(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)

>>Hello everyone. Welcome back to the Cube's Live coverage. I'm John Fur, host of the Cube. We got two sets here, three sets total. Another one in the executive center. It's our 10th year covering AWS Reinvent. I remember 2013 like it was yesterday. You know, now it's a massive of people buying out restaurants. 35,000 people now it's 55,000, soon to be 70,000 back. Great event. Continuing to set the standard in the industry. We had an amazing guest here, Leah Bibo, vice President of Product Marketing. She's in charge of the messaging, the product, overseeing how these products gonna market. Leah, great to see you. Thanks for joining me on the Cube today. >>Absolutely. It's great to be here. It's also my 10 reinvent, so it's, it's been a wild ride. >>Absolutely. Yeah. You and I were talking before we came on camera, how much we love products and yes, this is a product-centric company, has been from day one and you know, over the years watching the announcements, the tsunami of announcements, just all the innovation that's come out from AWS over the years has been staggering to say the least. Everyone always jokes about, oh my God, 5,000 new announcements, over 200 services you're managing and you're marketing them. It's pretty crazy right now. And Adam, as he comes on, as I called them, the solutions CEO on my piece I wrote on Friday, we're in an era where solutions, the products are enabling more solutions. Unpack the messaging around this cuz this is really big moment for aws. >>Absolutely. Well, I'll say first of all that we are a customer focused company that happens to be really good at innovating incredible products and services for our customers. So today the, the energy in the room and what Adam talked about, I think is focused on a few great things for customers that are really important for transformation. So we talked a lot about best price performance for workloads and we talked about extreme workloads, but if you think about the work that we've been doing to innovate on the silicon side, we're really talking about with Graviton all your workloads and getting really great price performance for all of them. You know, we came out with graviton three 25% faster than graviton two, also 60% more energy efficient. We talked about something that is emerging that I think is gonna be really big, which is simulation and really the ability to model these complex worlds and all the little interactions, which I think, you know, in the future as we have more complex environments like 3D simulation is gonna be a bigger part of every, every business's >>Business. You know, just as an aside, we were talking on the analyst segment that speeds and feeds are back and the old days and the data center days was like, we don't wanna talk about speeds and feeds about solutions and you know, the outcomes when you get the cloud, it was like, okay, get the workloads over there, but people want faster and lower cost performance workloads gotta be running at at high performance. And, and there's a real discussion around those. Let's unpack security data performance. What, what does that mean for customers? Because again, I get the workloads run fast. That's great. What else is behind the curtain, so to speak from a customer standpoint? >>Absolutely. Well I think if you're gonna move all your workloads to the cloud, you know, security is a really big area that's important. It's important to every one of our enterprise companies customers. Actually it's important to all of our customers and we've been working, you know, since the beginning of AWS to really create and build the most secure global infrastructure. And you know, as our customers have moved mission critical workloads, we've built out a lot more capabilities and now we have a whole portfolio of security services. And what we announced today is kind of game changing. The service called Security Lake, which brings together, you know, an ecosystem of security data in a format that's open. So you can share data between all of these sources and it's gonna give folks the opportunity to really be able to analyze data, find threats faster, and just kind of know their security posture. And I think, you know, as we talked about today, you don't wanna think about the cloud as unfathomable, the unfathomable, you really need to know that security. And I think that like a lot of things we discussed, security is a data opportunity, right? And I think we, we had a section on on data, but really if you look at the keynote across security, across solutions, across the purpose built things we made, it's all, it all comes down to data and it's really the, the transformational element that our customers >>Are. I mean the data secured is very integral part good call out there. And I, I wanna just double down on that real quick because I remember in 2014 I interviewed Steven Schmidt when he was the CSOs and back then in 2014, if you remember the conversation was this, the clouds not secure, gotta be on premises. Now in today's keynote, Adam says, and he laid out the whole global security footprint. There's a lot going on that Amazon has now become more secure than on-prem. He actually made that statement. So, and then plus you got thousands of security partners, third party partners, you got the open cyber security framework which you guys co-found with all the other, so you got securities not as a team sport, this is what they, they said yes, yes. What does that mean for customers? Because now this is a big deal. >>Well I think for customers, I mean it means nothing but goodness, right? But all of these thousands of security partners have really innovated and created solutions that our customers are using. But they all have different types of data in different silos. And to really get a full picture bringing all that data together is really important. And it's not easy today. You know, log data from different sources, data from detection services and really what customers want is an easier way to get it all together. Which is why we have the open OCS F and really analyze using the tools of their choice. And whether that's AWS tools for analytics or it's tools from our partners, customers need to be able to make that choice so that they can feel like their applications and their workloads are the most secure on aws. >>You know, I've been very impressed with guard duty and I've been following Merit Bear's blogs on online. She's in the security team, she's amazing. Shout out to her. She's been pushing guard duty for a long time now there's big news around guard duty. So you got EKS protection, you know, at Coan this was the biggest cloud native issue, the runtime of Kubernetes and inside the container and outside the container detection of threats, right? As a real software supply chain concern. How are you guys marketing that? This is a huge announcement. EKS protection I know is very nuanced but it's pretty big deal. >>It is a big deal. It is a big deal. And guard duty has been kind of like a quiet service that maybe you don't hear a lot about, but has been really, really popular with our customers. Adam mentioned that 85% of, you know, our top 2000 customers are using guard duty today. And it was a big moment. We launched EKS protection, you know, a little bit earlier and the customer uptake on that has been really incredible. And it is because you can protect your Kubernetes cluster, which is really important because so many customers are, you know, part of their migration to the cloud is containers. Yeah. And so we're pretty excited that now we can answer that question of what's going on inside the container. And so you have both, yeah, right. You know that your Kubernetes pluses are good and you know what's going on inside the container and it's just more threats that you can detect