cloud. We want to make sure customers have the right outcomes. So based on that, whether those are cloud partners or platform partners like HPE, Dell, Supermicro. We just announced a partnership with Supermicro, now, we're selling our software. HPE, we run on GreenLake. Lenovo, we run on TruScale. Big support for Lenovo. Dell's still a great partner to us. On cloud partnerships, as Monica mentioned, obviously Azure. We had a big session with AWS. Lots of new work going on with Red Hat as an ISV partner. Tying that also to IBM Cloud, as we move forward, as Red Hat and IBM Cloud go hand in hand, and also tons of workarounds, as Monica mentioned. So it takes a village. We want to make sure customer outcomes deliver value. So anywhere, for any app, on any infrastructure, any cloud, regardless standards or protocols, we want to make sure we have an open system coverage, not only for operators, but also for application developers, develop those applications securely and for operators, run and manage those applications securely anywhere. So from that perspective, tons of interest, obviously, on the Citrix or the UC side, as Monica mentioned earlier, we also just announced the Red Hat partnership for cloud services. Right before that, next we highlighted that, and we are super excited about those two partnerships. >> Yeah, so, when I talked to some of your product folks and got into the technology a little bit, it's clear to me you're not wrapping your stack in containers and shoving it into the cloud and hosting it like some do. You're actually going much deeper. And, again, that's why it's hard. You could take advantage of those things, but-- So, Monica, you were on the stage at .NEXT with Eric Lockhart of Microsoft. Maybe you can share some details around the focus on Azure and what it means for customers. >> Absolutely. First of all, I'm so grateful that Eric actually flew out to the Bay Area to be live on stage with us. So very super grateful for Eric and Azure partnership there. As I said earlier, we announced the preview of Nutanix Clusters and Azure. It's a big deal. We've been working on it for a while. What this means is that a select few organizations will have an opportunity to get early access and also help shape the roadmap of our offering. And, obviously, we're looking forward to then announcing general availability soon after that. So that's number one. We're already seeing tremendous interest. We have a large number of customers who want to get their hands on early access. We are already working with them to get them set up. The second piece that Eric and I talked about really was, you know, the reason why the work that we're doing together is so important is because we do know that hybrid cloud is the preferred IT model. You know, we've heard that in spades from all different industries' research, by talking to customers, by talking to people like yourselves. However, when customers actually start deploying it, there's lots of issues that come up. There's limited skill sets, resources, and, most importantly, there's a disparity between the on-premises networking security management and the cloud networking security management. And that's what we are focused on, together as partners, is removing that barrier, the friction between on-prem and Azure cloud. So our customers can easily migrate their workloads in Azure cloud, do cloud disaster recovery, create a burst into cloud for elasticity if they need to, or even use Azure as an on-ramp to modernize applications by using the Azure cloud services. So that's one big piece. The second piece is our partnership around Kubernetes and cloud native, and that's something we've already provided to the market. It's GA with Azure and Nutanix cloud platform working together to build Kubernetes-based applications, container-based applications, and run them and manage them. So there's a lot more information on nutanix.com/azure. And I would say, for those of our listeners who want to give it a try and who want their hands on it, we also have a test drive available. You can actually experience the product by going to nutanix.com/azure and taking the test drive. >> Excellent. Now, Tarkan, we saw recently that you announced services. You've got HPE GreenLake, Lenovo, their Azure service, which is called TruScale. We saw you with Keith White at HPE Discover. I was just with Keith White this week, by the way, face to face. Awesome guy. So that's exciting. You got some investments going on there. What can you tell us about those partnerships? >> So, look, as we talked through this a little bit, the HPE relationship is a very critical relationship. One of our fastest growing partnerships. You know, our customers now can run a Nutanix software on any HPE platform. We call it DX, is the platform. But beyond that, now, if the customers want to use HPE service as-a-service, now, Nutanix software, the entire stack, it's not only hybrid multicloud platform, the database capability, EUC capability, storage capability, can run on HPE's service, GreenLake service. Same thing, by the way, same way available on Lenovo. Again, we're doing similar work with Dell and Supermicro, again, giving our customers choice. If they want to go to a public club partner like Azure, AWS, they have that choice. And also, as you know, I know Monica, you're going to talk about this, with our GSI partnerships and new service provider program, we're giving options to customers because, in some other regions, HPE might not be their choice or Azure not be choice, and a local telco might the choice in some country like Japan or India. So we give options and capability to the customers to run Nutanix software anywhere they like. >> I think that's a really important point you're making because, as I see all these infrastructure providers, who are traditionally on-prem players, introduce as-a-service, one of the things I'm looking for is, sure, they've got to have their own services, their own products available, but what other ecosystem partners are they offering? Are they truly giving the customers choice? Because that's, really, that's the hallmark of a cloud provider. You know, if we think about Amazon, you don't always have to use the Amazon product. You can use actually a competitive product, and that's the way it is. They let the customers choose. Of course, they want to sell their own, but, if you innovate fast enough, which, of course, Nutanix is all about innovation, a lot of customers are going to choose you. So that's key to these as-a-service models. So, Monica, Tarkan mentioned the GSIs. What can you tell us about the big partners there? >> Yeah, definitely. Actually, before I talk about GSIs, I do want to make sure our listeners understand we already support AWS in a public cloud, right? So Nutanix totally is available in general, generally available on AWS to use and build a hybrid cloud offering. And the reason I say that is because our philosophy from day one, even on the infrastructure side, has been freedom of choice for our customers and supporting as large a number of platforms and substrates as we can. And that's the notion that we are continuing, here, forward with. So to talk about GSIs a bit more, obviously, when you say one platform, any app, any cloud, any cloud includes on-prem, it includes hyperscalers, it includes the regional service providers, as well. So as an example, TCS is a really great partner of ours. We have a long history of working together with TCS, in global 2000 accounts across many different industries, retail, financial services, energy, and we are really focused, for example, with them, on expanding our joint business around mission critical applications deployment in our customer accounts, and specifically our databases with Nutanix Era, for example. Another great partner for us is HCL. In fact, HCL's solution SKALE DB, we showcased at .NEXT just yesterday. And SKALE DB is a fully managed database service that HCL offers which includes a Nutanix platform, including Nutanix Era, which is our database service, along with HCL services, as well as the hardware/software that customers need to actually run their business applications on it. And then, moving on to service providers, you know, we have great partnerships like with Cyxtera, who, in fact, was the service provider partner of the year. That's the award they just got. And many other service providers, including working with, you know, all of the edge cloud, Equinix. So, I can go on. We have a long list of partnerships, but what I want to say is that these are very important partnerships to us. All the way from, as Tarkan said, OEMs, hyperscalers, ISVs, you know, like Red Hat, Citrix, and, of course, our service provider, GSI partnerships. And then, last but not least, I think, Tarkan, I'd love for you to maybe comment on our channel partnerships as well, right? That's a very important part of our ecosystem. >> No, absolutely. You're absolutely right. Monica. As you suggested, our GSI program is one of the best programs in the industry in number of GSIs we support, new SP program, enterprise solution providers, service provider program, covering telcos and regional service providers, like you suggested, OVH in France, NTT in Japan, Yotta group in India, Cyxtera in the US. We have over 50 new service providers signed up in the last few months since the announcement, but tying all these things, obviously, to our overall channel ecosystem with our distributors and resellers, which is moving very nicely. We have Christian Alvarez, who is running our channel programs globally. And one last piece, Dave, I think this was important point that Monica brought up. Again, give choice to our customers. It's not about cloud by itself. It's outcomes, but cloud is an enabler to get there, especially in a hybrid multicloud fashion. And last point I would add to this is help customers regardless of the stage they're in in their cloud migration. From rehosting to replatforming, repurchasing or refactoring, rearchitecting applications or retaining applications or retiring applications, they will have different needs. And what we're trying to do, with Monica's help, with the entire team: choice. Choice in stage, choice in maturity to migrate to cloud, and choice on platform. >> So I want to close. First of all, I want to give some of my impressions. So we've been watching Nutanix since the early days. I remember vividly standing around the conference call with my colleague at the time, Stu Miniman. The state-of-the-art was converged infrastructure, at the time, bolting together storage, networking, and compute, very hardware centric. And the founding team at Nutanix told us, "We're going to have a software-led version of that." And you popularized, you kind of created the hyperconverged infrastructure market. You created what we called at the time true private cloud, scaled up as a company, and now you're really going after that multicloud, hybrid cloud opportunity. Jerry Chen and Greylock, they just wrote a piece called Castles on the Cloud, and the whole concept was, and I say this all the time, the hyperscalers, last year, just spent a hundred billion dollars on CapEx. That's a gift to companies that can add value on top of that. And that's exactly the strategy that you're taking, so I like it. You've got to move fast, and you are. So, guys, thanks for coming on, but I want you to both-- maybe, Tarkan, you can start, and Monica, you can bring us home. Give us your wrap up, your summary, and any final thoughts. >> All right, look, I'm going to go back to where I started this. Again, I know I go back. This is like a broken record, but it's so important we hear from the customers. Again, cloud is not a destination. It's a business model. We are here to support those outcomes, regardless of platform, regardless of hypervisor, cloud type or app, making sure from legacy apps to cloud native apps, we are there for the customers regardless of their stage in their migration. >> Dave: Right, thank you. Monica? >> Yeah. And I, again, you know, just the whole conversation we've been having is around this but I'll remind everybody that why we started out. Our journey was to make infrastructure invisible. We are now very well poised to helping our customers, making the cloud complexity invisible. So our customers can focus on business outcomes and innovation. And, as you can see, coming out of .NEXT, we've been firing on all cylinders to deliver this differentiated, unified hybrid multicloud platform so our customers can really run any app, anywhere, on any cloud. And with the simplicity that we are known for because, you know, our customers love us. NPS 90 plus seven years in a row. But, again, the guiding principle is simplicity, portability, choice. And, really, our compass is our customers. So that's what we are focused on. >> Well, I love not having to get on planes every Sunday and coming back every Friday, but I do miss going to events like .NEXT, where I meet a lot of those customers. And I, again, we've been following you guys since the early days. I can attest to the customer delight. I've spent a lot of time with them, driven in taxis, hung out at parties, on buses. And so, guys, listen, good luck in the next chapter of Nutanix. We'll be there reporting and really appreciate your time. >> Thank you so much. >> Thank you so much, Dave. >> All right, and thank you for watching, everybody. This is Dave Vellante for theCUBE, and, as always, we'll see you next time. (light music)

(upbeat electronic music plays) >> Hey everyone, welcome back to theCube's coverage of .NEXT 2021 Virtual. I'm John Furrier, hosts of theCube. We have two great guests, Rajiv Mirani, who's the Chief Technology Officer, and Thomas Cornely, SVP of Product Management. Day Two keynote product, the platform, announcements, news. A lot of people, Rajiv, are super excited about the, the platform, uh, moving to a subscription model. Everything's kind of coming into place. How are the customers, uh, seeing this? How they adopted hybrid cloud as a hybrid, hybrid, hybrid, data, data, data? Those are the, those are the, that's the, that's where the puck is right now. You guys are there. How are customers seeing this? >> Mirani: Um, um, great question, John, by the way, great to be back here on theCube again this year. So when we talk to our customers, pretty much, all of them agreed that for them, the ideal state that they want to be in is a hybrid world, right? That they want to essentially be able to run both of those, both on the private data center and the public cloud, and sort of have a common platform, common experience, common, uh, skillset, same people managing, managing workloads across both locations. And unfortunately, most of them don't have that that tooling available today to do so, right. And that's where the platform, the Nutanix platform's come a long way. We've always been great at running in the data center, running every single workload, we continue to make great strides on our core with the increased performance for, for the most demanding, uh, workloads out there. But what we have done in the last couple of years has also extended this platform to run in the public cloud and essentially provide the same capabilities, the same operational behavior across locations. And that's when you're seeing a lot of excitement from our customers because they really want to be in that state, for it to have the common tooling across work locations, as you can imagine, we're getting traction. Customers who want to move workloads to public cloud, they don't want to spend the effort to refactor them. Or for customers who really want to operate in a hybrid mode with things like disaster recovery, cloud bursting, workloads like that. So, you know, I think we've made a great step in that direction. And we look forward to doing more with our customers. >> Furrier: What is the big challenge that you're seeing with this hybrid transition from your customers and how are you solving that specifically? >> Mirani: Yeah. If you look at how public and private operate today, they're very different in the kind of technologies used. And most customers today will have two separate teams, like one for their on-prem workloads, using a certain set of tooling, a second completely different team, managing a completely different set of workloads, but with different technologies. And that's not an ideal state in some senses, that's not true hybrid, right? It's like creating two new silos, if anything. And our vision is that you get to a point where both of these operate in the same manner, you've got the same people managing all of them, the same workloads anyway, but similar performance, similar SaaS. So they're going to literally get to point where applications and data can move back and forth. And that's, that's, that's where I think the real future is for hybrid >> Furrier: I have to ask you a personal question. As the CTO, you've got be excited with the architecture that's evolving with hybrid and multi-cloud, I mean, I mean, it's pretty, pretty exciting from a tech standpoint, what is your reaction to that? >> Mirani: %100 and it's been a long time coming, right? We have been building pieces of this over years. And if you look at all the product announcements, Nutanix has made over the last few years and the acquisitions that made them and so on, there's been a purpose behind them. That's been a purpose to get to this model where we can operate a customer's workloads in a hybrid environment. So really, really happy to see all of that come together. Years and years of work finally finally bearing fruit. >> Furrier: Well, we've had many conversations in the past, but it congratulates a lot more to do with so much more action happening. Thomas, you get the keys to the kingdom, okay, and the product management you've got to prioritize, you've got to put it together. What are the key components of this Nutanix cloud platform? The hybrid cloud, multi-cloud strategy that's in place, because there's a lot of headroom there, but take us through the key components today and then how that translates into hybrid multi-cloud for the future. >> Cornely: Certainly, John, thank you again and great to be here, and kind of, Rajiv, you said really nicely here. If you look at our portfolio at Nutanix, what we have is great technologies. They've been sold as a lot of different products in the past, right. And what we've done last few months is we kind of bring things together, simplify and streamline, and we align everything around a cloud platform, right? And this is really the messaging that we're going after is look, it's not about the price of our solutions, but business outcomes for customers. And so are we focusing on pushing the cloud platform, which we encompasses five key areas for us, which we refer to as cloud infrastructure, no deficiencies running your workloads. Cloud management, which is how you're going to go and actually manage, operate, automate, and get governance. And then services on top that started on all around data, right? So we have unified storage, finding the objects, data services. We have database services. Now we have outset of desktop services, which is for EMC. So all of this, the big change for us is this is something that, you know, you can consume in terms of solutions and consume on premises. As Rajiv discussed, you know, we can take the same platform and deploy it in public cloud regions now, right? So you can now get no seamless hybrid cloud, same operating model. But increasingly what we're doing is taking your solutions and re-targeting issues and problems at workers running native public clouds. So think of this as going, after automating more governance, security, you know, finding objects, database services, wherever you're workload is running. So this is taking this portfolio and reapplying it, and targeting on prem at the edge in hybrid and in christening public cloud in ATV. >> Furrier: That's awesome. I've been watching some of the footage and I was noticing quite a lot of innovation around virtualized, networking, disaster, recovery security, and data services. It's all good. You guys were, and this is in your wheelhouse. I know you guys are doing this for many, many years. I want to dive deeper into that because the theme right now that we've been reporting on, you guys are hitting right here what the keynote is cloud scale is about faster development, right? Cloud native is about speed, it's about not waiting for these old departments, IT or security to get back to them in days or weeks and responding to either policy or some changes, you got to move faster. And data, data is critical in all of this. So we'll start with virtualized networking because networking again is a key part of it. The developers want to go faster. They're shifting left, take us through the virtualization piece of how important that is. >> Mirani: Yeah, that's actually a great question as well. So if you think about it, virtual networking is the first step towards building a real cloud like infrastructure on premises that extends out to include networking as well. So one of the key components of any cloud is automation. Another key component is self service and with the API, is it bigger on virtual networking All of that becomes much simpler, much more possible than having to, you know, qualify it, work with someone there to reconfigure physical networks and slots. We can, we can do that in a self service way, much more automated way. But beyond that, the, the, the notion of watching networks is really powerful because it helps us to now essentially extend networks and, and replicate networks anywhere on the private data center, but in the public cloud as well. So now when customers move their workloads, we'd already made that very simple with our clusters offering. But if you're only peek behind the layers a little bit, it's like, well, yea, but the network's not the same on the side. So now it, now it means that a go re IP, my workloads create new subnets and all of that. So there was a little bit of complication left in that process. So to actual network that goes away also. So essentially you can repeat the same network in both locations. You can literally move your workloads, no redesign of your network acquired and still get that self service and automation capabilities of which cookies so great step forward, it really helps us complete the infrastructure as a service stack. We had great storage capabilities before, we create compute capabilities before, and sort of networking the third leg and all of that. >> Furrier: Talk about the complexity here, because I think a lot of people will look at dev ops movement and say, infrastructure is code when you go to one cloud, it's okay. You can, you can, you know, make things easier. Programmable. When, when you start getting into data center, private data centers, or essentially edges now, cause if it's distributed cloud environment or cloud operations, it's essentially one big cloud operation. So the networks are different. As you said, this is a big deal. Okay. This is sort of make infrastructure as code happen in multiple environments across multiple clouds is not trivial. Could you talk about the main trends and how you guys see this evolving and how you solve that? >> Mirani: Yeah. Well, the beauty here is that we are actually creating the same environment everywhere, right? From, from, from point of view of networking, compute, and storage, but also things like security. So when you move workloads, things with security, posture also moves, which is also super important. It's a really hard problem, and something a lot of CIO's struggle with, but having the same security posture in public and private clouds reporting as well. So with this, with this clusters offering and our on-prem offering competing with the infrastructure service stack, you may not have this capability where your operations really are unified across multicloud hybrid cloud in any way you run. >> Furrier: Okay, so if I have multiple cloud vendors, there are different vendors. You guys are creating a connection unifying those three. Is that right? >> Mirani: Essentially, yes, so we're running the same stack on all of them and abstracting away the differences between the clouds that you can run operations. >> Furrier: And when the benefits, the benefits of the customers are what? What's the main, what's the main benefit there? >> Mirani: Essentially. They don't have to worry about, about where their workloads are running. Then they can pick the best cloud for their workloads. It can seamlessly move them between Cloud. They can move their data over easily, and essentially stop worrying about getting locked into a single, into a single cloud either in a multi-cloud scenario or in a hybrid cloud scenario, right. There many, many companies now were started on a cloud first mandate, but over time realized that they want to move workloads back to on-prem or the other way around. They have traditional workloads that they started on prem and want to move them to public cloud now. And we make that really simple. >> Furrier: Yeah. It's kind of a trick question. I wanted to tee that up for Thomas, because I love that kind of that horizontal scales, what the cloud's all about, but when you factor data into it, this is the sweet spot, because this is where, you know, I think it gets really exciting and complicated too, because, you know, data's got, can get unwieldy pretty quickly. You got state got multiple applications, Thomas, what's your, what can you share the data aspect of this? This is super, super important. >> Absolutely. It's, you know, it's really our core source of differentiation, when you think about