and protect >>Yourself from. You know, as an aside, I'm sure you're watching this, but you know, we go to a lot of events, you know, the C I C D pipeline as developers are getting higher velocity coding, it has moved in because of DevOps on the cloud into the C I C D pipeline. So you're seeing that developer takes some of those IT roles in the coding workflow, hence the, the shift left and or container security, which you guys now, now and are driving towards. But the security and the data teams are emerging as a very key element inside the organizational structure. When I sat down with Adam, one of the things he was very adamant about in my conversation was not just digital transformation, business transformation, structural organizational moves are making where it's not a department anymore, it is the company, a technology is the company when you transform. Absolutely. So digital is the process, business is the outcome. This is a really huge message. What's your reaction to that? What's, what can you share extra cuz that's, this is a big part of the thing. He hit it right outta the gate on the front end of the keynote. >>Absolutely. Absolutely. I mean I think, you know, companies have been migrating to the cloud for a while, but I think that this time that we're going through has really accelerated that migration And as part of that, you know, digital transformation has become real for a lot of companies. And it is true what Adam said there is technology transformation involved, there's data transformation involved, but it, it is transforming businesses. And I think if you look at some of the things that Adam talked about, you know, aws, supply chain, security Lake, aws clean rooms, and Omic, aws, omic, you know, those are all examples of data and the ability to work with data transforming different lines of business within a company, transforming horizontal processes like contact centers and like supply chain and also, you know, going into vertical specific solutions. So what it means is that as technology becomes more pervasive, as data becomes more pervasive, businesses are transforming and that means that a lot more people are going to use the cloud and interact with the cloud and they might not want to or be able to kind of use our building blocks. And so what's really exciting that what we're able to do is make cloud more accessible to lines of business folks to analysts, to security folks. So >>It's, yeah, and that's, and that's why I was calling my this this new trend I see as Amazon Classic, my words, not your words, I call the, hey there was classic cloud and then you got the next gen clown, the new next generation. And I was talking with Adrian Cockcroft, former aws, so he's now retired, he's gonna come on later today. He and I were talking, he use this thing of you got a bag of Legos aka primitives or a toy that's been assembled for you glued together, ones out of the box, but they're not mutually exclusive. You can build a durable application and foundation with the building blocks more durable. You can manage it, refine it, but you got the solution that breaks. You don't have as much flexibility but you gotta replace it. That's okay too. So like this is now kind of a new portfolio approach to the cloud. It's very interesting and I think, I think, I think that's what I took away from the keynote is that you can have both. >>Yes, absolutely. You can do both. I mean, we're gonna go full throttle on releasing innovations and pushing the envelope on compute and storage and databases and our core services because they matter. And having, you know, the choice to choose from a wide range of options. I mean that's what, that's what customers need. You know, if you're gonna run hpc, you're gonna run machine learning and you're gonna run your SAP applications or your Windows applications, you need choice of what you know, specific type of instance and compute capabilities. You need to get the price performance. It's, it's definitely not a one size fits all. It's a 600 instance type. Size fits all maybe. >>Exactly. And you got a lot of instance and we'll get to that in a second. Yeah, I love the themes. I love this keynote themes you had like at first space, but I get the whole data, then you look at it, you can look at it differently. Really good metaphor, the ocean one I love with the security because he mentioned you can have the confidence to explore go deep snorkeling versus scuba and knowing how much oxygen you have. I mean, so really cool metaphor made me think very provocative. So again, this is kind of why people go to AWS because you now have these, these abilities to do things differently, depend on the context of what products you're working with. Yes. Explain why that was the core theme. Was there any rationale behind that? Was it just how you guys saw it? I mean that was pretty clever. >>Well, I think that, you know, we're, we're talking about environments and I think in this world, you know, there's uncertainty in a lot of places and we really feel like all of us need to be prepared for different types of environments. And so we wanted to explore what that could look like. And I think, you know, we're fascinated by space and the vastness and it is very much like the world of data. I don't know about you, but I actually scuba dive. So I love the depths of the ocean. I loved working on that part. There's extremes, extreme workloads like hpc, extreme workloads like machine learning with the growing models and there's an imagination, which is also one of my favorite areas to explore. >>Yeah. And you use the Antarctica one for about the whole environment and extreme conditions. That's good in the performance. And I love that piece of it. And I want to get into the, some of the things I love the speeds and fee. I think the, the big innovation with the silicon we've been covering as, you know, like a blanket. The, he's got the GRAVITON three 25% faster than GRAVITON two, the C seven GN network intense workloads. This is kind of a big deal. I mean this is one of those things where it might not get picked up in the major press, but the network use cases are significant. Nira has been successful. Share your thoughts on these kinds of innovations because they look kind of small, but they're not, they're >>Big, they're not small for sure, especially at the scale that our customers are, are, are running their applications. Like every little optimization that you can get really makes a huge difference. And I think it's exciting. I mean you hit on, you kind of hit on it when we've been working on silicon for a while now we know that, you know, if we're gonna keep pushing the element, the envelope in these areas, we had to, we had to go down to the silicon. And I think that Nitro has really been what's kind of been a breakthrough for us. You know, reinventing that virtualization layer, offloading security and storage and networking to special purpose chips. And I think that it's not just in the area of network optimization, right? You saw training optimized instances and inference optimized instances and HPC optimized instances. So yeah, we are kind of looking at all the extremes of, of what customers want to do. >>I know you can't talk about the future, but I can almost connect the dots as you're talking. It's like, hmm, specialized instances, specialized chips, maybe programmability of workload, smart intelligence, generative AI, weaving in there. A lot of kind of cool things I can see around the corner around generative AI automation. Hey, go to this instance with that go here. This is kind of what I see kind of coming around the corner. >>And we have some of that with our instance optimizers, our cost optimizer products where, you know, we wanna help customers find the best instance for their workload, get the best utilization they possibly can, you know, cut costs, but still have the great performance. So I don't, I don't know about your future, John, it sounds great, but we have, you know, we're taking steps in that direction today. >>Still look in this code that's gonna be on this code. Okay. Any, okay, I wanna give you one final question. Well, well two questions. One was a comment Adam made, I'd love to get your reaction if you want to tighten your bell, come to the cloud. I thought that was a very interesting nuance. A lot of economic pressure. Cloud is an opportunity to get agile, time to value faster. We had Zs carve cube analyst who's with us earlier said, the more you spend on the cloud, the more you save. That was his line, which I thought was very smart. Spending more doesn't mean you're gonna lose money, means you can save money too. So a lot of cost optimization discussions. Absolutely. Hey, your belt come to the cloud. What does he mean by that? >>Well I think that in, in times where, you know, there's uncertainty and economic conditions, it is, it's really, you know, you sometimes wanna pull back kind of, you know, batten down the hatches. But the cloud really, and we saw this with C you know, if you, if you move to the cloud, not only can you cut costs, but you put yourself in this position where you can continue to innovate and you can be agile and you can be prepared for whatever environment you're in so that you know when things go back or you have a customer needs that and innovation that goes off like you, you can accelerate back up really, really quickly. And I think we talked about Airbnb, that example of how, you know, in, in that really tough time of covid when travel industry wasn't happening so much, you know, they were able to scale back and save money. And then at the same time when, you know, Airbnb's kind of once again travel came back, they were in a position to really, really quickly change with the, the customer needs. >>You know, Lee, it's always great talking with you. You got a lot of energy, you're so smart and we both love products and you're leading the product marketing. We have an Instagram challenge here on the cube. I'm gonna put you on the spot here. Oh my gosh. It's called Instagram. We called a bumper sticker section. We used to call it what's the bumper sticker for reinvent. But we kind of modernized that. If you were gonna do an Instagram reel right now, what would be the Instagram reel for reinvent Keynote day one. As we look for, we got Verner, we'll probably talk about productivity with developers. What's the Instagram reel for reinvent? >>Wow. That means I have to get short with it, right? I am, I'm not always, that's still wrong answer. Yeah, well I think, you know, this is really big day one, so it's excitement, it's, we're glad to be here. We have a lot coming for you. We're super excited. And if you think about it, it's price, performance, it's data, it's security and it's solutions for purpose-built use cases. >>Great job. Congratulations. I love the message. I love how you guys had the theme. I thought it was great. And it's great to see Amazon continue to innovate with, with the, with the, with the innovation on the product side. But as we get into transformation, starting to see these solutions and the ecosystem is thriving and looking forward to hearing the, the new partner, chief Aruba tomorrow. Absolutely. See what she's got a new plan apparently unveiling. So exciting. Everyone's pretty excited. Thanks for coming >>On. Great. Great. Thanks for having >>Me. All right. Leah, here in the cube. You are the cube, the leader in tech coverage. I'm John Fur, your host. More live coverage after the short break. We'll be right back here. Day two of the cube, day one of reinvent. Lot of great action. Three, four days of wall to wall coverage. We'll be right back.

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

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 at our inaugural super cloud 22 event we further refined the concept of a super cloud iterating on the definition the salient attributes and some examples of what is and what is not a super cloud welcome to this week's wikibon cube insights powered by etr you know snowflake has always been what we feel is one of the strongest examples of a super cloud and in this breaking analysis from our studios in palo alto we unpack our interview with benoit de javille co-founder and president of products at snowflake and we test our super cloud definition on the company's data cloud platform and we're really looking forward to your feedback first let's examine how we defl find super cloudant very importantly one of the goals of super cloud 22 was to get the community's input on the definition and iterate on previous work super cloud is an emerging computing architecture that comprises a set of services which are abstracted from the underlying primitives of hyperscale clouds we're talking about services such as compute storage networking security and other native tooling like machine learning and developer tools to create a global system that spans more than one cloud super cloud as shown on this slide has five essential properties x number of deployment models and y number of service models we're looking for community input on x and y and on the first point as well so please weigh in and contribute now we've identified these five essential elements of a super cloud let's talk about these first the super cloud has to run its services on more than one cloud leveraging the cloud native tools offered by each of the cloud providers the builder of the super cloud platform is responsible for optimizing the underlying primitives of each cloud and optimizing for the specific needs be it cost or performance or latency or governance data sharing security etc but those primitives must be abstracted such that a common experience is delivered across the clouds for both users and developers the super cloud has a metadata intelligence layer that can maximize efficiency for the specific purpose of the super cloud i.e the purpose that the super cloud is intended for and it does so in a federated model and it includes what we call a super pass this is a prerequisite that is a purpose-built component and enables ecosystem partners to customize and monetize incremental services while at the same time ensuring that the common experiences exist across clouds now in terms of deployment models we'd really like to get more feedback on this piece but here's where we are so far based on the feedback we got at super cloud 22. we see three deployment models the first is one where a control plane may run on one cloud but supports data plane interactions with more than one other cloud the second model instantiates the super cloud services on each individual cloud and within regions and can support interactions across more than one cloud with a unified interface connecting those instantiations those instances to create a common experience and the third model superimposes its services as a layer or in the case of snowflake they call it a mesh on top of the cloud on top of the cloud providers region or regions with a single global instantiation a single global instantiation of those services which spans multiple cloud providers this is our understanding from a comfort the conversation with benoit dejaville as to how snowflake approaches its solutions and for now we're going to park the service models we need to more time to flesh that out and we'll propose something shortly for you to comment on now we peppered benoit dejaville at super cloud 22 to test how the snowflake data cloud aligns to our concepts and our definition let me also say that snowflake doesn't use the term data cloud they really want to respect and they want to denigrate the importance of their hyperscale partners nor do we but we do think the hyperscalers today anyway