it. That's what makes Nutanix special right? In, in the market. When we talk about cloud, right. Actually, if you've been following Nutanix for years, you know, we've been talking a lot about making infrastructure invisible, right? The new way for us to talk about what we're doing, with our vision is, is to make clouds invisible so that in the end, you can focus on your own business, right? So how do you make Cloud invisible? Lots of technology is at the application layer to go and containerize applications, you know, make them portable, modernize them, make them cloud native. That's all fine when you're not talking of state class containers, that the simplest thing to move around. Right. But as we all know, you know, applications end of the day, rely on data and measure the data across all of these different locations. I'm not even going to go seconds. Cause that's almost a given, you're talking about attribution. You can go straight from edge to on-prem to hybrid, to different public cloud regions. You know, how do you go into the key control of that and get consistency of all of this, right? So that's part of it is being aware of where your data is, right? But the other part is that inconsistency of set up data services regardless of where you're running. And so this is something that we look at the cloud platform, where we provide you the cloud infrastructure go and run the applications. But we also built into the cloud platform. You get all of your core data services, whether you have to consume file services, object services, or database services to really support your application. And that will move with your application, that is the key thing here by bringing everything onto the same platform. You now can see all operations, regardless of where you're running the application. The last thing that we're adding, and this is a new offering that we're just launching, which is a service, it's called, delete the dead ends. Which is a solution that gives you visibility and allow you to go and get better governance around all your data, wherever it may live, across on-prem edge and public clouds. That's a big deal again, because to manage it, you first have to make sense of it and get control over it. And that's what data answer's is going to be all about. >> Furrier: You know, one of the things we've we've been reporting on is data is now a competitive advantage, especially when you have workflows involved, um, super important. Um, how do you see customers going to the edge? Because if you have this environment, how does the data equation, Thomas, go to the edge? How do you see that evolving? >> Cornely: So it's yeah. I mean, edge is not one thing. And that's actually the biggest part of the challenge of defining what the edge is depending on the customer that you're working with. But in many cases you get data ingesting or being treated at the edge that you then have to go move to either your private cloud or your public cloud environment to go and basically aggregate it, analyze it and get insights from it. Right? So this is where a lot of our technologies, whether it's, I think the object's offering built in, we'll ask you to go and make the ingest over great distances over the network, right? And then have your common data to actually do an ethics audit over our own object store. Right? Again, announcements, we brought into our storage solutions here, we want to then actually organize it then actually organize it directly onto the objects store solution. Nope. Using things, things like or SG select built into our protocols. So again, make it easy for you to go in ingest anywhere, consolidate your data, and then get value out of it. Using some of the latest announcements on the API forms. >> Furrier: Rajiv databases are still the heart of most applications in the enterprise these days, but databases are not just the data is a lot of different data. Moving around. You have a lot a new data engineering platforms coming in. A lot of customers are scratching their head and, and they want to kind of be, be ready and be ready today. Talk about your view of the database services space and what you guys are doing to help enterprise, operate, manage their databases. >> Mirani: Yeah, it's a super important area, right? I mean, databases are probably the most important workload customers run on premises and pretty close on the public cloud as well. And if you look at it recently, the tooling that's available on premises, fairly traditional, but the clouds, when we integrate innovation, we're going to be looking at things like Amazon's relational database service makes it an order of magnitude simpler for our customers to manage the database. At the same time, also a proliferation of databases and we have the traditional Oracle and SQL server. But if you have open source Mongo, DB, and my SQL, and a lot of post-grads, it's a lot of different kinds of databases that people have to manage. And now it just becomes this cable. I have the spoke tooling for each one of them. So with our Arab product, what we're doing is essentially creating a data management layer, a database management layer that unifies operations across your databases and across locations, public cloud and private clouds. So all the operations that you need, you do, which are very complicated in, in, in, in with traditional tooling now, provisioning of databases backing up and restoring them providing a true time machine capabilities, so you can pull back transactions. We can copy data management for your data first. All of that has been tested in Era for a wide variety of database engines, your choice of database engine at the back end. And so the new capabilities are adding sort of extend that lead that we have in that space. Right? So, so one of the things we announced at .Next is, is, is, is one-click storage scaling. So one of the common problems with databases is as they grow over time, it's not running out of storage capacity. Now re-provisions to storage for a database, migrate all the data where it's weeks and months of look, right? Well, guess what? With Era, you can do that in one click, it uses the underlying AOS scale-out architecture to provision more storage and it does it have zero downtime. So on the fly, you can resize your databases that speed, you're adding some security capabilities. You're adding some capabilities around resilience. Era continues to be a very exciting product for us. And one of the things, one of the real things that we are really excited about is that it can really unify database operations between private and public. So in the future, we can also offer an aversion of Era, which operates on native public cloud instances and really excited about that. >> Furrier: Yeah. And you guys got that two X performance on scaling up databases and analytics. Now the big part point there, since you brought up security, I got to ask you, how are you guys talking about security? Obviously it's embedded in from the beginning. I know you guys continue to talk about that, but talk about, Rajiv, the security on, on that's on everyone's mind. Okay. It goes evolving. You seeing ransomware are continuing to happen more and more and more, and that's just the tip of the iceberg. What do you guys, how are you guys helping customers stay secure? >> Mirani: Security is something that you always have to think about as a defense in depth when it comes to security, right? There's no one product that, that's going to do everything for you. That said, what we are trying to do is to essentially go with the gamut of detection, prevention, and response with our security, and ransom ware is a great example of that, right. We've partnered with Qualys to essentially be able to do a risk assessment of your workloads, to basically be able to look into your workloads, see whether they have been bashed, whether they have any known vulnerabilities and so on. To try and prevent malware from infecting your workloads in the first place, right? So that's, that's the first line of defense. Now not systems will be perfect. Some, some, some, some malware will probably get in anyway But then you detect it, right. We have a database of all the 4,000 ransomware signatures that you can use to prevent ransomware from, uh, detecting ransom ware if it does infect the system. And if that happens, we can prevent it from doing any damage by putting your fire systems and storage into read-only mode, right. We can also prevent lateral spread of, of your ransomware through micro-segmentation. And finally, if you were, if you were to invade, all those defenses that you were actually able to encrypt data on, on, on a filer, we have immutable snapshots, they can recover from those kinds of attacks. So it's really a defense in depth approach. And in keeping with that, you know, we also have a rich ecosystem of partners while this is one of them, but older networks market sector that we work with closely to make sure that our customers have the best tooling around and the simplest way to manage security of their infrastructure. >> Furrier: Well, I got to say, I'm very impressed guys, by the announcements from the team I've been, we've been following Nutanix in the beginning, as you know, and now it's back in the next phase of the inflection point. I mean, looking at my notebook here from the announcements, the VPC virtual networking, DR Observability, zero trust security, workload governance, performance expanded availability, and AWS elastic DR. Okay, we'll get to that in a second, clusters on Azure preview cloud native ecosystem, cloud control plane. I mean, besides all the buzzword bingo, that's going on there, this is cloud, this is a cloud native story. This is distributed computing. This is virtualization, containers, cloud native, kind of all coming together around data. >> Cornely: What you see here is, I mean, it is clear that it is about modern applications, right? And this is about shifting strategy in terms of focusing on the pieces where we're going to be great at. And a lot of these are around data, giving you data services, data governance, not having giving you an invisible platform that can be running in any cloud. And then partnering, right. And this is just recognizing what's going on in the world, right? People want options, customers and options. When it comes to cloud, they want options to where they're running the reports, what options in terms of, whether it be using to build the modern applications. Right? So our big thing here is providing and being the best platform to go and actually support for Devers to come in and build and run their new and modern applications. That means that for us supporting a broad ecosystem of partners, entrepreneur platform, you know, we announced our partnership with Red Hat a couple of months ago, right? And this is going to be a big deal for us because again, we're bringing two leaders in the industry that are eminently complimentary when it comes to providing you a complete stack to go and build, run, and manage your client's applications. When you do that on premises, utilizing like the preferred ATI environment to do that. Using the Red Hat Open Shift, or, you're doing this open to public cloud and again, making it seamless and easy, to move the applications and their supporting data services around, around them that support them, whether they're running on prem in hybrid winter mechanic. So client activity is a big deal, but when it comes to client activity, the way we look at this, it's all about giving customers choice, choice of that from services and choice of infrastructure service. >> Furrier: Yeah. Let's talk to the red hat folks, Rajiv, it's you know, it's, they're an operating system thinking company. You know, you look at the internet now in the cloud and edge, and on-premise, it's essentially an operating system. you need your backup and recovery needs to disaster recovery. You need to have the HCI, you need to have all of these elements part of the system. It's, it's, it's, it's building on top of the existing Nutanix legacy, then the roots and the ecosystem with new stuff. >> Mirani: Right? I mean, it's, in fact, the Red Hat part is a great example of, you know, the perfect marriage, if you will, right? It's, it's, it's the best in class platform for running the cloud-native workloads and the best in class platform with a service offering in there. So two really great companies coming together. So, so really happy that we could get that done. You know, the, the point here is that cloud native applications still need infrastructure to run off, right? And then that infrastructure, if anything, the demands on that and growing it since it's no longer that hail of, I have some box storage, I have some filers and, you know, just don't excite them, set. People are using things like object stores, they're using databases increasingly. They're using the Kafka and Map Reduce and all kinds of data stores out there. And back haul must be great at supporting all of that. And that's where, as Thomas said, earlier, data services, data storage, those are our strengths. So that's certainly a building from platform to platform. And then from there onwards platform services, great to have right out of the pocket. >> Furrier: People still forget this, you know, still hardware and software working together behind the scenes. The old joke we have here on the cube is server less is running on a bunch of servers. So, you know, this is the way that is going. It's really the innovation. This is the infrastructure as code truly. This is what's what's happened is super exciting. Rajiv, Thomas, thank you guys for coming on. Always great to talk to you guys. Congratulations on an amazing platform. You guys are developing. Looks really strong. People are giving it rave reviews and congratulations on, on, on your keynotes. >> Cornely: Thank you for having us >> Okay. This is theCube's coverage of.next global virtual 2021 cube coverage day two keynote review. I'm John Furrier Furrier with the cube. Thanks for watching.

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is breaking analysis with Dave Vellante. >> ServiceNow is a company that investors love to love, but there's caution in the investor community right now is confusion about transitory inflation and higher interest rates looms. ServiceNow also suffers from a perfection syndrome of sorts. The company has seen that the slightest misstep can cause many freak outs from the investor community. So what it's done is it's architected a financial and communications model that allows it to beat expectations and raise its outlook on a consistent basis. Regardless, ServiceNow appears to be on track to vie for what its CEO Bill McDermott refers to as the next great enterprise software company. Wait, I thought Marc Benioff had his hands on that steering wheel. Hello everyone, and welcome to this week's Wikibon CUBE insights powered by ETR. In this breaking analysis, we'll dig into one of the companies we began following almost 10 years ago and provide some thoughts on ServiceNow's March to 15 billion by 2026, which we think is a highly probable achievement. In 2020, despite the contraction in IT spending, SeviceNow outperformed both the S&P 500 and the NASDAQ, but here's a view of 2021. And you can see while the stock has done well since it saw a softness in May and again in early June, and it bounced off that double bottom, it's performance is well below those other benchmarks. This is not a big surprise given the fact that this is a high growth stock and we all know that those names with high multiples get hurt in an inflationary environment, but still the gaps are notable. This is especially true given the performance of the company. It's not often that you see a company with four to $5 billion in revenue growing at a 30% clip, throwing off billions of dollars in free cash flow and increasing operating margins at 100 basis points a year and promising to do that over the next several years. In fact, I don't think we've ever seen that before. I remember years ago, when the trade press was criticizing SeviceNow for its lofty valuation, despite the fact that it was losing money, then CEO, Frank Slootman said to me, "Dave, we can be highly profitable tomorrow if we want it to be, but this is a marathon and we're planning to go big." So essentially Slootman was telling me that this company was going to be an ATM machine that prints money. And that seems to be how it's shaping up. I happened to be at SeviceNow headquarters in 2017, literally the first day on the job for John Donahoe, the CEO replaced Slootman, and I remember while I was there thinking Donahoe was certainly capable, but why the heck I said, would the board let Frank Slootman get away? You know what? It turned great for Slootman, he's at snowflake. Donahoe, I always felt was a consumer guy anyway, and not long for SeviceNow. And now you have this guy, new CEO, Bill McDermott at the helm. He's not a more qualified CEO for the company in my view. About two months ago, McDermott led a virtual investor day. We've had McDermott on theCUBE a couple of times back when he was CEO of SAP and this individual is very compelling. He's got JFK like looks and charisma, but more than that, he's passionate and convincing. And he obviously knows enterprise software. And with conviction, he laid the groundwork for how SeviceNow will get to $10 billion in revenue by 2024 on its way to 15 billion two years thereafter. And one of the big things McDermott's stressed was they're going to get there without any big M&A moves. And that's important because previously the door was left open for that possibility. And now the company is assuring investors that it can get there organically, even with slower growth. So this chart implies no big M&A, and you can see Slootman handed over the reigns at that year one tick on the horizontal axis. This was not a turnaround story. It was a rocket ship at the time. And look at the logos on this chart. This is a revenue view and SeviceNow is aiming to be the fastest to get to 10 billion in software industry history. SeviceNow is valuation just to sort of shift gears here for a minute blew by workdays years ago. Its sites are now set on SAP which is currently valued at 170 billion. And then there's Oracle and Salesforce. They're at around 250 billion and 225 billion in valuation respectively. And these lines back to revenue show the trajectory that these companies took to get to 10 billion. And you can see how SeviceNow plans to get there with those dotted lines. And this is why I call this a collision course with Salesforce, because I think Marc Benioff might say, "Hey, we are ready." Are the next great enterprise software company. We have no plans to give up that post, that mantle anytime soon. I want to share a clip from four years ago. something we've been saying for a long, long time. Roll the clip. >> As they say their goal now is to be four billion by 2020. It feels like, you know, when we first covered SeviceNow knowledge, we said, wow, this company reminds us a lot of the early days of Salesforce. They've got this platform you can develop on this platform, you know, call it paths or, you know, whatever you want to call it, but we at the time said, they're on a collision course with Salesforce. Now there's plenty of room for both of those companies in the marketplace. Salesforce obviously focused predominantly on Salesforce automation, SeviceNow really on workflow automation, but you can see those sort of two markets coming together. >> Now you may be thinking isn't Salesforce's revenue like 5X that of SeviceNow? And yes it is. But I would say a couple of things. One is that Salesforce has gotten to where it is with a lot of M&A, more than 60 acquisitions. At some high profile wants to like slack and Tableau as well as MuleSoft and Heroku back in the day and many others. So we'll see how far McDermott can get before he reverts to his inquisitive self that we saw at SAP. But the second thing I'll say is serviceNow positions itself as the platform of platforms. And the thing is it runs its own cloud. And when it does acquisitions, it replatforms the acquiree into the now platform so that it can drive integrations more seamlessly. That's fundamentally part of its value proposition, a big part of its value proposition. And that means it's somewhat limited on the acquisitions it can make, it has to be pretty selective. Otherwise it's got to do a heavy lift to get it the now platform. It's the power of the models, especially if customers can get to a single CMDB, that configuration database management system, which by the way, a lot of customers never get to that kind of skirt that, but remember SeviceNow is like the ERP for IT. So the more you can get to a single data model, the more effective you're going to be, especially in this data era where you got to put data at the core of your organization, something we've talked about a lot. And the third thing I'll mention the SeviceNow wants to use this platform to attack what it sees as a very large TAM as shown here. Now, a couple of things I want to point out. One is when SeviceNow IPO in 2012, a lot of the analysts said that they were way overvalued because they were in a market. It was help desk and writing tickets was a $2 billion business that was in decline and BMC remedy. Wasn't really that big of a base to attack. In 2013, the Wikibon team took a stab at sizing the TAM. I dug back into the old Wiki. We had well over 30 billion at the time and we expected the company to move deeper into IT and then beyond IT into lines of business and line of business management. Yeah, we felt we were being conservative. We thought the number could be as big as 100 billion, but we felt like putting that number out there, was too aggressive but, you know, it turns out from SeviceNow standpoint, it sees these new software opportunities coming together. And SeviceNow in a way they can double dip both in and beyond their current markets. What I mean by that is it can partner with, for instance, HCM vendors and then at the same time offer employee workflows. They can partner or even purchase RPA tools from specialists like UI path or automation anywhere. And it can go acquire a company which it did like Intel a bot and integrate what I would consider lighter-weight RPA into its platform. So it can manage workflows for best of breed and pick off functionality throughout the software stack. Now what's interesting in this chart is first, the size of the TAM that SeviceNow sees 175 billion, but also how it's now reorganizing its business around workflows, which you see in the left-hand side. This was done of course, to simplify the many, many, many things that you can buy from SeviceNow. But there's also speculation that SeviceNow is leveraging its orchestration and service catalog capabilities, which are meaningful from a revenue standpoint and using them to power these workflows because the way it was organized was both confusing and not as effective as it could be. Now, it's well known that SeviceNow has ITSM this comprises the biggest piece of its revenue pie, probably a couple billion. And it's adding to that with ITSM pro and ITSM enterprise going deeper, deeper into the ITSM space. And it's ITAM business is also doing well against the likes of Datadog and Elastic and Splunk and others and its acquisition of LightStep. It's going to push it further into this space, which is both crowded is morphing into observability as we've been reporting. What's unclear though is how well, for instance, HR and the CSM businesses are doing as sort of standalone businesses, you might remember they used to be standalone businesses with standalone GMs. They've sort of changed that up a little bit. So this is potentially not only a way to simplify, but also shuffle the deck chairs a bit and maybe prop up the non IT workflows, which then allows SeviceNow to show this chart, which essentially says to the street, see, we have this huge TAM and our TAM expansion strategy is working as the overall business is growing nicely yet the mix is shifting toward customer, employee and creator workflows. See how awesome our business is and see how smart we are. So this is possibly a way to hide some of the warts and accentuate the growth. Look, there's not a lot to criticize SeviceNow about, but they've been pretty good at featuring what some perceive as weaknesses. Like for instance, the way it marketed it's a multi-instance and turned that into an advantage as a better model. Even though the whole cloud world was going multitenant and within a ServiceNow you got to really plan new releases, which they drop every six months, although CJ decide. So he's SeviceNows head of products. He did say at the investor meeting, that event that they held last May, that they do certain releases now bi-monthly and even some bi-weekly. So, yeah, maybe a little bit of nitpicking here, but I always liked to question when such changes are made to the reporting structures to the street. And if workflows are the new black, so to speak, I wonder will SeviceNow start pricing by workflows versus what really has been a legacy of, you know, what's your ticket volume and how many agents need access to the model and we'll charge you accordingly? Now, I'm not a service pricing expert and they don't make it easy to figure out that pricing. So let's dig a little bit more on that and keep an eye on it. Now I want to turn to the customers survey data from ETR on ServiceNow. First, here's the latest update on IT spending from ETR, something that we've been tracking for quite some time. We've been consistently saying to expect this year a seven to 8% growth for 2021 IT spend off of last year's