are building or not building what we call super clouds but they are but but people who bar are building super clouds are building on top of hyperscale clouds that is a prerequisite so here are the questions that we tested with snowflake first question how does snowflake architect its data cloud and what is its deployment model listen to deja ville talk about how snowflake has architected a single system play the clip there are several ways to do this you know uh super cloud as as you name them the way we we we picked is is to create you know one single system and that's very important right the the the um [Music] there are several ways right you can instantiate you know your solution uh in every region of a cloud and and you know potentially that region could be a ws that region could be gcp so you are indeed a multi-cloud solution but snowflake we did it differently we are really creating cloud regions which are superposed on top of the cloud provider you know region infrastructure region so we are building our regions but but where where it's very different is that each region of snowflake is not one in instantiation of our service our service is global by nature we can move data from one region to the other when you land in snowflake you land into one region but but you can grow from there and you can you know exist in multiple clouds at the same time and that's very important right it's not one single i mean different instantiation of a system is one single instantiation which covers many cloud regions and many cloud providers snowflake chose the most advanced level of our three deployment models dodgeville talked about too presumably so it could maintain maximum control and ensure that common experience like the iphone model next we probed about the technical enablers of the data cloud listen to deja ville talk about snow grid he uses the term mesh and then this can get confusing with the jamaicani's data mesh concept but listen to benoit's explanation well as i said you know first we start by building you know snowflake regions we have today furry region that spawn you know the world so it's a worldwide worldwide system with many regions but all these regions are connected together they are you know meshed together with our technology we name it snow grid and that makes it hard because you know regions you know azure region can talk to a ws region or gcp regions and and as a as a user of our cloud you you don't see really these regional differences that you know regions are in different you know potentially clown when you use snowflake you can exist your your presence as an organization can be in several regions several clouds if you want geographic and and and both geographic and cloud provider so i can share data irrespective of the the cloud and i'm in the snowflake data cloud is that correct i can do that today exactly and and that's very critical right what we wanted is to remove data silos and and when you instantiate a system in one single region and that system is locked in that region you cannot communicate with other parts of the world you are locking the data in one region right and we didn't want to do that we wanted you know data to be distributed the way customer wants it to be distributed across the world and potentially sharing data at world scale now maybe there are many ways to skin the other cat meaning perhaps if a platform does instantiate in multiple places there are ways to share data but this is how snowflake chose to approach the problem next question how do you deal with latency in this big global system this is really important to us because while snowflake has some really smart people working as engineers and and the like we don't think they've solved for the speed of light problem the best people working on it as we often joke listen to benoit deja ville's comments on this topic so yes and no the the way we do it it's very expensive to do that because generally if you want to join you know data which is in which are in different regions and different cloud it's going to be very expensive because you need to move you know data every time you join it so the way we do it is that you replicate the subset of data that you want to access from one region from other regions so you can create this data mesh but data is replicated to make it very cheap and very performant too and is the snow grid does that have the metadata intelligence yes to actually can you describe that a little bit yeah snow grid is both uh a way to to exchange you know metadata about so each region of snowflake knows about all the other regions of snowflake every time we create a new region diary you know the metadata is distributed over our data cloud not only you know region knows all the regions but knows you know every organization that exists in our clouds where this organization is where data can be replicated by this organization and then of course it's it's also used as a way to uh uh exchange data right so you can exchange you know beta by scale of data size and we just had i was just receiving an email from one of our customers who moved more than four petabytes of data cross-region cross you know cloud providers in you know few days and you know it's a lot of data so it takes you know some time to move but they were able to do that online completely online and and switch over you know to the diff to the other region which is failover is very important also so yes and no probably means typically no he says yes and no probably means no so it sounds like snowflake is selectively pulling small amounts of data and replicating it where necessary but you also heard him talk about the metadata layer which is one of the essential aspects of super cloud okay next we dug into security it's one of the most important issues and we think one of the hardest parts related to deploying super cloud so we've talked about how the cloud has become the first line of defense for the cso but now with multi-cloud you have multiple first lines of defense and that means multiple shared responsibility models and multiple tool sets from different cloud providers and an expanded threat surface so listen to benoit's explanation here please play the clip this is a great question uh security has always been the most important aspect of snowflake since day one right this is the question that every customer of ours has you know how you can you guarantee the security of my data and so we secure data really tightly in region we have several layers of security it starts by by encrypting it every data at rest and that's very important a lot of customers are not doing that right you hear these attacks for example on on cloud you know where someone left you know their buckets uh uh open and then you know you can access the data because it's a non-encrypted uh so we are encrypting everything at rest we are encrypting everything in transit so a region is very secure now you know you never from one region you never access data from another region in snowflake that's why also we replicate data now the replication of that data across region or the metadata for that matter is is really highly secure so snow grits ensure that everything is encrypted everything is you know we have multiple you know encryption keys and it's you know stored in hardware you know secure modules so we we we built you know snow grids such that it's secure and it allows very secure movement of data so when we heard this explanation we immediately went to the lowest common denominator question meaning when you think about how aws for instance deals with data in motion or data and rest it might be different from how another cloud provider deals with it so how does aws uh uh uh differences for example in the aws maturity model for various you know cloud capabilities you know let's say they've got a faster nitro or graviton does it do do you have to how does snowflake deal with that do they have to slow everything else down like imagine a caravan cruising you know across the desert so you know every truck can keep up let's listen it's a great question i mean of course our software is abstracting you know all the cloud providers you know infrastructure so that when you run in one region let's say aws or azure it doesn't make any difference as far as the applications are concerned and and this abstraction of course is a lot of work i mean really really a lot of work because it needs to be secure it needs to be performance and you know every cloud and it has you know to expose apis which are uniform and and you know cloud providers even though they have potentially the same concept let's say blob storage apis are completely different the way you know these systems are secure it's completely different the errors that you can get and and the retry you know mechanism is very different from one cloud to the other performance is also different we discovered that when we were starting to port our software and and and you know we had to completely rethink how to leverage blob storage in that cloud versus that cloud because just of performance too so we had you know for example to you know stripe data so all this work is work that's you know you don't need as an application because our vision really is that applications which are running in our data cloud can you know be abstracted of all this difference and and we provide all the services all the workload that this application need whether it's transactional access to data analytical access to data you know managing you know logs managing you know metrics all of these is abstracted too such that they are not you know tied to one you know particular service of one cloud and and distributing this application across you know many regions many cloud is very seamless so from that answer we know that snowflake takes care of everything but we really don't understand the performance implications in you know in that specific case but we feel pretty certain that the promises that snowflake makes around governance and security within their data sharing construct construct will be kept now another criterion that we've proposed for super cloud is a super pass layer to create a common developer experience and an enabler for ecosystem partners to monetize please play the clip let's listen we build it you know a custom build because because as you said you know what exists in one cloud might not exist in another cloud provider right so so we have to build you know on this all these this components that modern application mode and that application need and and and and that you know goes to machine learning as i say transactional uh analytical system and the entire thing so such that they can run in isolation basically and the objective is the developer experience will be identical across those clouds yes right the developers doesn't need to worry about cloud provider and actually our system we have we didn't talk about it but the marketplace that we have which allows actually to deliver we're getting there yeah okay now we're not going to go deep into ecosystem today we've talked about snowflakes strengths in this regard but snowflake they pretty much ticked all the boxes on our super cloud attributes and definition we asked benoit dejaville to confirm that this is all shipping and available today and he also gave us a glimpse of the future play the clip and we are still developing it you know the transactional you know unistore as we call it was announced in last summit so so they are still you know working properly but but but that's the vision right and and and that's important because we talk about the infrastructure right you mentioned a lot about storage and compute but it's not only that right when you think about application they need to use the transactional database they need to use an analytical system they need to use you know machine learning so you need to provide also all these services which are consistent across all the cloud providers so you can hear deja ville talking about expanding beyond taking advantage of the core infrastructure storage and networking et cetera and bringing intelligence to the data through machine learning and ai so of course there's more to come and there better be at this company's valuation despite the recent sharp pullback in a tightening fed environment okay so i know it's cliche but everyone's comparing snowflakes and data bricks databricks has been pretty vocal about its open source posture compared to snowflakes and it just so happens that we had aligotsy on at super cloud 22 as well he wasn't in studio he had to do remote because i guess he's presenting at an investor conference this week so we had to bring him in remotely now i didn't get to do this interview john furrier did but i listened to it and captured this clip about how data bricks sees super cloud and the importance of open source take a listen to goatzee yeah i mean let me start by saying we just we're big fans of open source we think that open source is a force in software that's going to continue for you know decades hundreds of years and it's going to slowly replace all proprietary code in its way we saw that you know it could do that with the most advanced technology windows you know proprietary operating system very complicated got replaced with linux so open source can pretty much do anything and what we're seeing with the data lake house is that slowly the open source community is building a replacement for the proprietary data warehouse you know data lake machine learning real-time stack in open source and we're excited to be part of it for us delta lake is a very important project that really helps you standardize how you lay out your data in the cloud and with it comes a really important protocol called delta sharing that enables you in an open way actually for the first time ever share large data sets between organizations but it uses an open protocol so the great thing about that is you don't need to be a database customer you don't even like databricks you just need to use this open source project and you can now securely share data sets between organizations across clouds and it actually does so really efficiently just one copy of the data so you don't have to copy it if you're within the same cloud so the implication of ellie gotzi's comments is that databricks with delta sharing as john implied is playing a long game now i don't know if enough about the databricks architecture to comment in detail i got to do more research there so i reached out to my two analyst friends tony bear and sanji mohan to see what they thought because they cover these companies pretty closely here's what tony bear said quote i've viewed the divergent lake house strategies of data bricks and snowflake in the context of their roots prior to delta lake databrick's prime focus was the compute not the storage layer and more specifically they were a compute engine not a database snowflake approached from the opposite end of the pool as they originally fit the mold of the classic database company rather than a specific compute engine per se the lake house pushes both companies outside of their original comfort zones data bricks to storage snowflake to compute engine so it makes perfect sense for databricks to embrace the open source narrative at the storage layer and for snowflake to continue its walled garden approach but in the long run their strategies are already overlapping databricks is not a 100 open source company its practitioner experience has always been proprietary and now so is its sql query engine likewise snowflake has had to open up with the support of iceberg for open data lake format the question really becomes how serious snowflake will be in making