contraction. And the latest ETR survey data puts it right at 8%. So we really liked that number. You know, could even be higher push 10% this year. Now, let's look at the spending profile within the ETR dataset. Of the 1100 plus respondents to this quarter, there were 377 SeviceNow customers, and this chart shows the breakdown of net score or spending velocity among those respondents. Remember, net score is a measure of that spending momentum. What it does is it takes the lime green bar, which is adopting new, that says 11% of that 377 customers are adopting ServiceNow for the first time. It takes that lime green and it adds the forest green bar that's growth in spending of 6% or more this half relative to the first half. That's 43% of the customers that have been surveyed here. And then it subtracts out the reds, which is that pinkish is spending less, that's 3%, small number of spending less. And then the bright red is we're leaving the platform. That's a minuscule 1% of the respondents. And you can see the rest in that gray area is flat spending, which is ignored. And so what this does is it calculates out, you'd take the greens minus the reds. It calculates out to a net score 50% for SeviceNow, which is well above that magic 40% elevated mark that we'd like to see. It's rare for a company of this size, except for the hyperscalers. You see AWS and Microsoft and Google are up that high and oh, there's another great enterprise software company at the 45% net score level. Guess who that is, salesforce.com. But anyway, it's rare to see that large of a company have that much spending momentum in the ETR surveys. Now let's take a look at the time series data for ServiceNow. This chart shows the net score granularity over time. So you see the bars, that time series, the blue line is net score. And you can see that it was dragged down during last year's lockdown. As, even though SeviceNow did pretty well last year and it's now spiking back to pre-COVID levels, which is a very positive sign for the company. That red call-out that ETR makes it shows market share. That's an indicator of pervasiveness in the dataset. I'm not overlyconcern there that downturn. I don't think it's a meaningful indicator because ServiceNow revenue is skewed towards a big spender accounts and this is an account unit indicator, if you will not spending level metric. And okay, and here's another reason and why I'm not concerned about SeviceNow is a so-called market share number in the ETR dataset as ETR defines it. This is an X, Y Z view chart that we'd like to show here. We've got net score on the vertical axis and market share in the horizontal plane. This is focusing on enterprise software. So remember that 40% red line is the magic level, anything above that is really indicative of momentum. Oh look, there's Salesforce and ServiceNow on that little collision course that I talked about. Now, CEO McDermott, I would say as by the way, would his predecessors, look, we're a platform of platforms and we partner with other companies, we'll meet at the customer level and sure we'll integrate functions where we think it can add value to customers. But we also understand we have to work with the vendors that our customers are using. So it's all good, plenty of room for growth for all of us, which by the way is true. But I would say this, anyone who's ever been in the enterprise software industry knows that enterprise software execs and their salespeople believe that every dollar spent on software should go to them. And if it's a good market with momentum and growth, they believe they can either organically write software to deliver customer function and value, or they can acquire to fill gaps. So, well, what McDermott would say is true. The likes of Oracle, Microsoft, SAP, Salesforce, Infor, et cetera, they all want as big of a budget piece as possible in the enterprise software space. That's just the way it is. Now, we're going to close with some anecdotal comments from ETR insights, formerly called VENN, which is a round table discussion with CXOs. You can read the summaries when we post on Wikibon and SiliconANGLE but let me summarize. This first comment comes from an assistant VP in retail who says SeviceNow is a key part of their digital transformation. They moved off of BMC remedy two years ago for the global ticketing system. And this person is saying that while the platform is extremely powerful, you got to buy into specific modules to just get one feature that you want. You may not need a lot of the other features, so it starts to get expensive. The other thing this individual is saying is initially, it's a very services heavy project. And so I'll tell you, when you look at the SeviceNow ecosystem the big SIs, the big names, they have big appetites. They love to eat at the trough as I sometimes say, and they want big clients with big budgets. So if you're not one of those top 500 or 700 customers, the big name SIs, you know, they might not be for you. They're not going to pay attention to you. They're going after the big prizes. So what I would suggest is you call up someone like Jason Wojahn of third era, he's the CEO over there and he's got a lot of experience in this space or some more specialized SeviceNow consultancy like them because you're going to get better value for the money. And you're going to get short-term ROI faster with a long-term sustainable ROI as a measurable objective. And I think this last comment sums it up nice, let me to skip over the second one and go just jump to the third one. This basically says the platform is integrated. It's like a mesh. It's not a bunch of stovepipes and cul-de-sacs. Yes it's expensive, but people love it. And like the iPhone, it just works. And their feature pace is accelerating. So pretty strong testimonials, but I want to keep an eye on price transparency any possible backlash there and how the ecosystem evolves. It's something that we called out early on. It's an indicator and SeviceNow needs to continue to invest in that partner network is especially as it builds out its vertical industry practices and expands internationally. Okay, we'll leave it there for now. Remember I publish each week on wikibon.com and siliconangle.com. These episodes they're all available as podcasts. All you got to do is search for breaking analysis podcast. You can always connect with me on Twitter @DVellante or email me @david.vellantesiliconangle.com. Appreciate the comments on LinkedIn. And don't forget to check out etr.plus for all the survey data. This is Dave Vellante for theCUBE insights powered by ETR. Be well, and we'll see you next time. (upbeat music)

>>President. >>Hello everyone and welcome to wien on 2021. My name is Dave Volonte and you're watching the cubes continuous coverage of the event. You know, VM is a company that made its mark riding the virtualization wave, but quite amazingly has continued to extend its product portfolio and catch the other major waves of the industry. Of course, we're talking about cloud backup. SaS data protection was one of the early players there making moves and containers. And this is the VM on power panel with me or Danny Allen, who is the Ceo and Senior vice president of product strategy at VM. Dave Russell is the vice President of enterprise Strategy, of course, said Vin and Rick Vanover, senior director of product strategy at VM. It's great to see you again. Welcome back to the cube. >>Good to be here. >>Well, it had to be here. >>Yeah, let's do it. >>Let's do this. So Danny, you know, we heard you kind of your keynotes and we saw the general sessions and uh sort of diving into the breakouts. But the thing that jumps out to me is this growth rate that you're on. Uh you know, many companies and we've seen this throughout the industry have really struggled, you know, moving from the traditional on prem model to an an A. R. R. Model. Uh they've had challenges doing so the, I mean, you're not a public company, but you're quite transparent and a lot of your numbers 25% a our our growth year of a year in the last quarter, You know, 400,000 plus customers. You're talking about huge numbers of downloads of backup and replication Danny. So what are your big takeaways from the last, You know, 6-12 months? I know it was a strange year obviously, but you guys just keep cranking. >>Yeah, so we're obviously hugely excited by this and it really is a confluence of various things. It's our, it's our partners, it's the channel. Um, it's our customers frankly that that guide us and give us direction on what to do. But I always focus in on the product because I, you know, we run product strategy here, this group and we're very focused on building good products and I would say there's three product areas that are on maximum thrust right now. One is in the data center. So we built a billion dollar business on being the very best in the data center for V sphere, hyper V, um, for Nutanix, HV and as we announced also with red hat virtualization. So data center obviously a huge thrust for us going forward. The second assess Office 3 65 is exploding. We already announced we're protecting 5.8 million users right now with being back up for Office 3 65 and there's a lot of room to grow there. There's 145 million daily users of Microsoft teams. So a lot of room to grow. And then the third areas cloud, we moved over 100 petabytes of data into the public cloud in Q one and there's a lot of opportunity there as well. So those three things are driving the growth, the data center SaAS and cloud >>Davis. I want to get your kind of former analyst perspective on this. Uh you know, I know, you know, it's kind of become cliche but you still got that D. N. A. And I'm gonna tap it. So when you think about and you were following beam, of course very closely during its ascendancy with virtualization. And back then you wouldn't just take your existing, you know, approaches to back up in your processes and just slap them on to virtualization. That that wouldn't have worked. You had to rethink your backup. And it seems like I want to ask you about cloud because people talk about lift and shift and what I hear from customers is, you know, if I just lift and shift to cloud, it's okay, but if I don't have a plan to change my operating model, you know, I don't get the real benefit out of it. And so I would think back up data protection, data management etcetera is a key part of that. So how are you thinking about cloud and the opportunity there? >>Yeah, that's a good point, David. You know, I think the key area right there is it's important to protect the workload of the environment. The way that that environment is naturally is best suited to be protected and also to interact in a way that the administrator doesn't have to rethink, doesn't have to change their process so early on. Um I think it was very successful because the interface is the work experience looked like what an active directory administrator was used to, seeing if they went to go and protect something with me where to go recover an item. Same is true in the cloud, You don't want to just take what's working well in one area and just force it, you know, around round peg into a square hole. This doesn't work well. So you've got to think about the environment and you've got to think about what's gonna be the real use case for getting access to this data. So you want to really tune things and there's obviously commonality involved, but from a workflow perspective, from an application perspective and then a delivery model perspective, Now, when it comes to hybrid cloud multi cloud, it's important to look like that you belong there, not a fish out of water. >>Well, so of course, Danny you were talking to talking about you guys have product first, Right? And so rick your your key product guy here. What's interesting to me is when you look at the history of the technology industry and disruption, it's it's so often that the the incumbent, which you knew now an incumbent, you know, you're not the startup anymore, but the incumbent has challenges riding these these new waves because you've got to serve the existing customer base, but you gotta ride the new momentum as well. So how rick do you approach that from a product standpoint? Because based on the numbers that we see it doesn't you seem to be winning in both the traditional business and the new business. So how do you adapt from a product standpoint? >>Well, Dave, that's a good question. And Danny set it up? Well, it's really the birth of the Wien platform and its relevance in the market. In my 11th year here at Wien, I've had all kinds of conversations. Right. You know, the perception was that, you know, this smb toy for one hyper Advisor those days are long gone. We can check the boxes across the data center and cloud and even cloud native apps. You know, one of the things that my team has done is invest heavily in both people and staff on kubernetes, which aligns to our casting acquisition, which was featured heavily here at V Mon. So I think that being able to have that complete platform conversation Dave has really given us incredible momentum but also credibility with the customers because more than ever, this fundamental promise of having data backed up and being able to drive a recovery for whatever may happen to data nowadays. You know, that's a real emotional, important thing for people and to be able to bring that kind of outcome across the data center, across the cloud, across changes in what they do kubernetes that's really aligned well to our success and you know, I love talking to customers now. It's a heck of a lot easier when you can say yes to so many things and get the technical win. So that kind of drives a lot of the momentum Dave, but it's really the platform. >>So let's talk about the future of it and I want all you guys to chime in here and Danny, you start up, How do you see it? I mean, I always say the last 10 years, the next 10 years ain't gonna be like the last 10 years whether it's in cloud or hybrid et cetera. But so how Danny do you see I. T. In the future of I. T. Where do you see VM fitting in, how does that inform your roadmap, your product strategy? Maybe you could kick that segment off? >>Yeah. I think of the kind of the two past decades that we've gone through starting back in 2000 we had a lot of digital services built for end users and it was built on physical infrastructure and that was fantastic. Obviously we could buy things online, we could order close we could order food, we we could do things interact with end users. The second era about a decade later was based on virtualization. Now that wasn't a benefit so much to the end user is a benefit to the business. The Y because you could put 10 servers on a single physical server and you could be a lot more flexible in terms of delivery. I really think this next era that we're going into is actually based on containers. That's why the cost of acquisition is so strategic to us. Because the unique thing about containers is they're designed for to be consumption friendly. You spin them up, you spin them down, you provision them, you d provisions and they're completely portable. You can move it >>from on >>premises if you're running open shift to e k s a k s G k E. And so I think the next big era that we're going to go through is this movement towards containerized infrastructure. Now, if you ask me who's running that, I still think there's going to be a data center operations team, platform ups is the way that I think about them who run that because who's going to take the call in the middle of the night. But it is interesting that we're going through this transformation and I think we're in the very early stages of this radical transformation to a more consumption based model. Dave. I don't know what you think about that. >>Yeah, I would say something pretty similar Danny. It sounds cliche day valenti, but I take everything back to digital transformation. And the reason I say that is to me, digital transformation is about improving customer intimacy and so that you can deliver goods and services that better resonate and you can deliver them in better time frame. So exactly what Danny said, you know, I think that the siloed approaches of the past where we built very hard in environments and we were willing to take a long time to stand those up and then we have very tight change control. I feel like 2020 sort of a metaphor for where the data center is going to throw all that out the window we're compiling today. We're shipping today and we're going to get experience today and we're going to refine it and do it again tomorrow. But that's the environment we live in. And to Danny's point why containers are so important. That notion of shift left meaning experience things earlier in the cycle. That is going to be the reality of the data center regardless of whether the data center is on prem hybrid cloud, multi cloud or for some of us potentially completely in the cloud. >>So rick when you think about some of your peeps like the backup admit right and how that role is changing in a big discussion in the economy now about the sort of skills gap we got all these jobs and and yet there's still all this unemployment now, you know the debate about the reasons why, but there's a there's a transition enrolls in terms of how people are using products and obviously containers brings that, what what are you seeing when you talk to like a guy called him your peeps? Yeah, it's >>an evolving conversation. Dave the audience, right. It has to be relevant. Uh you know, we were afforded good luxury in that data center wheelhouse that Danny mentioned. So virtualization platform storage, physical servers, that's a pretty good start. But in the software as a service wheelhouse, it's a different persona now, they used to talk to those types of people, there's a little bit of connection, but as we go farther to the cloud, native apps, kubernetes and some of the other SAAS platforms, it is absolutely an audience journey. So I've actually worked really hard on that in my team, right? Everything from what I would say, parachuting into a community, right? And you have to speak their language. Number one reason is just number one outcomes just be present. And if you're in these communities you can find these individuals, you can talk their language, you can resonate with their needs, right? So that's something uh you know, everything from Levin marketing strategy to the community strategy to even just seating products in the market, That's a recipe that beam does really well. So yeah, it's a moving target for sure. >>Dave you were talking about the cliche of digital transformation and I'll say this may be pre Covid, I really felt like it was a cliche, there was a lot of, you know, complacency, I'll call it, but then the force marks the digital change that uh and now we kind of understand if you're not a digital business, you're in trouble. Uh And so my question is how it relates to some of the trends that we've been talking about in terms of cloud containers, We've seen the SAs ification for the better part of a decade now, but specifically as it relates to migration, it's hard for customers to just migrate their application portfolio to the cloud. Uh It's hard to fund it. It takes a long time. It's complex. Um how do you see that cloud migration evolving? Maybe that's where hybrid comes in And again, I'm interested in how you guys think about it and how it affects your strategy. >>Yeah. Well it's a complex answer as you might imagine because 400,000 customers, we take the exact same code. The exact same ice so that I run on my laptop is the exact same being backup and replication image that a major bank protects almost 20,000 machines and a petabytes of data. And so what that means is that you have to look at things on a case by case basis for some of us continuing to operate proprietary systems on prem might be the best choice for a certain workload. But for many of us the Genie is kind of out of the bottle with 2020 we have to move faster. It's less about safety and a lot more about speed and favorable outcome. We'll fix it if it's broken but let's get going. So for organizations struggling with how to move to the cloud, believe it or not, backup and recovery is an excellent way to start to venture into that because you can start to move data backup ISm data movement engine. So we can start to see data there where it makes sense. But rick would be quick to point out we want to offer a safe return. We have instances of where people want to repatriate data back and having a portable data format is key to that Rick. >>Uh yeah, I had a conversation recently with an organization managing cloud sprawl. They decided to consolidate, we're going to use this cloud, so it was removing a presence from one cloud that starts with an A and migrating it to the other cloud that starts with an A. You know, So yeah, we've seen that need for portability repatriation on prem classic example going from on prem apps to software as a service models for critical apps. So data mobility is at the heart of VM and with all the different platforms, kubernetes comes into play as well. It's definitely aligning to the needs that we're seeing in the market for sure. >>So repatriation, I want to stay on that for a second because you're, you're an arms dealer, you don't care if they're in the cloud or on prem and I don't know, maybe you make more money in one or the other, but you're gonna ride whatever waves the market gives you so repatriation to me implies. Or maybe I'm just inferring that somebody's moved to the cloud and they feel like, wow, we've made a mistake, it was too fast, too expensive. It didn't work for us. So now we're gonna bring it back on prem. Is that what you're saying? Are you saying they actually want their data in both both places. As another layer of data protection Danny. I wonder if you could address that. What are you seeing? >>Well, one of the interesting things that we saw recently, Dave Russell actually did the survey on this is that customers will actually build their work laid loads in the cloud with the intent to bring it back on premises. And so that repatriation is real customers actually don't just accidentally fall into it, but they intend to do it. And the thing about being everyone says, hey, we're disrupting the market, we're helping you go through this transformation, we're helping you go forward. Actually take a slightly different view of this. The team gives them the confidence that they can move forward if they want to, but if they don't like it, then they can move back and so we give them the stability through this incredible pace, change of innovation. We're moving forward so so quickly, but we give them the ability to move forward if they want then to recover to repatriate if that's what they need to do in a very effective way. And Dave maybe you can touch on that study because I know that you talked to a lot of customers who do repatriate workloads after moving them to the cloud. >>Yeah, it's kind of funny Dave not in the analyst business right now, but thanks to Danny and our chief marketing Officer, we've got now half a dozen different research surveys that have either just completed or in flight, including the largest in the data protection industry's history. And so the survey that Danny alluded to, what we're finding is people are learning as they're going and in some cases what they thought would happen when they went to the cloud they did not experience. So the net kind of funny slide that we discovered when we asked people, what did you like most about going to the cloud and then what did you like least about going to the cloud? The two lists look very similar. So in some cases people said, oh, it was more stable. In other cases people said no, it was actually unstable. So rick I would suggest that that really depends on the practice that you bring to it. It's like moving from a smaller house to a larger house and hoping that it won't be messy again. Well if you don't change your habits, it's eventually going to end up in the same situation. >>Well, there's still door number three and that's data reuse and analytics. And I found a lot of organizations love the idea of at least manipulating data, running test f scenarios on yesterday's production, cloud workload completely removed from the cloud or even just analytics. I need this file. You know, those types of scenarios are very easy to do today with them. And you know, sometimes those repatriations, those portable recoveries, Sometimes people do that intentionally, but sometimes they have to do it. You know, whether it's fire, flood and blood and you know, oh, I was looks like today we're moving to the cloud because I've lost my data center. Right. Those are scenarios that, that portable data format really allows organizations to do that pretty easily with being >>it's a good discussion because to me it's not repatriation, it has this negative connotation, the zero sum game and it's not Danny what you describe and rick as well. It was kind of an experimentation, a purposeful. We're going to do it in the cloud because we can and it's cheap and low risk to spin it up and then we're gonna move it because we've always thought we're going to have it on prem. So, so you know, there is some zero sum game between the cloud and on prem. Clearly no question about it. But there's also this rising tide lifts all ship. I want to, I want to change the subject to something that's super important and and top of mind it's in the press and it ain't going away and that is cyber and specifically ransomware. I mean, since the solar winds hack and it seems to me that was a new milestone in the capabilities and aggressiveness of the adversary who is very well funded and quite capable. And what we're seeing is this idea of tucking into the supply chain of islands, so called island hopping. You're seeing malware that's self forming and takes different signatures very stealthy. And the big trend that we've seen in the last six months or so is that the bad guys will will lurk and they'll steal all kinds of sensitive data. And then when you have an incident response, they will punish you for responding. And they will say, okay, fine, you want to do that. We're going to hold you ransom. We're gonna encrypt your data. And oh, by the way, we stole this list of positive covid test results with names from your website and we're gonna release it if you don't pay their. I mean, it's like, so you have to be stealthy in your incident response. And this is a huge problem. We're talking about trillions of dollars lost each year in, in in cybercrime. And so, uh, you know, it's again, it's this uh the bad news is good news for companies like you. But how do you help customers deal with this problem? What are you seeing Danny? Maybe you can chime in and others who have thoughts? >>Well we're certainly seeing the rise of cyber like crazy right now and we've had a focus on this for a while because if you think about the last line of defense for customers, especially with ransomware, it is having secure backups. So whether it be, you know, hardened Linux repositories, but making sure that you can store the data, have it offline, have it, have it encrypted immutable. Those are things that we've been focused on for a long while. It's more than that. Um it's detection and monitoring of the environment, which is um certainly that we do with our monitoring tools and then also the secure recovery. The last thing that you want to do of course is bring your backups or bring your data back online only to be hit again. And so we've had a number of capabilities across our portfolio to help in all of these. But I think what's interesting is where it's going, if you think about unleashing a world where we're continuously delivering, I look at things like containers where you have continues delivery and I think every time you run that helm commander, every time you run that terra form command, wouldn't that be a great time to do a backup to capture your data so that you don't have an issue once it goes into production. So I think we're going towards a world where security and the protection against these cyber threats is built into the supply chain rather than doing it on just a time based uh, schedule. And I know rick you're pretty involved on the cyber side as well. Would you agree with that? I >>would. And you know, for organizations that are concerned about ransomware, you know, this is something that is taken very seriously and what Danny explained for those who are familiar with security, he kind of jumped around this, this universally acceptable framework in this cybersecurity framework there, our five functions that are a really good recipe on how you can go about this. And and my advice to IT professionals and decision makers across the board is to really align everything you do to that framework. Backup is a part of it. The security monitoring and user training. All those other things are are areas that that need to really follow that wheel of functions. And my little tip here and this is where I think we can introduce some differentiation is around detection and response. A lot of people think of backup product would shine in both protection and recovery, which it does being does, but especially on response and detection, you know, we have a lot of capabilities that become impact opportunities for organizations to be able to really provide successful outcomes through the other functions. So it's something we've worked on a lot. In fact we've covered here at the event. I'm pretty sure it will be on replay the updated white paper. All those other resources for different levels can definitely guide them through. >>So we follow up to the detection is what analytics that help you identify whatever lateral movement or people go in places they shouldn't go. I mean the hard part is is you know, the bad guys are living off the land, meaning they're using your own tooling to to hack you. So they're not it's not like they're introducing something new that shouldn't be there. They're they're just using making judo moves against you. So so specifically talk a little bit more about your your detection because that's critical. >>Sure. So I'll give you one example imagine we capture some data in the form of a backup. Now we have an existing advice that says, you know what Don't put your backup infrastructure with internet connectivity. Use explicit minimal permissions. And those three things right there and keep it up to date. Those four things right there will really hedge off a lot of the different threat vectors to the back of data, couple that with some of the mutability offline or air gapped capabilities that Danny mentioned and you have an additional level of resiliency that can really ensure that you can drive recovery from an analytic standpoint. We have an api that allows organizations to look into the backup data. Do more aggressive scanning without any exclusions with different tools on a flat file system. You know, the threats can't jump around in memory couple that with secure restore. When you reintroduce things into the environment From a recovery standpoint, you don't want to reintroduce threats. So there's protections, there's there's confidence building steps along the way with them and these are all generally available technologies. So again, I got this white paper, I think we're up to 50 pages now, but it's a very thorough that goes through a couple of those scenarios. But you know, it gets the uh, it gets quickly into things that you wouldn't expect from a backup product. >>Please send me a copy if you, if you don't mind. I this is a huge problem and you guys are global company. I admittedly have a bit of a US bias, but I was interviewing robert Gates one time the former defense secretary and we're talking about cyber war and I said, don't we have the best cyber, can't we let go on the offense? He goes, yeah, we can, but we got the most to lose. So this is really a huge problem for organizations. All right, guys, last question I gotta ask you. So what's life like under, under inside capital of the private equity? What's changed? What's, what's the same? Uh, do you hear from our good friend ratner at all? Give us the update there. >>Yes. Oh, absolutely fantastic. You know, it's interesting. So obviously acquired by insight partners in February of 2020, right, when the pandemic was hitting, but they essentially said light the fuse, keep the engine's going. And we've certainly been doing that. They haven't held us back. We've been hiring like crazy. We're up to, I don't know what the count is now, I think 4600 employees, but um, you know, people think of private equity and they think of cost optimizations and, and optimizing the business, That's not the case here. This is a growth opportunity and it's a growth opportunity simply because of the technology opportunity in front of us to keep, keep the engine's going. So we hear from right near, you know, on and off. But the new executive team at VM is very passionate about driving the success in the industry, keeping abreast of all the technology changes. It's been fantastic. Nothing but good things to say. >>Yes, insight inside partners, their players, we watched them watch their moves and so it's, you know, I heard Bill McDermott, the ceo of service now the other day talking about he called himself the rule of 60 where, you know, I always thought it was even plus growth, you know, add that up. And that's what he was talking about free cash flow. He's sort of changing the definition a little bit but but so what are you guys optimizing for you optimizing for growth? Are you optimising for Alberta? You optimizing for free cash flow? I mean you can't do All three. Right. What how do you think about that? >>Well, we're definitely optimizing for growth. No question. And one of the things that we've actually done in the past 12 months, 18 months is beginning to focus on annual recurring revenue. You see this in our statements, I know we're not public but we talk about the growth in A. R. R. So we're certainly focused on that growth in the annual recovering revenue and that that's really what we tracked too. And it aligns well with the cloud. If you look at the areas where we're investing in cloud native and the cloud and SAAS applications, it's very clear that that recurring revenue model is beneficial. Now We've been lucky, I think we're 13 straight quarters of double-digit growth. And and obviously they don't want to see that dip. They want to see that that growth continue. But we are optimizing on the growth trajectory. >>Okay. And you see you clearly have a 25% growth last quarter in A. R. R. Uh If I recall correctly, the number was evaluation was $5 billion last january. So obviously then, given that strategy, Dave Russell, that says that your tam is a lot bigger than just the traditional backup world. So how do you think about tam? I'll we'll close there >>and uh yeah, I think you look at a couple of different ways. So just in the backup recovery space or backup in replication to paying which one you want to use? You've got a large market there in excess of $8 billion $1 billion dollar ongoing enterprise. Now, if you look at recent i. D. C. Numbers, we grew and I got my handy HP calculator. I like to make sure I got this right. We grew 44.88 times faster than the market average year over year. So let's call that 45 times faster and backup. There's billions more to be made in traditional backup and recovery. However, go back to what we've been talking around digital transformation Danny talking about containers in the environment, deployment models, changing at the heart of backup and recovery where a data capture data management, data movement engine. We envision being able to do that not only for availability but to be able to drive the business board to be able to drive economies of scale faster for our organizations that we serve. I think the trick is continuing to do more of the same Danny mentioned, he knows the view's got lit. We haven't stopped doing anything. In fact, Danny, I think we're doing like 10 times more of everything that we used to be doing prior to the pandemic. >>All right, Danny will give you the last word, bring it home. >>So our goal has always been to be the most trusted provider of backup solutions that deliver modern data protection. And I think folks have seen at demon this year that we're very focused on that modern data protection. Yes, we want to be the best in the data center but we also want to be the best in the next generation, the next generation of I. T. So whether it be sas whether it be cloud VM is very committed to making sure that our customers have the confidence that they need to move forward through this digital transformation era. >>Guys, I miss flying. I mean, I don't miss flying, but I miss hanging with you all. We'll see you. Uh, for sure. Vim on 2022 will be belly to belly, but thanks so much for coming on the the virtual edition and thanks for having us. >>Thank you. >>All right. And thank you for watching everybody. This keeps continuous coverage of the mon 21. The virtual edition. Keep it right there for more great coverage. >>Mm

>>My name is Dave Volonte, and with me are two world class technologists, visionaries and entrepreneurs. And Wa Dodgeville is the he co founded Snowflake, and he's now the president of the product division. And Florian Duetto is the co founder and CEO of Data Aiko. Gentlemen, welcome to the Cube to first timers. Love it. >>Great to be here >>now, Florian you and Ben Wa You have a number of customers in common. And I have said many times on the Cube that you know, the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud is really the scale platform. So is that premise your relevant to you? Do you buy that? And and why do you think snowflake and data ICU make a good match for customers? >>I think that because it's our values that are aligned when it's all about actually today allowing complexity for customers. So you close the gap or the democratizing access to data access to technology. It's not only about data data is important, but it's also about the impact of data. Who can you make the best out of data as fast as possible as easily as possible within an organization. And another value is about just the openness of the platform building the future together? Uh, I think a platform that is not just about the platform but also full ecosystem of partners around it, bringing the level off accessibility and flexibility you need for the 10 years away. >>Yeah, so that's key. But it's not just data. It's turning data into insights. Have been why you came out of the world of very powerful but highly complex databases. And we know we all know that you and the snowflake team you get very high marks for really radically simplifying customers lives. But can you talk specifically about the types of challenges that your customers air using snowflake to solve? >>Yeah, so So the really the challenge, you know, be four. Snowflake. I would say waas really? To put all the data, you know, in one place and run all the computers, all the workloads that you wanted to run, You know, against that data and off course, you know, existing legacy platforms. We're not able to support. You know that level of concurrency, Many workload. You know, we we talk about machine learning that a science that are engendering, you know, that our house big data were closed or running in one place didn't make sense at all. And therefore, you know what customers did is to create silos, silos of data everywhere, you know, with different system having a subset of the data. And of course, now you cannot analyze this data in one place. So, snowflake, we really solve that problem by creating a single, you know, architectural where you can put all the data in the cloud. So it's a really cloud native we really thought about You know how to solve that problem, how to create, you know, leverage, Cloud and the lessee cc off cloud to really put all the die in one place, but at the same time not run all workload at the same place. So each workload that runs in Snowflake that is dedicated, You know, computer resource is to run, and that makes it very Ajai, right? You know, Floyd and talk about, you know, data scientists having to run analysis, so they need you know a lot of compute resources, but only for, you know, a few hours on. Do you know, with snowflake they can run these new work lord at this workload to the system, get the compute resources that they need to run this workload. And when it's over, they can shut down. You know that their system, it will be automatically shut down. Therefore, they would not pay for the resources that they don't use. So it's a very Ajai system where you can do this, analyzes when you need, and you have all the power to run all this workload at the same time. >>Well, it's profound what you guys built to me. I mean, of course, everybody's trying to copy it now. It was like, remember that bringing the notion of bringing compute to the data and the Hadoop days, and I think that that Asai say everybody is sort of following your suit now are trying to Florian I gotta say the first data scientist I ever interviewed on the Cube was amazing. Hilary Mason, right after she started a bit Lee. And, you know, she made data science that sounds so compelling. But data science is hard. So same same question for you. What do you see is the biggest challenges for customers that they're facing with data science. >>The biggest challenge, from my perspective, is that owns you solve the issue of the data. Seidel with snowflake, you don't want to bring another Seidel, which would be a side off skills. Essentially, there is to the talent gap between the talented label of the market, or are it is to actually find recruits trained data scientist on what needs to be done. And so you need actually to simplify the access to technologies such as every organization can make it, whatever the talent, by bridging that gap and to get there, there is a need of actually breaking up the silos. And in a collaborative approach where technologists and business work together and actually put some their hands into those data projects together, >>it makes sense for flooring. Let's stay with you for a minute. If I can your observation spaces, you know it's pretty, pretty global, and and so you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends may be differences between regions or maybe within different industries. What are you seeing? >>Yes. Yeah, definitely. I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to start data in the last few years and no age, a level of maturity where they can invest more and get to the next steps. And it's really rely on the ability of certain medial certain organization actually to have built this long term strategy a few years ago and no start raping up the benefits. >>You know, a decade ago, Florian Hal Varian, we, you know, famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of change that to data scientists and then everybody. All the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What what skills >>do >>you see as critical for the next generation of data science? >>Yeah, it's a good question because I think the first generation of the patient is became the licenses because they could done some pipe and quickly on be flexible. And I think that the skills or the next generation of data sentences will definitely be different. It will be first about being able to speak the language of the business, meaning, oh, you translate data inside predictive modeling all of this into actionable insight or business impact. And it would be about you collaborate with the rest of the business. It's not just a farce. You can build something off fast. You can do a notebook in python or your credit models off themselves. It's about, oh, you actually build this bridge with the business. And obviously those things are important. But we also has become the center of the fact that technology will evolve in the future. There will be new tools and technologies, and they will still need to keep this level of flexibility and get to understand quickly, quickly. What are the next tools they need to use the new languages or whatever to get there. >>As you look back on 2020 what are you thinking? What are you telling people as we head into next year? >>Yeah, I I think it's Zaveri interesting, right? We did this crisis, as has told us that the world really can change from one day to the next. And this has, you know, dramatic, you know, and perform the, you know, aspect. For example, companies all the sudden, you know, So their revenue line, you know, dropping. And they had to do less meat data. Some of the companies was the reverse, right? All the sudden, you know, they were online, like in stock out, for example, and their business, you know, completely, you know, change, you know, from one day to the other. So this GT off, You know, I, you know, adjusting the resource is that you have tow the task a need that can change, you know, using solution like snowflakes, you know, really has that. And we saw, you know, both in in our customers some customers from one day to the to do the next where, you know, growing like big time because they benefited, you know, from from from from co vid and their business benefited, but also, as you know, had to drop. And what is nice with with with cloud, it allows to, you know, I just compute resources toe, you know, to your business needs, you know, and really adjusted, you know, in our, uh, the the other aspect is is understanding what is happening, right? You need to analyze the we saw all these all our customers basically wanted to understand. What is that going to be the impact on my business? How can I adapt? How can I adjust? And and for that, they needed to analyze data. And, of course, a lot of data which are not necessarily data about, you know, their business, but also data from the outside. You know, for example, coffee data, You know, where is the States? You know, what is the impact? You know, geographic impact from covitz, You know, all the time and access to this data is critical. So this is, you know, the promise off the data crowd, right? You know, having one single place where you can put all the data off the world. So our customers, all the Children you know, started to consume the cov data from our that our marketplace and and we had the literally thousands of customers looking at this data analyzing this data, uh, to make good decisions So this agility and and and this, you know, adapt adapting, you know, from from one hour to the next is really critical. And that goes, you know, with data with crowding adjusting, resource is on and that's, you know, doesn't exist on premise. So So So indeed, I think the lesson learned is is we are living in a world which machines changing all the time and we have for understanding We have to adjust and and And that's why cloud, you know, somewhere it's great. >>Excellent. Thank you. You know the kid we like to talk about disruption, of course. Who doesn't on And also, I mean, you look at a I and and the impact that is beginning to have and kind of pre co vid. You look at some of the industries that were getting disrupted by, you know, we talked about digital transformation and you had on the one end of the spectrum industries like publishing which are highly disrupted or taxis. And you could say Okay, well, that's, you know, bits versus Adam, the old Negroponte thing. But then the flip side of that look at financial services that hadn't been dramatically disrupted. Certainly healthcare, which is ripe for disruption Defense. So the number number of industries that really hadn't leaned into digital transformation If it ain't broke, don't fix it. Not on my watch. There was this complacency and then, >>of >>course, co vid broke everything. So, florian, I wonder if you could comment? You know what industry or industries do you think you're gonna be most impacted by data science and what I call machine intelligence or a I in the coming years and decades? >>Honestly, I think it's all of them artist, most of them because for some industries, the impact is very visible because we're talking about brand new products, drones like cars or whatever that are very visible for us. But for others, we are talking about sport from changes in the way you operate as an organization, even if financial industry itself doesn't seems to be so impacted when you look it from the consumer side or the outside. In fact, internally, it's probably impacted just because the way you use data on developer for flexibility, you need the kind off cost gay you can get by leveraging the latest technologies is just enormous, and so it will actually transform the industry that also and overall, I think that 2020 is only a where, from the perspective of a I and analytics, we understood this idea of maturity and resilience, maturity, meaning that when you've got a crisis, you actually need data and ai more than before. You need to actually call the people from data in the room to take better decisions and look for a while and not background. And I think that's a very important learning from 2020 that will tell things about 2021 and the resilience it's like, Yeah, Data Analytics today is a function consuming every industries and is so important that it's something that needs to work. So the infrastructure is to work in frustration in super resilient. So probably not on prime on a fully and prime at some point and the kind of residence where you need to be able to plan for literally anything like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we're just getting to the next step for the analytics. >>I wonder, Benoit, if you have anything to add to that. I mean, I often wonder, you know, winter machine's gonna be able to make better diagnoses than doctors. Some people say already, you know? Well, the financial services traditional banks lose control of payment systems. Uh, you know what's gonna happen to big retail stores? I mean, maybe bring us home with maybe some of your final thoughts. >>Yeah, I would say, you know, I I don't see that as a negative, right? The human being will always be involved very closely, but the machine and the data can really have, you know, see, Coalition, you know, in the data that that would be impossible for for for human being alone, you know, you know, to to discover so So I think it's going to be a compliment, not a replacement on. Do you know everything that has made us you know faster, you know, doesn't mean that that we have less work to do. It means that we can doom or and and we have so much, you know, to do, uh, that that I would not be worried about, You know, the effect off being more efficient and and and better at at our you know, work. And indeed, you know, I fundamentally think that that data, you know, processing off images and doing, you know, I ai on on on these images and discovering, you know, patterns and and potentially flagging, you know, disease, where all year that then it was possible is going toe have a huge impact in in health care, Onda and And as as as Ryan was saying, every you know, every industry is going to be impacted by by that technology. So So, yeah, I'm very optimistic. >>Great guys. I wish we had more time. I gotta leave it there. But so thanks so much for coming on. The Cube was really a pleasure having you.