iceberg a first-class citizen in its environment that is not necessarily officially branding a lake house but effectively is and likewise can databricks deliver the service levels associated with walled gardens through a more brute force approach that relies heavily on the query engine at the end of the day those are the key requirements that will matter to data bricks and snowflake customers end quote that was some deep thought by by tony thank you for that sanjay mohan added the following quote open source is a slippery slope people buy mobile phones based on open source android but it's not fully open similarly databricks delta lake was not originally fully open source and even today its photon execution engine is not we are always going to live in a hybrid world snowflake and databricks will support whatever model works best for them and their customers the big question is do customers care as deeply about which vendor has a higher degree of openness as we technology people do i believe customers evaluation criteria is far more nuanced than just to decipher each vendor's open source claims end quote okay so i had to ask dodgeville about their so-called wall garden approach and what their strategy is with apache iceberg here's what he said iceberg is is very important so just to to give some context iceberg is an open you know table format right which was you know first you know developed by netflix and netflix you know put it open source in the apache community so we embrace that's that open source standard because because it's widely used by by many um many you know companies and also many companies have you know really invested a lot of effort in building you know big data hadoop solution or data like solution and they want to use snowflake and they couldn't really use snowflake because all their data were in open you know formats so we are embracing icebergs to help these companies move through the cloud but why we have been relentless with direct access to data direct access to data is a little bit of a problem for us and and the reason is when you direct access to data now you have direct access to storage now you have to understand for example the specificity of one cloud versus the other so as soon as you start to have direct access to data you lose your you know your cloud diagnostic layer you don't access data with api when you have direct access to data it's very hard to secure data because you need to grant access direct access to tools which are not you know protected and you see a lot of you know hacking of of data you know because of that so so that was not you know direct access to data is not serving well our customers and that's why we have been relented to do that because it's it's cr it's it's not cloud diagnostic it's it's you you have to code that you have to you you you need a lot of intelligence while apis access so we want open apis that's that's i guess the way we embrace you know openness is is by open api versus you know you access directly data here's my take snowflake is hedging its bets because enough people care about open source that they have to have some open data format options and it's good optics and you heard benoit deja ville talk about the risks of directly accessing the data and the complexities it brings now is that maybe a little fud against databricks maybe but same can be said for ollie's comments maybe flooding the proprietaryness of snowflake but as both analysts pointed out open is a spectrum hey i remember unix used to equal open systems okay let's end with some etr spending data and why not compare snowflake and data bricks spending profiles this is an xy graph with net score or spending momentum on the y-axis and pervasiveness or overlap in the data set on the x-axis this is data from the january survey when snowflake was holding above 80 percent net score off the charts databricks was also very strong in the upper 60s now let's fast forward to this next chart and show you the july etr survey data and you can see snowflake has come back down to earth now remember anything above 40 net score is highly elevated so both companies are doing well but snowflake is well off its highs and data bricks has come down somewhat as well databricks is inching to the right snowflake rocketed to the right post its ipo and as we know databricks wasn't able to get to ipo during the covet bubble ali gotzi is at the morgan stanley ceo conference this week they got plenty of cash to withstand a long-term recession i'm told and they've started the message that they're a billion dollars in annualized revenue i'm not sure exactly what that means i've seen some numbers on their gross margins i'm not sure what that means i've seen some numbers on their net retention revenue or net revenue retention again i'll reserve judgment until we see an s1 but it's clear both of these companies have momentum and they're out competing in the market well as always be the ultimate arbiter different philosophies perhaps is it like democrats and republicans well it could be but they're both going after a solving data problem both companies are trying to help customers get more value out of their data and both companies are highly valued so they have to perform for their investors to paraphrase ralph nader the similarities may be greater than the differences okay that's it for today thanks to the team from palo alto for this awesome super cloud studio build alex myerson and ken shiffman are on production in the palo alto studios today kristin martin and sheryl knight get the word out to our community rob hoff is our editor-in-chief over at siliconangle thanks to all please check out etr.ai for all the survey data remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcasts i publish each week on wikibon.com and siliconangle.com and you can email me at david.vellante at siliconangle.com or dm me at devellante or comment on my linkedin posts and please as i say etr has got some of the best survey data in the business we track it every quarter and really excited to be partners with them this is dave vellante for the cube insights powered by etr thanks for watching and we'll see you next time on breaking analysis [Music] you

(upbeat music) >> Welcome back to Boston, everybody. This is the birthplace of theCUBE. In 2010, May of 2010 at EMC World, right in this very venue, John Furrier called it the chowder and lobster post. I'm Dave Vellante. We're here at RE:INFORCE 2022, Ed Walsh, CEO of ChaosSearch. Doing a drive by Ed. Thanks so much for stopping in. You're going to help me wrap up in our final editorial segment. >> Looking forward to it. >> I really appreciate it. >> Thank you for including me. >> How about that? 2010. >> That's amazing. It was really in this-- >> Really in this building. Yeah, we had to sort of bury our way in, tunnel our way into the Blogger Lounge. We did four days. >> Weekends, yeah. >> It was epic. It was really epic. But I'm glad they're back in Boston. AWS was going to do June in Houston. >> Okay. >> Which would've been awful. >> Yeah, yeah. No, this is perfect. >> Yeah. Thank God they came back. You saw Boston in summer is great. I know it's been hot, And of course you and I are from this area. >> Yeah. >> So how you been? What's going on? I mean, it's a little crazy out there. The stock market's going crazy. >> Sure. >> Having the tech lash, what are you seeing? >> So it's an interesting time. So I ran a company in 2008. So we've been through this before. By the way, the world's not ending, we'll get through this. But it is an interesting conversation as an investor, but also even the customers. There's some hesitation but you have to basically have the right value prop, otherwise things are going to get sold. So we are seeing longer sales cycles. But it's nothing that you can't overcome. But it has to be something not nice