>> My name is Dave Vellante. And with me are two world-class technologists, visionaries and entrepreneurs. Benoit Dageville, he co-founded Snowflake and he's now the President of the Product Division, and Florian Douetteau is the Co-founder and CEO of Dataiku. Gentlemen, welcome to the cube to first timers, love it. >> Yup, great to be here. >> Now Florian you and Benoit, you have a number of customers in common, and I've said many times on theCUBE, that the first era of cloud was really about infrastructure, making it more agile, taking out costs. And the next generation of innovation, is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake, and Dataiku make a good match for customers? >> I think that because it's our values that aligned, when it gets all about actually today, and knowing complexity of our customers, so you close the gap. Where we need to commoditize the access to data, the access to technology, it's not only about data. Data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible, within an organization. And another value is about just the openness of the platform, building a future together. Having a platform that is not just about the platform, but also for the ecosystem of partners around it, bringing the level of accessibility, and flexibility you need for the 10 years of that. >> Yeah, so that's key, that it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we know we all know that you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so the challenge before snowflake, I would say, was really to put all the data in one place, and run all the computes, all the workloads that you wanted to run against that data. And of course existing legacy platforms were not able to support that level of concurrency, many workload, we talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place didn't make sense at all. And therefore be what customers did this to create silos, silos of data everywhere, with different system, having a subset of the data. And of course now, you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data into cloud. So it's a really cloud native. We really thought about how solve that problem, how to create, leverage cloud, and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake, at its dedicated compute resources to run. And that makes it agile, right? Florian talked about data scientist having to run analysis, so they need a lot of compute resources, but only for a few hours. And with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system, it will automatically shut down. Therefore they would not pay for the resources that they don't use. So it's a very agile system, where you can do this analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. I mean to me, I mean of course everybody's trying to copy it now, it was like, I remember that bringing the notion of bringing compute to the data, in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say the first data scientist I ever interviewed on theCUBE, it was the amazing Hillary Mason, right after she started at Bitly, and she made data sciences sounds so compelling, but data science is a hard. So same question for you, what do you see as the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective, is that once you solve the issue of the data silo, with Snowflake, you don't want to bring another silo, which will be a silo of skills. And essentially, thanks to the talent gap, between the talent available to the markets, or are released to actually find recruits, train data scientists, and what needs to be done. And so you need actually to simplify the access to technologies such as, every organization can make it, whatever the talent, by bridging that gap. And to get there, there's a need of actually backing up the silos. Having a collaborative approach, where technologies and business work together, and actually all puts up their ends into those data projects together. >> It makes sense, Florain let's stay with you for a minute, if I can. Your observation space, it's pretty, pretty global. And so you have a unique perspective on how can companies around the world might be using data, and data science. Are you seeing any trends, maybe differences between regions, or maybe within different industries? What are you seeing? >> Yeah, definitely I do see trends that are not geographic, that much, but much more in terms of maturity of certain industries and certain sectors. Which are, that certain industries invested a lot, in terms of data, data access, ability to store data. As well as experience, and know region level of maturity, where they can invest more, and get to the next steps. And it's really relying on the ability of certain leaders, certain organizations, actually, to have built these long-term data strategy, a few years ago when no stats reaping of the benefits. >> A decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientist. And then everybody, all the statisticians became data scientists, and they got a raise. But data science requires more than just statistics acumen. What skills do you see as critical for the next generation of data science? >> Yeah, it's a great question because I think the first generation of data scientists, became data scientists because they could have done some Python quickly, and be flexible. And I think that the skills of the next generation of data scientists will definitely be different. It will be, first of all, being able to speak the language of the business, meaning how you translates data insight, predictive modeling, all of this into actionable insights of business impact. And it would be about how you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python, or do predictive models of some sorts. It's about how you actually build this bridge with the business, and obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools, new technologies, and they will still need to keep this level of flexibility to understand quickly what are the next tools they need to use a new languages, or whatever to get there. >> As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right? This crises has told us that the world really can change from one day to the next. And this has dramatic and perform the aspects. For example companies all of a sudden, show their revenue line dropping, and they had to do less with data. And some other companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely changed from one day to the other. So this agility of adjusting the resources that you have to do the task, and need that can change, using solution like Snowflake really helps that. Then we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID, and their business benefited. But others had to drop. And what is nice with cloud, it allows you to adjust compute resources to your business needs, and really address it in house. The other aspect is understanding what happening, right? You need to analyze. We saw all our customers basically, wanted to understand what is the going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data which are not necessarily data about their business, but also they are from the outside. For example, COVID data, where is the States, what is the impact, geographic impact on COVID, the time. And access to this data is critical. So this is the premise of the data cloud, right? Having one single place, where you can put all the data of the world. So our customer obviously then, started to consume the COVID data from that our data marketplace. And we had delete already thousand customers looking at this data, analyzing these data, and to make good decisions. So this agility and this, adapting from one hour to the next is really critical. And that goes with data, with cloud, with interesting resources, and that doesn't exist on premise. So indeed I think the lesson learned is we are living in a world, which is changing all the time, and we have to understand it. We have to adjust, and that's why cloud some ways is great. >> Excellent thank you. In theCUBE we like to talk about disruption, of course, who doesn't? And also, I mean, you look at AI, and the impact that it's beginning to have, and kind of pre-COVID. You look at some of the industries that were getting disrupted by, everyone talks about digital transformation. And you had on the one end of the spectrum, industries like publishing, which are highly disrupted, or taxis. And you can say, okay, well that's Bits versus Adam, the old Negroponte thing. But then the flip side of, you say look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is ripe for disruption, defense. So there a number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science, and what I call machine intelligence, or AI, in the coming years and decade? >> Honestly, I think it's all of them, or at least most of them, because for some industries, the impact is very visible, because we have talking about brand new products, drones, flying cars, or whatever that are very visible for us. But for others, we are talking about a part from changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted, when you look at it from the consumer side, or the outside insights in Germany, it's probably impacted just because the way you use data (mumbles) for flexibility you need. Is there kind of the cost gain you can get by leveraging the latest technologies, is just the numbers. And so it's will actually comes from the industry that also. And overall, I think that 2020, is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience, maturity meaning that when you've got to crisis you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions, and look for one and a backlog. And I think that's a very important learning from 2020, that will tell things about 2021. And the resilience, it's like, data analytics today is a function transforming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient, so probably not on prem or not fully on prem, at some point. And the kind of resilience where you need to be able to blend for literally anything, like no hypothesis in terms of BLOs, can be taken for granted. And that's something that is new, and which is just signaling that we are just getting to a next step for data analytics. >> I wonder Benoir if you have anything to add to that. I mean, I often wonder, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? What's going to happen to big retail stores? I mean, maybe bring us home with maybe some of your finals thoughts. >> Yeah, I would say I don't see that as a negative, right? The human being will always be involved very closely, but then the machine, and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So I think it's going to be a compliment not a replacement. And everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do, that I will not be worried about the effect of being more efficient, and bare at our work. And indeed, I fundamentally think that data, processing of images, and doing AI on these images, and discovering patterns, and potentially flagging disease way earlier than it was possible. It is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, guys, I wish we had more time. I've got to leave it there, but so thanks so much for coming on theCUBE. It was really a pleasure having you.

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante as we watch an historic election unfold before our eyes we look back at the early days of the millennium with the memorable presidential race of 2000 that decade of course was defined by 911 which permanently reshaped our thinking and we exited that decade at the tail end of a massive financial crisis only to enter the 2010s with the hope and the momentum of fiscal stimulus a flat globe job growth and very importantly the ascendancy of the cloud cloud computing unquestionably powered the innovation engine over the last 10 years and the pandemic marks a new era where adoption of cloud data and ai have been accelerated by at least two to three years and that's what's going to shape the future of the technology industry and frankly all businesses and organizations hello everyone and welcome to this week's episode of thecube insights powered by etr in this breaking analysis we're going to update you on our latest cloud market share and dig in to some fresh october survey data from our partners over at etr let me start just with a brief summary of the latest action that's going on in cloud now quite interestingly each of the big three cloud players they showed nearly identical year-on-year growth rates in q3 as they did in q2 now we're going to dig into that in a moment but our data suggests that these three companies combined will account for more than 75 billion dollars in infrastructure as a service and platform as a service revenue in 2020 and they're potentially on track to hit 100 billion in 2021. customer survey data indicates that cio's top two infrastructure priorities remain security and cloud migration now that said as we previously reported the cloud it's not immune to the pandemic the remote worker pivot well it's a positive for cloud hasn't completely eradicated certain headwinds now what i mean here is that because the cloud vendors are now so large they're somewhat exposed to the softness in the overall i.t spending climate and also industries that have been hit hardest by the pandemic now would the cloud growth have been better if the pandemic didn't hit we'll never know for sure but our data suggests no covet has definitely been a benefactor to cloud in our view cloud will remain at the center of technological innovation for the foreseeable future the economics of cloud are becoming so compelling that we think the power of the big cloud companies will only increase this decade now importantly we're talking about the costs of running hyper-distributed systems we're not commenting here on what they charge customers that's a different story we believe the cost structure for the hyperscalers is superior to alternative approaches and we believe this advantage will only accelerate over the next several years we also believe that competition is going to continue to drive competitive pricing and innovation all right let's look at our latest market share numbers for the big three this chart shows our estimates of aws azure and the google cloud platform now viewers of this program know that these are is and pass figures and you also know that aws is the only company that provides clean numbers on that sector whereas azure and gcp are estimates that we make based on tidbits of guidance that the companies give us and survey data that we capture and other modeling that we do now as we've said we'll end this year it's about 75 billion in revenue or maybe even a little bit more note that for these three note that we've we've slightly restated some of our earlier estimates for azure to reconcile some differences that we had between constant currency and actual growth we try to keep things in constant currency where possible sorry for that but sometimes that happens azure according to our estimates as we reported last week is now 18 of microsoft's overall revenue number we had it at 19 that last week but when i dug in we made some adjustments so we toned it down a bit aws represents a much smaller percentage of course of amazon's revenues at about 12 percent but it represents 56 percent of amazon's profits gcp on the other hand accounts for less than five percent of google's overall revenue which as we've stated a few weeks ago needs more attention from google but look at the growth rates for these three platforms and the respective size of their is and pass businesses hear all this talk about repatriation i.e that what i mean by that is people go to the cloud but they're unhappy or the bill is too high it's too expensive so then they come back on prem well you just don't see that in the numbers so you gotta be careful when vendor a vendor tries to sell you on that trend i don't buy it except for selective situations now let's bring in some of the etr data and compare the spending momentum for each of the big three you've seen these wheel graphs before they show the breakdown of net score for aws microsoft and google now one note these figures represent these three companies overall within the etr technology taxonomy so for example they don't include amazon's retail business of course but they do include for example microsoft's entire tech portfolio not just the cloud the green portion of the wheel represents increases in spending via new adoptions and increased spending whereas the red sections show decreases via lower spending and defections net score which i've highlighted in the orange is calculated by subtracting the two reds from the two true greens in other words adoptions and increase minus decrease and replacements the takeaway here is these are all pretty strong with aws leading the pack microsoft is exceptionally strong as we pointed out last last week because they're so huge and they still have net scores comparable to aws which is a pure play gcp is a laggard and is showing softness in the data despite a sanguine outlook that we had back in 2019 based on survey data i don't know perhaps google's smaller presence muted their customers ability to take advantage of the platform the thinking there is the customers maybe needed to pivot to the cloud so quickly and aws and azure were the incumbents and that was maybe the most expedient path hence the higher increases in the spend more category but you do see gcp um they had 13 new adoptions which is pretty good so we'll keep looking at that regardless again these are not pure play cloud comparisons but they give a good indication of spending momentum i'd also note that all three show very low defections well each is showing solid increases in new adoptions especially google as i mentioned so that's kind of interesting to see but again google much much smaller you would expect that now i want to turn our attention to one of the hottest areas in cloud which is serverless and this is a pure play comparison so serverless let me start there it's a strange term because it's not really accurate but it's stuck serverless computing is a model where the cloud platform dynamically delivers services as the application requires so so you don't have to configure the compute and the containers for example rather when an application needs resources it goes and gets them and you only pay for when the services are actually invoked and in use so it's really good for workloads that spin up and spin down very frequently it kind of reminds me in concept anyway of the component tree that we saw in the days of soa if you remember that services oriented architecture but now this is cloud it's cloud native it's a whole new world and it's increasingly a popular model and as we'll show in a moment there's a lot of spending momentum in this area but before we do that i want to share some comments made by andy jassy a while back about serverless take a listen it's a good question and you know i really the comment i made was really about um directionally what amazon would do you know in this in the very earliest days of aws jeff used to say a lot if i were starting amazon today i'd have built it on top of aws we didn't have all the capability and all the functionality at that very moment but he knew what was coming and he saw what people were still able to accomplish even with where the services were at that point i think the same thing is true here with lambda which is i think if amazon were starting today it's a given they would build it on the cloud and i think with a lot of the applications that comprise amazon's consumer business we would build those on on our serverless capabilities now now lambda of course jesse referring to lambda that's amazon's serverless offering and if you think about amazon's retail business and take for example the frequent spin up and spin down of resources for something like black monday serverless would be a much more cost effective approach same for a managed data warehouse service for example where you know you don't want to pay for the compute if it's idle the app just calls for the compute when it's needed so it's a very popular model and it's got increased momentum today and you see that in this slide it shows the net score breakdown for serverless for azure aws is lambda which is again is their serverless offering and google cloud functions again you're shipping functions to the application that's why it's called functions look at the net scores azure functions nearly 70 aws at 65 google again lagging and that's a bit of a concern because this is a really really hot space all right let's move on and look at the competitive landscape as we like to do often and update you on that this xy graph is one of our favorites and it shows net score or spending momentum on the vertical axis and market share on the horizontal market share is a measure of pervasiveness in the data set in the upper right you also see a table that ranks each vendor my net score and it includes the shared n in other words the number of mentions in this sector for each vendor now you can you can see up top in the middle i've selected on the cloud computing category so this represents only the cloud businesses for each of these players there's a little bit of nuance here and that we've selected on microsoft azure there's a category in the etr taxonomy for that and we're comparing that with aws overall so there's there are things in the aws overall number that fit into the other parts of the taxonomy like maybe ai collaboration etc whereas azures and gcp are just the cloud segments so i i know it's a bit strange because aws is all cloud but don't get caught up in the taxonomical nuance the point is it's good to be azure in aws it's shown there when you look at the upper right of the chart here they stand out and they stand alone in cloud leadership google cloud is they have nice elevated levels but they're much much smaller they don't have the presence in the market now look at that hybrid cloud zone emerging we've talked about this sometimes in the past and and i want to call it vmware cloud on aws red hat open shift and vmware cloud itself like vmware cloud foundation and their other cloud services all of these appear to be gaining traction and you can see in the number of occurrences in the upper right that shared end that i talked about we're starting to see real numbers that are meaningful in this space vmware cloud on aws for example has a net score of 53 percent with 116 accounts within that total respondent sample that you see there in the middle left of 1438 that's how many cios and technology buyers responded to the etr survey in october you look at open shift at 45 net score and that's with 82 accounts now openshift is in beta with what looked to be some really strong offerings on aws and you can see for context i've added dell emc's cloud offerings hpe's cloud offerings and the oracle cloud and ibm cloud and also rackspace dell actually pretty strong with a net score of 20 and 185 shared accounts much much higher than dell overall which is kind of in the red zone oracle ibm you see those rackspace you know organizing not killing it rackspace is kind of in the big negative so that's a concern but anyway we'd like for these guys we'd like to see the data match the marketing rhetoric for the the guys that are in the red and look alibaba is starting to to show up in the server there's only 26 shared ends but we thought we'd we'd put it in there those three key points again aws and microsoft keep on trucking google needs to do better hybrid is becoming real and that bodes well for multi-cloud and the legacy on-prem guys they got a lot of work to do they're under a lot of pressure the pivot to cloud has not been easy for them uh and it's still a case where they're i've talked about this a lot they're they're declines in their on-premises offerings they're not being offset by the new stuff the cloud momentum all right i want to close out by sharing some of the conversations and thoughts that we've had in the community around sas and its impact on cloud we really have been focusing on ias and pass of the sas layer obviously up the stack so let me first share that there's a lot of talk around and has been for years about aws they're slowing growth rates and whether or not they'll have to enter the sas market to expand their total available market and i've said consistently while i never say never about aws i don't think so at least not yet this chart plots the big three cloud players note aws is a bigger piece of this pie now that i've turned off the cloud computing filter and i know more nuances but the data wonks will will find you know see this and they'll ask me about it this is all of aws portfolio and again it's only the microsoft azure portfolio so you see it aws now overtakes azure on the x-axis i.e market share now we've plotted some of the major sas vendors and you can see servicenow and salesforce both very large and they have really strong spending momentum and servicenow's you know pushing 100 billion dollars in market value they've surpassed workday quite some time ago workday's got less presence but they've got really really solid net score and i got to say i'm impressed with sap despite some of the earnings challenges that they've been having they're right up there with splunk and tableau splunk has softened in recent surveys and i've i've also plotted in there netsuite and oracle fusion which are just okay and that is i think for now anyway aws is going to position as the best place and the most friendly and highest quality cloud in which to run your sas for example workday runs on aws aws is salesforce's preferred infrastructure platform so my premise here is just like retail companies might want not want to run on aws a number of sas companies that compete with microsoft they might think twice about running on azure so aws would be better off for now trying to attract those sas players and drive their services and sticking to infrastructure and the pass layer snowflake is actually kind of interesting and i've added them for context because their netscore is always kind of a bellwether it's really off the charts and they're an isv running on the cloud they're different from some of the other sas players and the snowflake is a database okay and most of snowflake's business runs on aws and aws competes with snowflake with redshift but aws has the best cloud and drives a lot of business for snowflake and vice versa so it's kind of interesting snow snowflake to redshift and a much smaller example is kind of like netflix to amazon prime video to compete they both thrive so i think aws is going to continue to grow by attracting sas players as the preferred platform and they'll also attract developers and try to disrupt sas players like servicenow which runs on its own cloud i remember years ago david floyer and i said that servicenow was it was awesome but at some point its infrastructure cost structure its infrastructure cost structure is going to be less competitive than those companies that are running on hyperscale clouds certainly the hyperscale clouds themselves and servicenow they have this multi-instance architecture which just can't easily port over to the cloud but it can charge a lot which it does now at some point some sharp developers are going to look at all this and say whoa see that service now i can build this for less and they'll attack servicenow and their seat base license model maybe with the consumption pricing model and a platform that's perhaps or a set of services that are perhaps less expensive you're seeing this to a you know a certain degree with like elastic inside the application performance management space so there's some some things to watch there but there are those who firmly believe that aws will and must enter the sas space directly we talked last week about how beneficial microsoft's application business is for azure and what a flywheel that is but for me i think we're not there yet let's give it some time i think maybe four to five years before aws may even start to think about filling some of the space up the stack now maybe they'll find some unique opportunities to do that for instance at the edge but i think that's way off okay so bottom line it's good to be in tech these days it's even better to be in the cloud and it's best if you're aws and microsoft and i don't see that changing for a while now remember these episodes are all available as podcasts wherever you listen i publish each week on wikibon.com and siliconangle.com you can get in touch with me through email it's david at siliconangle.com feel free to dm me on twitter at d vallante i post on linkedin love your comments there thank you and don't forget to check out etr plus for all the survey action thanks for watching this episode of thecube insights powered by etr this is dave vellante stay safe stay sane and we'll see you next time you