to have, has to be a need to have. But I think we all get through it. And then there is some, on the VC side, it's now buckle down, let's figure out what to do which is always a challenge for startup plans. >> In pre 2000 you, maybe you weren't a CEO but you were definitely an executive. And so now it's different and a lot of younger people haven't seen this. You've got interest rates now rising. Okay, we've seen that before but it looks like you've got inflation, you got interest rates rising. >> Yep. >> The consumer spending patterns are changing. You had 6$, $7 gas at one point. So you have these weird crosscurrents, >> Yup. >> And people are thinking, "Okay post-September now, maybe because of the recession, the Fed won't have to keep raising interest rates and tightening. But I don't know what to root for. It's like half full, half empty. (Ed laughing) >> But we haven't been in an environment with high inflation. At least not in my career. >> Right. Right. >> I mean, I got into 92, like that was long gone, right?. >> Yeah. >> So it is a interesting regime change that we're going to have to deal with, but there's a lot of analogies between 2008 and now that you still have to work through too, right?. So, anyway, I don't think the world's ending. I do think you have to run a tight shop. So I think the grow all costs is gone. I do think discipline's back in which, for most of us, discipline never left, right?. So, to me that's the name of the game. >> What do you tell just generally, I mean you've been the CEO of a lot of private companies. And of course one of the things that you do to retain people and attract people is you give 'em stock and it's great and everybody's excited. >> Yeah. >> I'm sure they're excited cause you guys are a rocket ship. But so what's the message now that, Okay the market's down, valuations are down, the trees don't grow to the moon, we all know that. But what are you telling your people? What's their reaction? How do you keep 'em motivated? >> So like anything, you want over communicate during these times. So I actually over communicate, you get all these you know, the Sequoia decks, 2008 and the recent... >> (chuckles) Rest in peace good times, that one right? >> I literally share it. Why? It's like, Hey, this is what's going on in the real world. It's going to affect us. It has almost nothing to do with us specifically, but it will affect us. Now we can't not pay attention to it. It does change how you're going to raise money, so you got to make sure you have the right runway to be there. So it does change what you do, but I think you over communicate. So that's what I've been doing and I think it's more like a student of the game, so I try to share it, and I say some appreciate it others, I'm just saying, this is normal, we'll get through this and this is what happened in 2008 and trust me, once the market hits bottom, give it another month afterwards. Then everyone says, oh, the bottom's in and we're back to business. Valuations don't go immediately back up, but right now, no one knows where the bottom is and that's where kind of the world's ending type of things. >> Well, it's interesting because you talked about, I said rest in peace good times >> Yeah >> that was the Sequoia deck, and the message was tighten up. Okay, and I'm not saying you shouldn't tighten up now, but the difference is, there was this period of two years of easy money and even before that, it was pretty easy money. >> Yeah. >> And so companies are well capitalized, they have runway so it's like, okay, I was talking to Frank Slootman about this now of course there are public companies, like we're not taking the foot off the gas. We're inherently profitable, >> Yeah. >> we're growing like crazy, we're going for it. You know? So that's a little bit of a different dynamic. There's a lot of good runway out there, isn't there? >> But also you look at the different companies that were either born or were able to power through those environments are actually better off. You come out stronger in a more dominant position. So Frank, listen, if you see what Frank's done, it's been unbelievable to watch his career, right?. In fact, he was at Data Domain, I was Avamar so, but look at what he's done since, he's crushed it. Right? >> Yeah. >> So for him to say, Hey, I'm going to literally hit the gas and keep going. I think that's the right thing for Snowflake and a right thing for a lot of people. But for people in different roles, I literally say that you have to take it seriously. What you can't be is, well, Frank's in a different situation. What is it...? How many billion does he have in the bank? So it's... >> He's over a billion, you know, over a billion. Well, you're on your way Ed. >> No, no, no, it's good. (Dave chuckles) Okay, I want to ask you about this concept that we've sort of we coined this term called Supercloud. >> Sure. >> You could think of it as the next generation of multi-cloud. The basic premises that multi-cloud was largely a symptom of multi-vendor. Okay. I've done some M&A, I've got some Shadow IT, spinning up, you know, Shadow clouds, projects. But it really wasn't a strategy to have a continuum across clouds. And now we're starting to see ecosystems really build, you know, you've used the term before, standing on the shoulders of giants, you've used that a lot. >> Yep. >> And so we're seeing that. Jerry Chen wrote a seminal piece on Castles in The Cloud, so we coined this term SuperCloud to connote this abstraction layer that hides the underlying complexities and primitives of the individual clouds and then adds value on top of it and can adjudicate and manage, irrespective of physical location, Supercloud. >> Yeah. >> Okay. What do you think about that concept?. How does it maybe relate to some of the things that you're seeing in the industry? >> So, standing on shoulders of giants, right? So I always like to do hard tech either at big company, small companies. So we're probably your definition of a Supercloud. We had a big vision, how to literally solve the core challenge of analytics at scale. How are you going to do that? You're not going to build on your own. So literally we're leveraging the primitives, everything you can get out of the Amazon cloud, everything get out of Google cloud. In fact, we're even looking at what it can get out of this Snowflake cloud, and how do we abstract that out, add value to it? That's where all our patents are. But it becomes a simplified approach. The customers don't care. Well, they care where their data is. But they don't care how you got there, they just want to know the end result. So you simplify, but you gain the advantages. One thing's interesting is, in this particular company, ChaosSearch, people try to always say, at some point the sales cycle they say, no way, hold on, no way that can be fast no way, or whatever the different issue. And initially we used to try to explain our technology, and I would say 60% was explaining the public, cloud capabilities and then how we, harvest those I guess, make them better add value on top and what you're able to get is something you couldn't get from the public clouds themselves and then how we did that across public clouds and then extracted it. So if you think about that like, it's the Shoulders of giants. But what we now do, literally to avoid that conversation because it became a lengthy conversation. So, how do you have a platform for analytics that you can't possibly overwhelm for ingest. All your messy data, no pipelines. Well, you leverage things like S3 and EC2, and you do the different security things. You can go to environments say, you can't possibly overrun me, I could not say that. If I didn't literally build on the shoulders giants of all these public clouds. But the value. So if you're going to do hard tech as a startup, you're going to