>> Hello everyone, welcome back to theCUBE'S wall to wall coverage of the Snowflake Data Cloud Summit. My name is Dave Vellante and with me are two world-class technologists, visionaries, and entrepreneurs. Benoit Dageville is the, he co-founded Snowflake. And he's now the president of the Product division and Florian Douetteau is the co-founder and CEO of Dataiku. Gentlemen, welcome to theCUBE, two first timers, love it. >> Great time to be here. >> Now Florian, you and Benoit, you have a number of customers in common. And I've said many times on theCUBE that, the first era of cloud was really about infrastructure, making it more agile taking out costs. And the next generation of innovation is really coming from the application of machine intelligence to data with the cloud, is really the scale platform. So is that premise relevant to you, do you buy that? And why do you think Snowflake and Dataiku make a good match for customers? >> I think that because it's our values that align. When it gets all about actually today, and knowing complexity per customer, so you close the gap or we need to commoditize the access to data, the access to technology, it's not only about data, data is important, but it's also about the impacts of data. How can you make the best out of data as fast as possible, as easily as possible within an organization? And another value is about just the openness of the platform, building a future together. I think a platform that is not just about the platform but also for the ecosystem of partners around it, bringing the little bit of accessibility and flexibility, you need for the 10 years of that. >> Yes, so that's key, but it's not just data. It's turning data into insights. Now Benoit, you came out of the world of very powerful, but highly complex databases. And we all know that, you and the Snowflake team, you get very high marks for really radically simplifying customers' lives. But can you talk specifically about the types of challenges that your customers are using Snowflake to solve? >> Yeah, so really the challenge before Snowflake, I would say, was really to put all the data, in one place and run all the computes, all the workloads that you wanted to run, against that data. And of course, existing legacy platforms were not able to support that level of concurrency, many workload. We talk about machine learning, data science, data engineering, data warehouse, big data workloads, all running in one place, didn't make sense at all. And therefore, what customers did, is to create silos, silos of data everywhere, with different systems having a subset of the data. And of course now you cannot analyze this data in one place. So Snowflake, we really solved that problem by creating a single architecture where you can put all the data in the cloud. So it's a really cloud native. We really thought about how to solve that problem, how to create leverage cloud and the elasticity of cloud to really put all the data in one place. But at the same time, not run all workload at the same place. So each workload that runs in Snowflake at least dedicate compute resources to run. And that makes it very agile, right. Florian talked about data scientist having to run analysis. So they need a lot of compute resources, but only for few hours and with Snowflake, they can run these new workload, add this workload to the system, get the compute resources that they need to run this workload. And then when it's over, they can shut down their system. It will automatically shut down. Therefore they would not pay for the resources that they don't choose. So it's a very agile system, where you can do these analysis when you need, and you have all the power to run all these workload at the same time. >> Well, it's profound what you guys built. To me, I mean, because everybody's trying to copy it now. It's like, I remember the notion of bringing compute to the data in the Hadoop days. And I think that, as I say, everybody is sort of following your suit now or trying to. Florian, I got to say, the first data scientist I ever interviewed on theCUBE was the amazing Hilary Mason, right after she started at Bitly. And she made data science sounds so compelling, but data science is hard. So same question for you. What do you see is the biggest challenges for customers that they're facing with data science? >> The biggest challenge from my perspective is that once you solve the issue of the data silo with Snowflake, you don't want to bring another silo, which would be a silo of skills. And essentially, thanks to that talent gap between the talent and labor of the markets, or how it is to actually find, recruit and train data scientists and what needs to be done. And so you need actually to simplify the access to technology such as every organization can make it, whatever the talents by bridging that gap. And to get there, there is a need of actually breaking up the silos. I think a collaborative approach, where technologies and business work together and actually all put some of their ends into those data projects together. >> Yeah, it makes sense. So Florian, Let's stay with you for a minute, if I can. Your observation spaces, is pretty, pretty global. And so, you have a unique perspective on how companies around the world might be using data and data science. Are you seeing any trends, maybe differences between regions or maybe within different industries? What are you seeing? >> Yep. Yeah, definitely, I do see trends that are not geographic that much, but much more in terms of maturity of certain industries and certain sectors, which are that certain industries invested a lot in terms of data, data access, ability to store data as well as few years and know each level of maturity where they can invest more and get to the next steps. And it's really reliant to reach out to certain details, certain organization, actually to have built this longterm data strategy a few years ago, and no stocks ripping off the benefits. >> You know, a decade ago, Florian, Hal Varian famously said that the sexy job in the next 10 years will be statisticians. And then everybody sort of changed that to data scientists. And then everybody, all the statisticians became data scientists and they got a raise. But data science requires more than just statistics acumen. What skills do you see is critical for the next generation of data science? >> Yeah, it's a good question because I think the first generation of data scientists became better scientists because they could learn some Python quickly and be flexible. And I think that skills of the next generation of data scientists will definitely be different. It will be first about being able to speak the language of the business, meaning all you translate data insight, predictive modeling, all of this into actionable insights or business impact. And it will be about who you collaborate with the rest of the business. It's not just how fast you can build something, how fast you can do a notebook in Python or do quantity models of some sorts. It's about how you actually build this bridge with the business. And obviously those things are important, but we also must be cognizant of the fact that technology will evolve in the future. There will be new tools in technologies, and they will still need to get this level of flexibility and get to understand quickly what are the next tools, they need to use or new languages or whatever to get there. >> Thank you for that. Benoit, let's come back to you. This year has been tumultuous to say the least for everyone, but it's a good time to be in tech, ironically. And if you're in cloud, it's even better. But you look at Snowflake and Dataiku, you guys had done well, despite the economic uncertainty and the challenges of the pandemic. As you look back on 2020, what are you thinking? What are you telling people as we head into next year? >> Yeah, I think it's very interesting, right. We, this crisis has told us that the world really can change from one day to the next. And this has dramatic and profound aspects. For example, companies all of a sudden, saw their revenue line dropping and they had to do less with data. And some of the companies was the reverse, right? All of a sudden, they were online like Instacart, for example, and their business completely change from one day to the other. So this agility of adjusting the resources that you have to do the task, a need that can change, using solution like Snowflake, really helps that. And we saw both in our customers. Some customers from one day to the next, were growing like big time, because they benefited from COVID and their business benefited, but also, as you know, had to drop and what is nice with cloud, it allows to adjust compute resources to your business needs and really address it in-house. The other aspect is understanding what is happening, right? You need to analyze. So we saw all our customers basically wanted to understand, what is it going to be the impact on my business? How can I adapt? How can I adjust? And for that, they needed to analyze data. And of course, a lot of data, which are not necessarily data about their business, but also data from the outside. For example, COVID data. Where is the state, what is the impact, geographic impact on COVID all the time. And access to this data is critical. So this is the promise of the data cloud, right? Having one single place where you can put all the data of the world. So, our customers all of a sudden, started to consume the COVID data from our data marketplace. And we have the unit already thousands of customers looking at this data, analyzing this data to make good decisions. So this agility and this adapting from one hour to the next is really critical and that goes with data, with cloud, more interesting resources and that's doesn't exist on premise. So, indeed I think the lesson learned is, we are living in a world which is changing all the time, and we have to understand it. We have to adjust and that's why cloud, some way is great. >> Excellent, thank you. You know, in theCUBE, we like to talk about disruption, of course, who doesn't. And also, I mean, you look at AI and the impact that it's beginning to have and kind of pre-COVID, you look at some of the industries that were getting disrupted by, everybody talks about digital transformation and you had on the one end of the spectrum, industries like publishing, which are highly disrupted or taxis, and you can say, "Okay well, that's Bits versus Adam, the old Negroponte thing." But then the flip side of this, it says, "Look at financial services that hadn't been dramatically disrupted, certainly healthcare, which is right for disruption, defense." So the more the number of industries that really hadn't leaned into digital transformation, if it ain't broke, don't fix it. Not on my watch. There was this complacency. And then of course COVID broke everything. So Florian, I wonder if you could comment, what industry or industries do you think are going to be most impacted by data science and what I call machine intelligence or AI in the coming years and decades? >> Honestly, I think it's all of them, or at least most of them. Because for some industries, the impact is very visible because we are talking about brand new products, drones, flying cars, or whatever is that are very visible for us. But for others, we are talking about spectrum changes in the way you operate as an organization. Even if financial industry itself doesn't seem to be so impacted when you look at it from the consumer side or the outside. In fact internally, it's probably impacted just because of the way you use data to develop for flexibility you need, is there kind of a cost gain you can get by leveraging the latest technologies, is just enormous. And so it will, actually comes from the industry, that also. And overall, I think that 2020 is a year where, from the perspective of AI and analytics, we understood this idea of maturity and resilience. Maturity, meaning that when you've got a crisis, you actually need data and AI more than before, you need to actually call the people from data in the room to take better decisions and look forward and not backward. And I think that's a very important learning from 2020 that will tell things about 2021. And resilience, it's like, yeah, data analytics today is a function consuming every industries, and is so important that it's something that needs to work. So the infrastructure needs to work, the infrastructure needs to be super resilient. So probably not on trend and not fully on trend, at some point and the kind of residence where you need to be able to plan for literally anything. like no hypothesis in terms of behaviors can be taken for granted. And that's something that is new and which is just signaling that we are just getting into a next step for all data analytics. >> I wonder Benoit, if you have anything to add to that, I mean, I often wonder, you know, when are machines going to be able to make better diagnoses than doctors, some people say already. Will the financial services, traditional banks lose control of payment systems? You know, what's going to happen to big retail stores? I mean, may be bring us home with maybe some of your final thoughts. >> Yeah, I would say, I don't see that as a negative, right? The human being will always be involved very closely, but then the machine and the data can really help, see correlation in the data that would be impossible for human being alone to discover. So, I think it's going to be a compliment, not a replacement and everything that has made us faster, doesn't mean that we have less work to do. It means that we can do more. And we have so much to do. That I would not be worried about the effect of being more efficient and better at our work. And indeed, I fundamentally think that, data, processing of images and doing AI on these images and discovering patterns and potentially flagging disease, way earlier than it was possible, it is going to have a huge impact in health care. And as Florian was saying, every industry is going to be impacted by that technology. So, yeah, I'm very optimistic. >> Great, Guys, I wish we had more time. We got to leave it there but so thanks so much for coming on theCUBE. It was really a pleasure having you. >> [Benoit & Florian] Thank you. >> You're welcome but keep it right there, everybody. We'll back with our next guest, right after this short break. You're watching theCUBE.

>> Narrator: From around the globe. It's theCUBE with digital coverage of Exascale Day. Made possible by Hewlett Packard Enterprise. >> Welcome everyone to theCUBE's celebration of Exascale Day. Dr. Mark Fernandez is here. He's the HPC technology officer for the Americas at Hewlett Packard enterprise. And he's a developer of the spaceborne computer, which we're going to talk about today. Mark, welcome. It's great to see you. >> Great to be here. Thanks for having me. >> You're very welcome. So let's start with Exascale Day. It's on 10 18, of course, which is 10 to the power of 18. That's a one followed by 18 zeros. I joke all the time. It takes six commas to write out that number. (Mark laughing) But Mark, why don't we start? What's the significance of that number? >> So it's a very large number. And in general, we've been marking the progress of our computational capabilities in thousands. So exascale is a thousand times faster than where we are today. We're in an era today called the petaflop era which is 10 to the 15th. And prior to that, we were in the teraflop era, which is 10 to the 12th. I can kind of understand a 10 to the 12th and I kind of can discuss that with folks 'cause that's a trillion of something. And we know a lot of things that are in trillions, like our national debt, for example. (Dave laughing) But a billion, billion is an exascale and it will give us a thousand times more computational capability than we have in general today. >> Yeah, so when you think about going from terascale to petascale to exascale I mean, we're not talking about orders of magnitude, we're talking about a much more substantial improvement. And that's part of the reason why it's sort of takes so long to achieve these milestones. I mean, it kind of started back in the sixties and seventies and then... >> Yeah. >> We've been in the petascale now for more than a decade if I think I'm correct. >> Yeah, correct. We got there in 2007. And each of these increments is an extra comma, that's the way to remember it. So we want to add an extra comma and get to the exascale era. So yeah, like you say, we entered the current petaflop scale in 2007. Before that was the terascale, teraflop era and it was in 1997. So it took us 10 years to get that far, but it's taken us, going to take us 13 or 14 years to get to the next one. >> And we say flops, we're talking about floating point operations. And we're talking about the number of calculations that can be done in a second. I mean, talk about not being able to get your head around it, right? Is that's what talking about here? >> Correct scientists, engineers, weather forecasters, others use real numbers and real math. And that's how you want to rank those performance is based upon those real numbers times each other. And so that's why they're floating point numbers. >> When I think about supercomputers, I can't help but remember whom I consider the father of supercomputing Seymour Cray. Cray of course, is a company that Hewlett Packard Enterprise acquired. And he was kind of an eclectic fellow. I mean, maybe that's unfair but he was an interesting dude. But very committed to his goal of really building the world's fastest computers. When you look at back on the industry, how do you think about its developments over the years? >> So one of the events that stands out in my mind is I was working for the Naval Research Lab outside Stennis Space Center in Mississippi. And we were doing weather modeling. And we got a Cray supercomputer. And there was a party when we were able to run a two week prediction in under two weeks. So the scientists and engineers had the math to solve the problem, but the current computers would take longer than just sitting and waiting and looking out the window to see what the weather was like. So when we can make a two week prediction in under two weeks, there was a celebration. And that was in the eighties, early nineties. And so now you see that we get weather predictions in eight hours, four hours and your morning folks will get you down to an hour. >> I mean, if you think about the history of super computing it's really striking to consider the challenges in the efforts as we were just talking about, I mean, decade plus to get to the next level. And you see this coming to fruition now, and we're saying exascale likely 2021. So what are some of the innovations in science, in medicine or other areas you mentioned weather that'll be introduced as exascale computing is ushered in, what should people expect? >> So we kind of alluded to one and weather affects everybody, everywhere. So we can get better weather predictions, which help everybody every morning before you get ready to go to work or travel or et cetera. And again, storm predictions, hurricane predictions, flood predictions, the forest fire predictions, those type things affect everybody, everyday. Those will get improved with exascale. In terms of medicine, we're able to take, excuse me, we're able to take genetic information and attempt to map that to more drugs quicker than we have in the past. So we'll be able to have drug discovery happening much faster with an exascale system out there. And to some extent that's happening now with COVID and all the work that we're doing now. And we realize that we're struggling with these current computers to find these solutions as fast as everyone wants them. And exascale computers will help us get there much faster in the future in terms of medicine. >> Well, and of course, as you apply machine intelligence and AI and machine learning to the applications running on these supercomputers, that just takes it to another level. I mean, people used to joke about you can't predict the weather and clearly we've seen that get much, much better. Now it's going to be interesting to see with climate change. That's another wildcard variable but I'm assuming the scientists are taking that into consideration. I mean, actually been pretty accurate about the impacts of climate change, haven't they? >> Yeah, absolutely. And the climate change models will get better with exascale computers too. And hopefully we'll be able to build a confidence in the public and the politicians in those results with these better, more powerful computers. >> Yeah let's hope so. Now let's talk about the spaceborne computer and your involvement in that project. Your original spaceborne computer it went up on a SpaceX reusable rocket. Destination of course, was the international space station. Okay, so what was the genesis of that project and what was the outcome? So we were approached by a long time customer NASA Ames. And NASA Ames says its mission is to model rocket launches and space missions and return to earth. And they had the foresight to realize that their supercomputers here on earth, could not do that mission when we got to Mars. And so they wanted to plan ahead and they said, "Can you take a small part of our supercomputer today and just prove that it can work in space? And if it can't figure out what we need to do to make it work, et cetera." So that's what we did. We took identical hardware, that's present at NASA Ames. We put it on a SpaceX rocket no special preparations for it in terms of hardware or anything of that sort, no special hardening, because we want to take the latest technology just before we head to Mars with us. I tell people you wouldn't want to get in the rocket headed to Mars with a flip phone. You want to take the latest iPhone, right? And all of the computers on board, current spacecrafts are about the 2007 era that we were talking about, in that era. So we want to take something new with us. We got the spaceone computer on board. It was installed in the ceiling because in space, there's no gravity. And you can put computers in the ceiling. And we immediately made a computer run. And we produced a trillion calculations a second which got us into the teraflop range. The first teraflop in space was pretty exciting. >> Well, that's awesome. I mean, so this is the ultimate example of edge computing. >> Yes. You mentioned you wanted to see if it could work and it sounds like it did. I mean, there was obviously a long elapse time to get it up and running 'cause you have to get it up there. But it sounds like once you did, it was up and running very quickly so it did work. But what were some of the challenges that you encountered maybe some of the learnings in terms of getting it up and running? >> So it's really fascinating. Astronauts are really cool people but they're not computer scientists, right? So they see a cord, they see a place to plug it in, they plug it in and of course we're watching live on the video and you plugged it in the wrong spot. So (laughs) Mr. Astronaut, can we back up and follow the procedure more carefully and get this thing plugged in carefully. They're not computer technicians used to installing a supercomputer. So we were able to get the system packaged for the shake, rattle and roll and G-forces of launch in the SpaceX. We were able to give astronaut instructions on how to install it and get it going. And we were able to operate it here from earth and get some pretty exciting results. >> So our supercomputers are so easy to install even an astronaut can do it. I don't know. >> That's right. (both laughing) Here on earth we have what we call a customer replaceable units. And we had to replace a component. And we looked at our instructions that are tried and true here on earth for average Joe, a customer to do that and realized without gravity, we're going to have to update this procedure. And so we renamed it an astronaut replaceable unit and it worked just fine. >> Yeah, you can't really send an SE out to space to fix it, can you? >> No sir. (Dave laughing) You have to have very careful instructions for these guys but they're great. It worked out wonderfully. >> That's awesome. Let's talk about spaceborne two. Now that's on schedule to go back to the ISS next year. What are you trying to accomplish this time? >> So in retrospect, spaceborne one was a proof of concept. Can we package it up to fit on SpaceX? Can we get the astronauts to install it? And can we operate it from earth? And if so, how long will it last? And do we get the right answers? 100% mission success on that. Now spaceborne two is, we're going to release it to the community of scientists, engineers and space explorers and say, "Hey this thing is rock solid, it's proven. Come use it to improve your edge computing." We'd like to preserve the network downlink bandwidth for all that imagery, all that genetic data, all that other data and process it on the edge as the whole world is moving to now. Don't move the data, let's compute at the edge and that's what we're going to do with spaceborne two. And so what's your expectation for how long the project is going to last? What does success look like in your mind? So spaceborne one was given a one year mission just to see if we could do it but the idea then was planted it's going to take about three years to get to Mars and back. So if you're successful, let's see if this computer can last three years. And so we're going up February 1st, if we go on schedule and we'll be up two to three years and as long as it works, we'll keep computing and computing on the edge. >> That's amazing. I mean, I feel like, when I started the industry, it was almost like there was a renaissance in supercomputing. You certainly had Cray and you had all these other companies, you remember thinking machines and convex spun out tried to do a mini supercomputer. And you had, really a lot of venture capital and then things got quiet for a while. I feel like now with all this big data and AI, we're seeing in all the use cases that you talked about, we're seeing another renaissance in supercomputing. I wonder if you could give us your final thoughts. >> Yeah, absolutely. So we've got the generic like you said, floating point operations. We've now got specialized image processing processors and we have specialized graphics processing units, GPUs. So all of the scientists and engineers are looking at these specialized components and bringing them together to solve their missions at the edge faster than ever before. So there's heterogeneity of computing is coming together to make humanity a better place. And how are you going to celebrate Exascale Day? You got to special cocktail you going to shake up or what are you going to do? It's five o'clock somewhere on 10 18, and I'm a Parrothead fan. So I'll probably have a margarita. There you go all right. Well Mark, thanks so much for sharing your thoughts on Exascale Day. Congratulations on your next project, the spaceborne two. Really appreciate you coming to theCUBE. Thank you very much I've enjoyed it. All right, you're really welcome. And thank you for watching everybody. Keep it right there. This is Dave Vellante for thecUBE. We're celebrating Exascale Day. We'll be right back. (upbeat music)

>>welcome. My name is Ben Bennett. I am the director of HPC Strategic programs here at Hewlett Packard Enterprise. It is my great pleasure and honor to be talking to Professor Mark Parsons from the Edinburgh Parallel Computing Center. And we're gonna talk a little about exa scale. What? It means we're gonna talk less about the technology on Maura about the science, the requirements on the need for exa scale. Uh, rather than a deep dive into the enabling technologies. Mark. Welcome. >>I then thanks very much for inviting me to tell me >>complete pleasure. Um, so I'd like to kick off with, I suppose. Quite an interesting look back. You and I are both of a certain age 25 plus, Onda. We've seen these milestones. Uh, I suppose that the S I milestones of high performance computing's come and go, you know, from a gig a flop back in 1987 teraflop in 97 a petaflop in 2000 and eight. But we seem to be taking longer in getting to an ex a flop. Um, so I'd like your thoughts. Why is why is an extra flop taking so long? >>So I think that's a very interesting question because I started my career in parallel computing in 1989. I'm gonna join in. IPCC was set up then. You know, we're 30 years old this year in 1990 on Do you know the fastest computer we have them is 800 mega flops just under a getting flogged. So in my career, we've gone already. When we reached the better scale, we'd already gone pretty much a million times faster on, you know, the step from a tariff block to a block scale system really didn't feel particularly difficult. Um, on yet the step from A from a petaflop PETA scale system. To an extent, block is a really, really big challenge. And I think it's really actually related to what's happened with computer processes over the last decade, where, individually, you know, approached the core, Like on your laptop. Whoever hasn't got much faster, we've just got more often So the perception of more speed, but actually just being delivered by more course. And as you go down that approach, you know what happens in the supercomputing world as well. We've gone, uh, in 2010 I think we had systems that were, you know, a few 1000 cores. Our main national service in the UK for the last eight years has had 118,000 cores. But looking at the X scale we're looking at, you know, four or five million cores on taming that level of parallelism is the real challenge. And that's why it's taking an enormous and time to, uh, deliver these systems. That is not just on the hardware front. You know, vendors like HP have to deliver world beating technology and it's hard, hard. But then there's also the challenge to the users. How do they get the codes to work in the face of that much parallelism? >>If you look at what the the complexity is delivering an annex a flop. Andi, you could have bought an extra flop three or four years ago. You couldn't have housed it. You couldn't have powered it. You couldn't have afforded it on, do you? Couldn't program it. But you still you could have You could have bought one. We should have been so lucky to be unable to supply it. Um, the software, um I think from our standpoint, is is looking like where we're doing mawr enabling with our customers. You sell them a machine on, then the the need then to do collaboration specifically seems mawr and Maura around the software. Um, so it's It's gonna be relatively easy to get one x a flop using limb pack, but but that's not extra scale. So what do you think? On exa scale machine versus an X? A flop machine means to the people like yourself to your users, the scientists and industry. What is an ex? A flop versus >>an exa scale? So I think, you know, supercomputing moves forward by setting itself challenges. And when you when you look at all of the excess scale programs worldwide that are trying to deliver systems that can do an X a lot form or it's actually very arbitrary challenge. You know, we set ourselves a PETA scale challenge delivering a petaflop somebody manage that, Andi. But you know, the world moves forward by setting itself challenges e think you know, we use quite arbitrary definition of what we mean is well by an exit block. So, you know, in your in my world, um, we either way, first of all, see ah flop is a computation, so multiply or it's an ad or whatever on we tend. Thio, look at that is using very high precision numbers or 64 bit numbers on Do you know, we then say, Well, you've got to do the next block. You've got to do a billion billion of those calculations every second. No, a some of the last arbitrary target Now you know today from HPD Aiken by my assistant and will do a billion billion calculations per second. And they will either do that as a theoretical peak, which would be almost unattainable, or using benchmarks that stressed the system on demonstrate a relaxing law. But again, those benchmarks themselves attuned Thio. Just do those calculations and deliver and explore been a steady I'll way if you like. So, you know, way kind of set ourselves this this this big challenge You know, the big fence on the race course, which were clambering over. But the challenge in itself actually should be. I'm much more interesting. The water we're going to use these devices for having built um, eso. Getting into the extra scale era is not so much about doing an extra block. It's a new generation off capability that allows us to do better scientific and industrial research. And that's the interesting bit in this whole story. >>I would tend to agree with you. I think the the focus around exa scale is to look at, you know, new technologies, new ways of doing things, new ways of looking at data and to get new results. So eventually you will get yourself a nexus scale machine. Um, one hopes, sooner rather >>than later. Well, I'm sure you don't tell me one, Ben. >>It's got nothing to do with may. I can't sell you anything, Mark. But there are people outside the door over there who would love to sell you one. Yes. However, if we if you look at your you know your your exa scale machine, Um, how do you believe the workloads are going to be different on an extra scale machine versus your current PETA scale machine? >>So I think there's always a slight conceit when you buy a new national supercomputer. On that conceit is that you're buying a capability that you know on. But many people will run on the whole system. Known truth. We do have people that run on the whole of our archer system. Today's A 118,000 cores, but I would say, and I'm looking at the system. People that run over say, half of that can be counted on Europe on a single hand in a year, and they're doing very specific things. It's very costly simulation they're running on. So, you know, if you look at these systems today, two things show no one is. It's very difficult to get time on them. The Baroque application procedures All of the requirements have to be assessed by your peers and your given quite limited amount of time that you have to eke out to do science. Andi people tend to run their applications in the sweet spot where their application delivers the best performance on You know, we try to push our users over time. Thio use reasonably sized jobs. I think our average job says about 20,000 course, she's not bad, but that does mean that as we move to the exits, kill two things have to happen. One is actually I think we've got to be more relaxed about giving people access to the system, So let's give more people access, let people play, let people try out ideas they've never tried out before. And I think that will lead to a lot more innovation and computational science. But at the same time, I think we also need to be less precious. You know, we to accept these systems will have a variety of sizes of job on them. You know, we're still gonna have people that want to run four million cores or two million cores. That's absolutely fine. Absolutely. Salute those people for trying really, really difficult. But then we're gonna have a huge spectrum of views all the way down to people that want to run on 500 cores or whatever. So I think we need Thio broaden the user base in Alexa Skill system. And I know this is what's happening, for example, in Japan with the new Japanese system. >>So, Mark, if you cast your mind back to almost exactly a year ago after the HPC user forum, you were interviewed for Premier Magazine on Do you alluded in that article to the needs off scientific industrial users requiring, you know, uh on X a flop or an exa scale machine it's clear in your in your previous answer regarding, you know, the workloads. Some would say that the majority of people would be happier with, say, 10 100 petaflop machines. You know, democratization. More people access. But can you provide us examples at the type of science? The needs of industrial users that actually do require those resources to be put >>together as an exa scale machine? So I think you know, it's a very interesting area. At the end of the day, these systems air bought because they are capability systems on. I absolutely take the argument. Why shouldn't we buy 10 100 pattern block systems? But there are a number of scientific areas even today that would benefit from a nexus school system and on these the sort of scientific areas that will use as much access onto a system as much time and as much scale of the system as they can, as you can give them eso on immediate example. People doing chroma dynamics calculations in particle physics, theoretical calculations, they would just use whatever you give them. But you know, I think one of the areas that is very interesting is actually the engineering space where, you know, many people worry the engineering applications over the last decade haven't really kept up with this sort of supercomputers that we have. I'm leading a project called Asimov, funded by M. P S O. C in the UK, which is jointly with Rolls Royce, jointly funded by Rolls Royce and also working with the University of Cambridge, Oxford, Bristol, Warrick. We're trying to do the whole engine gas turbine simulation for the first time. So that's looking at the structure of the gas turbine, the airplane engine, the structure of it, how it's all built it together, looking at the fluid dynamics off the air and the hot gasses, the flu threat, looking at the combustion of the engine looking how fuel is spread into the combustion chamber. Looking at the electrics around, looking at the way the engine two forms is, it heats up and cools down all of that. Now Rolls Royce wants to do that for 20 years. Andi, Uh, whenever they certify, a new engine has to go through a number of physical tests, and every time they do on those tests, it could cost them as much as 25 to $30 million. These are very expensive tests, particularly when they do what's called a blade off test, which would be, you know, blade failure. They could prove that the engine contains the fragments of the blade. Sort of think, continue face really important test and all engines and pass it. What we want to do is do is use an exa scale computer to properly model a blade off test for the first time, so that in future, some simulations can become virtual rather than having thio expend all of the money that Rolls Royce would normally spend on. You know, it's a fascinating project is a really hard project to do. One of the things that I do is I am deaf to share this year. Gordon Bell Price on bond I've really enjoyed to do. That's one of the major prizes in our area, you know, gets announced supercomputing every year. So I have the pleasure of reading all the submissions each year. I what's been really interesting thing? This is my third year doing being on the committee on what's really interesting is the way that big systems like Summit, for example, in the US have pushed the user communities to try and do simulations Nowhere. Nobody's done before, you know. And we've seen this as well, with papers coming after the first use of the for Goku system in Japan, for example, people you know, these are very, very broad. So, you know, earthquake simulation, a large Eddie simulations of boats. You know, a number of things around Genome Wide Association studies, for example. So the use of these computers spans of last area off computational science. I think the really really important thing about these systems is their challenging people that do calculations they've never done before. That's what's important. >>Okay, Thank you. You talked about challenges when I nearly said when you and I had lots of hair, but that's probably much more true of May. Um, we used to talk about grand challenges we talked about, especially around the teraflop era, the ski red program driving, you know, the grand challenges of science, possibly to hide the fact that it was a bomb designing computer eso they talked about the grand challenges. Um, we don't seem to talk about that much. We talk about excess girl. We talk about data. Um Where are the grand challenges that you see that an exa scale computer can you know it can help us. Okay, >>so I think grand challenges didn't go away. Just the phrase went out of fashion. Um, that's like my hair. I think it's interesting. The I do feel the science moves forward by setting itself grand challenges and always had has done, you know, my original backgrounds in particle physics. I was very lucky to spend four years at CERN working in the early stage of the left accelerator when it first came online on. Do you know the scientists there? I think they worked on left 15 years before I came in and did my little ph d on it. Andi, I think that way of organizing science hasn't changed. We just talked less about grand challenges. I think you know what I've seen over the last few years is a renaissance in computational science, looking at things that have previously, you know, people have said have been impossible. So a couple of years ago, for example, one of the key Gordon Bell price papers was on Genome Wide Association studies on some of it. If I may be one of the winner of its, if I remember right on. But that was really, really interesting because first of all, you know, the sort of the Genome Wide Association Studies had gone out of favor in the bioinformatics by a scientist community because people thought they weren't possible to compute. But that particular paper should Yes, you could do these really, really big Continental little problems in a reasonable amount of time if you had a big enough computer. And one thing I felt all the way through my career actually is we've probably discarded Mawr simulations because they were impossible at the time that we've actually decided to do. And I sometimes think we to challenge ourselves by looking at the things we've discovered in the past and say, Oh, look, you know, we could actually do that now, Andi, I think part of the the challenge of bringing an extra service toe life is to get people to think about what they would use it for. That's a key thing. Otherwise, I always say, a computer that is unused to just be turned off. There's no point in having underutilized supercomputer. Everybody loses from that. >>So Let's let's bring ourselves slightly more up to date. We're in the middle of a global pandemic. Uh, on board one of the things in our industry has bean that I've been particularly proud about is I've seen the vendors, all the vendors, you know, offering up machine's onboard, uh, making resources available for people to fight things current disease. Um, how do you see supercomputers now and in the future? Speeding up things like vaccine discovery on help when helping doctors generally. >>So I think you're quite right that, you know, the supercomputer community around the world actually did a really good job of responding to over 19. Inasmuch as you know, speaking for the UK, we put in place a rapid access program. So anybody wanted to do covert research on the various national services we have done to the to two services Could get really quick access. Um, on that, that has worked really well in the UK You know, we didn't have an archer is an old system, Aziz. You know, we didn't have the world's largest supercomputer, but it is happily bean running lots off covert 19 simulations largely for the biomedical community. Looking at Druk modeling and molecular modeling. Largely that's just been going the US They've been doing really large uh, combinatorial parameter search problems on on Summit, for example, looking to see whether or not old drugs could be reused to solve a new problem on DSO, I think, I think actually, in some respects Kobe, 19 is being the sounds wrong. But it's actually been good for supercomputing. Inasmuch is pointed out to governments that supercomputers are important parts off any scientific, the active countries research infrastructure. >>So, um, I'll finish up and tap into your inner geek. Um, there's a lot of technologies that are being banded around to currently enable, you know, the first exa scale machine, wherever that's going to be from whomever, what are the current technologies or emerging technologies that you are interested in excited about looking forward to getting your hands on. >>So in the business case I've written for the U. K's exa scale computer, I actually characterized this is a choice between the American model in the Japanese model. Okay, both of frozen, both of condoms. Eso in America, they're very much gone down the chorus plus GPU or GPU fruit. Um, so you might have, you know, an Intel Xeon or an M D process er center or unarmed process or, for that matter on you might have, you know, 24 g. P. U s. I think the most interesting thing that I've seen is definitely this move to a single address space. So the data that you have will be accessible, but the G p u on the CPU, I think you know, that's really bean. One of the key things that stopped the uptake of GPS today and that that that one single change is going Thio, I think, uh, make things very, very interesting. But I'm not entirely convinced that the CPU GPU model because I think that it's very difficult to get all the all the performance set of the GPU. You know, it will do well in H p l, for example, high performance impact benchmark we're discussing at the beginning of this interview. But in riel scientific workloads, you know, you still find it difficult to find all the performance that has promised. So, you know, the Japanese approach, which is the core, is only approach. E think it's very attractive, inasmuch as you know They're using very high bandwidth memory, very interesting process of which they are going to have to, you know, which they could develop together over 10 year period. And this is one thing that people don't realize the Japanese program and the American Mexico program has been working for 10 years on these systems. I think the Japanese process really interesting because, um, it when you look at the performance, it really does work for their scientific work clothes, and that's that does interest me a lot. This this combination of a A process are designed to do good science, high bandwidth memory and a real understanding of how data flows around the supercomputer. I think those are the things are exciting me at the moment. Obviously, you know, there's new networking technologies, I think, in the fullness of time, not necessarily for the first systems. You know, over the next decade we're going to see much, much more activity on silicon photonics. I think that's really, really fascinating all of these things. I think in some respects the last decade has just bean quite incremental improvements. But I think we're supercomputing is going in the moment. We're a very very disruptive moment again. That goes back to start this discussion. Why is extra skill been difficult to get? Thio? Actually, because the disruptive moment in technology. >>Professor Parsons, thank you very much for your time and your insights. Thank you. Pleasure and folks. Thank you for watching. I hope you've learned something, or at least enjoyed it. With that, I would ask you to stay safe and goodbye.

>> Narrator: From around the globe. It's theCUBE with digital coverage of A New Era In Database Management. Brought to you by Nutanix. >> Hi, I'm Stu Miniman. And welcome to this special presentation with Nutanix. We're talking about A New Era In Database Management. To help us dig into it, first of all, I have the Senior Vice President and General Manager of Nutanix Era Databases and Business Critical Applications, that is Bala Kuchibhotla. And one of our other CUBE alongs, Monica Kumar. Who's an SVP also with Nutanix. Bala, Monica, thank you so much for joining us. >> Thank you, thank you so... >> Great to be here. All right, so first of all, Bala a new Era. We, have a little bit of a punj. You've got me with some punjs there. Of course we know that the database for Nutanix solution is Era. So, we always like to bring out the news first. Why don't you tell us, what does this mean? What is Nutanix announcing today? >> Awesome. Thank you, Stu. Yeah, so today's a very big day for us. I'm super excited to inform all of us and our audience that we are announcing the Eratory dot two GA bits for customers to enjoy it. Some customers can download and start playing with it. So what's new with Nutanix Eratory dot two? As you knows 1.0 is a single cluster solution meaning the customers have to have a Nutanix cluster and then have around the same cluster to enjoy the databases. But with Eratory dot two, it becomes multi-cluster solution. It's not just a multi-cluster solution, but customers can enjoy database across clusters, That means that they can have their Always On Availability Groups SQL servers, their Postgres servers across Nutanix clusters. That means that they can spread across Azure Availability Zones. Now, the most interesting point of this is, it's not just across clusters, customers can place these clusters in the cloud. That is AWS. You can have Nutanix cluster in the AWS cluster and then the primary production clusters maybe on the Nutanix and primary enterprise cloud kind of stuff, that's number one. Number two, we have extended our data management capabilities, data management platform capabilities, and what we call them as global time mission. Global time mission with a data access management. Like racing river, that you need to harness the racing river by constructing a dam and then harness it for multipurpose either irrigation projects or hydroelectric project kind of stuff. You need to kind of do the similar things for your data in company, enterprise company. You need to make sure that the right persons get the right amount of data, so that you don't kind of give all production data to everyone in the company. At the same time, they also need the accessible, with one click they can get the database, the data they want. So that's the data access management. Imagine a QA person only gets the sanitized snapshots or sanitize database backups for them to create the copies. And then we are extending our database engine portfolios too to introduce SAP HANA to the thing. As you know, that we support Oracle today, Postgres, MalSQL, Mariadb SQL server. I'm excited to inform that we are introducing SAP HANA. Our customers can do one click sandbox creation into an environment for SAP HANA predown intense platform. And lastly, I'm super excited to inform that we are becoming a Postgres vendor. We are willing to give 24 by seven, 365 day support but Postgres database engine, that's kind of a provision through Nutanix setup platform. So this way the customers can enjoy the engine, platform, service all together in one single shot with a single 180 company that they can call and get the support they want. I'm super duper excited that this is going to make the customers a truly multicloud multi cluster data management platform. Thank you. >> Yeah. And I'll just add to that too. It's fantastic that we are now offering this new capability. I just want to kind of remind our audience that Nutanix for many years has been providing the foundation the infrastructure software, where you can run all these multiple workloads including databases today. And what we're doing with Era is fantastic because now they are giving our customers the ability to take that database that they run on top of Nutanix to provide that as a service now. So now are talking to a whole different organization here. It's database administrations, it's administrators, it's teams that run databases, it teams that care about data and providing access to data and organizations. >> Well, first of all, congratulations, I've taught for a couple of years to the teams at Nutanix especially some of the people working on PostgreSQL really exciting stuff and you've both seen really the unlocking of database. It used to be ,we talked about, I have one database it's kind of the one that everything runs on. Now, customers they have more databases. You talked about that flexibility is then, where we run it. We'd love to hear, maybe Monica we start with you. You talk about the customers, what does this really mean for them? Because one of our most mission critical applications we talk about, we're not just throwing our databases or what. I don't wake up in the morning and say, Oh let me move it to this cloud and put it in this data center. This needs to be reliable. I need to have access to the data. I need to be able to work with it. So, what does this really mean? And what does it unlock for your customers? >> Yes absolutely, I love to talk about this topic. I mean, if you think about databases, they are means to an end. And in this case, the end is being able to mine insights from the data and then make meaningful decisions based on that. So when we talk to customers, it's really clear that data has not become one of the most valuable assets that an organization owns. Well, of course, in addition to the employees that are part of the organization and our customers. Data is one of the most important assets. But most organizations, the challenges they face is a lot of data gets collected. And in fact, we've heard numbers thrown around for many years like, almost 80% of world's data has been created in the last like three or four years. And data is doubling every two years in terms of volume. Well guess what? Data gets collected. It sits there and organizations are struggling to get access to it with the right performance, the right security and regulation compliance, the reliability, availability, by persona, developers need certain access, analysts needs different access line of businesses need different access. So what we see is organizations are struggling in getting access to data at the right time by the right person on the team and when they need it. And I think that's where database as a service is critical. It's not just about having the database software which is of course important but how you know not make that service available to your stakeholders, to developers to lines of business within the SLAs that they demand. So is it instantly? How quickly can you make it available? How quickly can you use have access to data and do something meaningful with it? And mind the insights for smarter business? And then the one thing I'd like to add is that's where IT and business really come together. That's the glue. If you think about it today, what is the blue between an IT Organization and a business organization? It's the data. And that's where they're really coming together to say how can we together deliver the right service? So you, the business owner can deliver the right outcome for our business. >> That's very true. Maybe I'll just add a couple of comments there. What we're trying to do is we are trying to bring the cloud experience, the RDS-like experience to the enterprise cloud and then hybrid cloud. So the customers will now have a choice of cloud. They don't need to be locked in a particular cloud, at the same time enjoy the true cloud utility experience. We help customers create clouds, database clouds either by themselves if that's big enough to manage the cloud themselves or they can partner with a GSIs like Wipro, WorkHCL and then create a completely managed database service kind of stuff. So, this brings this cloud neutrality, portability for customers and give them the choice and their terms, Stu. >> Well Bala, absolutely we've seen a huge growth in managed services as you've said, maybe bring us inside a little bit. What is free up customers? What we've said for so long that back when HCI first started, it was some of the storage administrators might bristle because you were taking things away from them. It was like, no, we're going to free you up to do other things that as Monica said, deliver more business value not mapping LUNs and doing that. How about from the DBA standpoint? What are some of those repetitive, undifferentiated heavy lifting that we're going to take away from them so that they can focus on the business value. >> Yep. Thank you Stu. So think about this. We all do copy paste operations in laptops. Something of that sort happens in data center at a much larger scale. Meaning that the same kind of copy paste operation happens to databases and petabytes and terabytes of scale. Hundreds of petabytes. It has become the most dreaded complex, long running error prone operation. Why should it be that way? Why should the DBS spend all this mundane tasks and then get busy for every cloning operation? It's a two day job for me, every backup job. It's like a hobby job for provisioning takes like three days. We can take this undifferentiated heavy lifting by this and then let the DBS focus on designing the cloud for them. Looking for the database tuning, design data modeling, ML aspects of the data kind of stuff. So we are freeing up the database Ops people, in a way that they can design the database cloud, and make sure that they are energy focused on high valid things and more towards the business center kind of stuff. >> Yeah. And you know automation is really important. You were talking about is automating mundane grunt work. Like IT spends 80% of its time in maintaining systems. So then where is the time for innovation. So if we can automate stuff that's repetitive, stuff that the machine can do, the software can do, why not? And I think that's what our database as a service often does. And I would add this, the big thing our database as a service does really is provide IT organizations and DV organizations a way to manage heterogeneous databases too. It's not like, here's my environment for Postgres. Here's my environment for my SQL. Here's my environment for Oracle. Here's my environment for SQL server. Now with a single offering, a single tool you can manage your heterogeneous environment across different clouds. On premises cloud, or in a public cloud environment. So I think that's the beauty we are talking about with Nutanix's Era. Is a truly, truly gives organizations that single environment to manage heterogeneous databases, apply the same automation and the ease of management across all these different environments. >> Yeah. I'll just add one comment to that. A true managed PaaS obviously customers in like a single shop go to public cloud, just click through and then they get the database and point. And then if someone is managing the database for them. But if you look at the enterprise data centers, they need to bring that enterprise GalNets and structure to these databases. It's not like anyone can do anything to any or these databases. So we are kind of getting the best of both, the needed enterprise GalNets by these enterprise people at the same time bringing the convenience for the application teams and developers they want to consume these databases like utility. So bringing the cloud experience, bringing the enterprise GalNets. At same time, I'm super confident we can cut down the cost. So that is what Nutanix Era is all about across all the clouds, including the enterprise cloud. >> Well, Bala being simpler and being less expensive are one of the original promises of the cloud that don't necessarily always come out there. So, that's super important. One of the other things, you talk about these hybrid environments. It's not just studied, in the public cloud want to understand these environments, if I'm in the public cloud, can I still leverage some of the services that are in the public cloud? So, if I want to run some analytics, if I want to use some of the phenomenal services that are coming out every day. Is that something that can be done in this environment? >> Yeah, beautiful. Thank you Stu. So we are seeing customers who two categories. There is a public cloud customer, completely born in public cloud cloud, native services. They realize that for every database that maintaining five or seven different copies and the management of these copies is prohibited just because every copy is a faulty copy in the public cloud. Meaning you take a backup snapshot and restore it. Your meter like New York taxi, it starts with running for your EBS   and that you are looking at it kind of stuff. So they can leverage Nutanix clusters and then have a highly efficient cloning capability so that they can cut down some of these costs for these secondary environments that I talk about. What we call is copy data management, that's one kind of use case. The other kind of customers that we are seeing who's cloud is a phenomenon. There's no way that people have to move to cloud. That's the something at a C level mandate that happens. These customers are enjoying their database experience on our enterprise cloud. But when they try to go to these big hyperscalers, they are seeing the disconnect that they're not able to enjoy some of the things that they are seeing on the enterprise cloud with us. So this transition, they are talking to us. Can you get this kind of functionality with Nutanix platform onto some of these big hyperscalers? So there are kind of customers moving both sides, some customers that are public cloud they're time to enjoy our facilities other than copy data management and Nutanix. Customers that are on-prem but they have a mandate to good public cloud ,with our hybrid cloud strategy. They get to enjoy the same kind of convenience that they are seeing it on enterprise and bringing the same kind of governance that they used to do it. so that maybe see customers. Yeah. >> Yeah. Monica, I want to go back to something you talked about customers dealing with that heterogeneous environment that they have reminds me of a lot of the themes that we talked about at nutanix.next because customers have they have multiple clouds they're using, requires different skillsets, different tooling. It's that simplicity layer that Nutanix has been working to deliver since day one. What are you from your customers? How are they doing with this? And especially in the database world. What are some of those challenges that they're really facing that we're looking to help solve with the solution today. >> Yeah. I mean, if you think about it, what customers at least in our experience, what they want or what they're looking for is this modern cloud platform that can really work across multiple cloud environments. Cause people don't want to change running, let's say an Oracle database you're on-prem on a certain stack and then using a whole different stack to run Oracle database in the cloud. What they want is the same exact foundation. So be so they can be, for sure have the right performance. Availability, reliability, the applications don't have to be rewritten on top of Oracle database. They want to preserve all of that, but they want the flexibility to be able to run that cloud platform wherever they choose to. So that's one. So that's choosing the right and modernizing and choosing the right cloud platform is definitely very important to our customers, but you nailed it on the head Stu. It's been about how do you manage it? How do you operate it on a daily basis? And that's where our customers are struggling with multiple types of tools out there, custom tool for every single environment. And that's what they don't want. They want to be able to manage, simply across multiple environments using the same tools and skillsets. And again, and I'm going to beat the same drum, but that's when Nutanix shines. That's a design principle is. It's the exact same technology foundation that you provide to customers to run any applications. In this case it happens to be databases. Exact same foundation you can use to run databases on-prem in the cloud. And then on top of that using Era boom! Simple management, simple operations, simple provisioning simple copy data management, simple patching, all of that becomes easy using just a single framework to manage and operate. And I will tell you this, when we talk to customers, what is it that DBS and database teams are struggling with? They're struggling with SLS and performance on scalability, that's one, number two they're struggling with keeping it up and running and fulfilling the demands of the stakeholders because they cannot keep up with how many databases they need to keep provisioning and patching and updating. So at Nutanix now we are actually solving both those problems with the platform. We are solving the problem of a very specific SLA that we can deliver in any cloud. And with Era, you're solving the issue of that operational complexity. We're making it really easy. So again, IT stakeholders DBS can fulfill the demands of the business stakeholders and really help them monetize the data. >> Yeah. I'll just add on with one concrete examples too. Like we have a big financial customer, they want to run Postgres. They are looking at the public cloud. Can we do a manage services kind of stuff, but you look at this, that the cost difference between a Postgres and your company infrastructure versus managed services almost like $3X to $4X dollars. Now, with Nutanix platform and Era, we were able to show that they can do at much reduced cost, manage their best service experience including their DBA cost are including the cloud administration cost. Like we added the infrastructure picture. We added the people who are going to manage the cloud, internal cloud and then intern experience being, plus plus of what they can see to public cloud. That's what makes the big difference. And this is what data sovereignty, data control, compliance and infrastructure governance, all these things coupled with cloud experiences, what customers really see the value of Era and the enterprise cloud and with an extension to the public cloud, with our hybrid cloud strategy. if they want to move this workload to public cloud they can do it. So, today with AWS clusters and tomorrow with our Azure clusters. So that gives them that kind of insurance not getting locked in by a big hyperscaler, but at same time enjoy the cloud experience. That's what big customers are looking for. >> Alright Bala, all the things you laid out here, what's the availability of Era rotically dot two? >> Era rotically dot two is actually available today. The customers can enjoy download the bits. We already have bunches of beta customers who are trying it out with the recall big telco companies are financial companies, and even big companies that manage big pensions kind of stuff. Let's talk about that kind of stuff. People are looking to us. In fact, there are customers who are looking for, when is this available for Azure cluster so that we can move some of our workloads to and manage the databases in Azure classes. So it is available and I'm looking forward to great feedback from our customers. And I'm hoping that it will solve some of their major critical problems. And in the process they get the best of Nutanix. >> Monica, last question I have for you. This doesn't seem like it's necessarily the same traditional infrastructure go to market for a solution like this. If I think back to, people think of HCI it was like, Oh! well, it was kind of a new box. We know Nutanix is a software company. More of what you do today is subscription based. So, maybe if you could talk a little bit to just how Nutanix goes to market with a solution like this. >> Yeah. And you know what, maybe people don't realize it but I'm hoping a lot of people do that. Nutanix is not just an infrastructure company anymore. In the last many years we've developed a full cloud platform in not only do we offer the infrastructure services with hyperconverged infrastructure which is now really the foundation. It's the hybrid cloud infrastructure. As you know, Stu, we talked to you a month ago and we talked about the evolution of XCI to really becoming the hybrid cloud infrastructure. But in addition to that, we also offer other data center services on storage DR Networking. We also offer DevOps services with application provisioning automation, application orchestration and then of course, database services that we talking about today and we offer desktop services. So Nutanix has really evolved in the last few years to a complete cloud platform really focusing on the application and workloads that run on top of the infrastructure stack. So not just the infrastructure layer but how can we be the best platform to run your databases? Your end is the computing workloads, your analytics applications your enterprise applications, cloud native applications. So that's what this is. And databases is one of our most successful workloads that's that runs a Nutanix very well because of the way the infrastructure software is architected. Because it's really great to scale high performance because again our superior architecture. And now with Era, it's a tool, it's all in one. Now it's also about really simplifying the management of databases and delivering them speedily and with agility to drive innovation in the organizations. >> Yep. Thank you Monica. Thank you. I I'll just add a couple of lines of comments into that. DTM for databases as erotically dots two, is going to be a challenge. And historically we are seen as an infrastructure company but the beauty of databases is so and to send to the infrastructure, the storage. So the language slightly becomes easy. And in fact, this holistic way of looking at solving the problem at the solution level rather than infrastructure helps us to go to a different kind of buyer, different kinds of decision maker, and we are learning. And I can tell you confidently the kind of progress that we have seen for in one enough year, the kind of customers that we are winning. And we are proving that we can bring a big difference to them. Though there is a challenge of DTM speaking the language of database, but the sheer nature of cloud platform the way they are a hundred hyperscale work. That's the kind of language that we take. You can run your solution. And here is how you can cut down your database backup time from hours to less than minute. Here's how you can cut down your patching from 16 hours to less than one hour. It is how you can cut down your provisioning time from multiple weeks to let them like matter of minutes. That holistic way of approaching it coupled with the power of the platform, really making the big difference for us. And I usually tell every time I meet, can you give us an opportunity to cut down your database cost, the PC vote, total cost of operations by close to 50%? That gets them excited that lets then move lean in and say, how do you plan to do it? And then we go about how do we do it? And we do a deep dive and PC people and all of it. So I'm excited. I think this is going to be a big play for Nutanix. We're going to make big difference. >> Absolutely well, Bala, congratulations to the team. Monica, both of you thank you so much for joining, really excited for all the announcements. >> Thank you so much. >> Thank you >> Stay with us. We're going to dig in a little bit more with one more interview for this product launch of the New Era and Database Management from Nutanix. I'm Stu Minimam as always, thank you for watching theCUBE. (cool music)

hi everyone this is dave vellante of the cube and i want to share with you an exciting development with some financial support from hpe the cube is hosting exascale day on friday october 16th high performance technical and business communities are coming together to celebrate exascale day now exascale day is happening on october 18th that's 10 18 as in 10 to the power of 18. now on that day we celebrate the scientists and researchers who make breakthrough discoveries with the assistance of some of the largest supercomputers in the world 10 to the power of 18 is a 1 with 18 zeros after that's six commas or seis comas for you russ hannemann fans of silicon valley fame remember he could only get to tres comas and he became suicidal when his net worth dropped below a billion aka dos comas now an exit scale computer exascale supercomputer can do math at the rate of 10 to the power of 18 calculations per second those are those calculations are called flops or floating point operations per second that's a billion calculations per second or exa-flops now we haven't hit that level yet that exit scale level but dollars to donuts we'll buy we will by next year now today we can do header scale computing that's 10 to the power of 15 calculations per second and we entered the petascale era in 2007 before that was the terrascale era it's kind of like dinosaurs which began in the middle of the dot-com boom in 1997. that's 10 to the 12th calculations per second or trillion per second so we can almost get our heads around that and all the way back in 1972 we had the first gigascale computer which was one times ten to the ninth yeah that's more russ hannemann's speed sorry rush you're not invited to at the exascale day party but you are so go to events dot cube365.net slash 10-18 exascale day it's right there in the screen so check it out mark your calendar we'll be sending out notices so don't worry if you're driving right now we have some of the smartest people in the world joining us they're going to share how innovations with supercomputing are changing the world in healthcare space exploration artificial intelligence and these other mind-melting projects we're super excited to be participating in this program we look forward to some great conversations october 16th right before exascale day put on your calendar see you there