build, you're going to be the principles of Supercloud. Maybe they're not the same size of Supercloud just looking at Snowflake, but basically, you're going to leverage all that, you abstract it out and that's where you're able to have a lot of values at that. >> So let me ask you, so I don't know if there's a strict definition of Supercloud, We sort of put it out to the community and said, help us define it. So you got to span multiple clouds. It's not just running in each cloud. There's a metadata layer that kind of understands where you're pulling data from. Like you said you can pull data from Snowflake, it sounds like we're not running on Snowflake, correct? >> No, complimentary to them in their different customers. >> Yeah. Okay. >> They want to build on top of a data platform, data apps. >> Right. And of course they're going cross cloud. >> Right. >> Is there a PaaS layer in there? We've said there's probably a Super PaaS layer. You're probably not doing that, but you're allowing people to bring their own, bring your own PaaS sort of thing maybe. >> So we're a little bit different but basically we publish open APIs. We don't have a user interface. We say, keep the user interface. Again, we're solving the challenge of analytics at scale, we're not trying to retrain your analytics, either analysts or your DevOps or your SOV or your Secop team. They use the tools they already use. Elastic search APIs, SQL APIs. So really they program, they build applications on top of us, Equifax is a good example. Case said it coming out later on this week, after 18 months in production but, basically they're building, we provide the abstraction layer, the quote, I'm going to kill it, Jeff Tincher, who owns all of SREs worldwide, said to the effect of, Hey I'm able to rethink what I do for my data pipelines. But then he also talked about how, that he really doesn't have to worry about the data he puts in it. We deal with that. And he just has to, just query on the other side. That simplicity. We couldn't have done that without that. So anyway, what I like about the definition is, if you were going to do something harder in the world, why would you try to rebuild what Amazon, Google and Azure or Snowflake did? You're going to add things on top. We can still do intellectual property. We're still doing patents. So five grand patents all in this. But literally the abstraction layer is the simplification. The end users do not want to know that complexity, even though they ask the questions. >> And I think too, the other attribute is it's ecosystem enablement. Whereas I think, >> Absolutely >> in general, in the Multicloud 1.0 era, the ecosystem wasn't thinking about, okay, how do I build on top and abstract that. So maybe it is Multicloud 2.0, We chose to use Supercloud. So I'm wondering, we're at the security conference, >> RE: INFORCE is there a security Supercloud? Maybe Snyk has the developer Supercloud or maybe Okta has the identity Supercloud. I think CrowdStrike maybe not. Cause CrowdStrike competes with Microsoft. So maybe, because Microsoft, what's interesting, Merritt Bear was just saying, look, we don't show up in the spending data for security because we're not charging for most of our security. We're not trying to make a big business. So that's kind of interesting, but is there a potential for the security Supercloud? >> So, I think so. But also, I'll give you one thing I talked to, just today, at least three different conversations where everyone wants to log data. It's a little bit specific to us, but basically they want to do the security data lake. The idea of, and Snowflake talks about this too. But the idea of putting all the data in one repository and then how do you abstract out and get value from it? Maybe not the perfect, but it becomes simple to do but hard to get value out. So the different players are going to do that. That's what we do. We're able to, once you land it in your S3 or it doesn't matter, cloud of choice, simple storage, we allow you to get after that data, but we take the primitives and hide them from you. And all you do is query the data and we're spinning up stateless computer to go after it. So then if I look around the floor. There's going to be a bunch of these players. I don't think, why would someone in this floor try to recreate what Amazon or Google or Azure had. They're going to build on top of it. And now the key thing is, do you leave it in standard? And now we're open APIs. People are building on top of my open APIs or do you try to put 'em in a walled garden? And they're in, now your Supercloud. Our belief is, part of it is, it needs to be open access and let you go after it. >> Well. And build your applications on top of it openly. >> They come back to snowflake. That's what Snowflake's doing. And they're basically saying, Hey come into our proprietary environment. And the benefit is, and I think both can win. There's a big market. >> I agree. But I think the benefit of Snowflake's is, okay, we're going to have federated governance, we're going to have data sharing, you're going to have access to all the ecosystem players. >> Yep. >> And as everything's going to be controlled and you know what you're getting. The flip side of that is, Databricks is the other end >> Yeah. >> of that spectrum, which is no, no, you got to be open. >> Yeah. >> So what's going to happen, well what's happening clearly, is Snowflake's saying, okay we've got Snowpark. we're going to allow Python, we're going to have an Apache Iceberg. We're going to have open source tooling that you can access. By the way, it's not going to be as good as our waled garden where the flip side of that is you get Databricks coming at it from a data science and data engineering perspective. And there's a lot of gaps in between, aren't there? >> And I think they both win. Like for instance, so we didn't do Snowpark integration. But we work with people building data apps on top of Snowflake or data bricks. And what we do is, we can add value to that, or what we've done, again, using all the Supercloud stuff we're done. But we deal with the unstructured data, the four V's coming at you. You can't pipeline that to save. So we actually could be additive. As they're trying to do like a security data cloud inside of Snowflake or do the same thing in Databricks. That's where we can play. Now, we play with them at the application level that they get some data from them and some data for us. But I believe there's a partnership there that will do it inside their environment. To us they're just another large scaler environment that my customers want to get after data. And they want me to abstract it out and give value. >> So it's another repository to you. >> Yeah. >> Okay. So I think Snowflake recently added support for unstructured data. You chose not to do Snowpark because why? >> Well, so the way they're doing the unstructured data is not bad. It's JSON data. Basically, This is the dilemma. Everyone wants their application developers to be flexible, move fast, securely but just productivity. So you get, give 'em flexibility. The problem with that is analytics on the end want to be structured to be performant. And this is where Snowflake, they have to somehow get that raw data. And it's changing every day because you just let the developers do what they want now, in some structured base, but do what you need to do your business fast and securely. So it completely destroys. So they have large customers trying to do big integrations for this messy data. And it doesn't quite work, cause you literally just can't make the pipelines work. So that's where we're complimentary do it. So now, the particular integration wasn't, we need a little bit deeper integration to do that. So we're integrating, actually, at the data app layer. But we could, see us and I don't, listen. I think Snowflake's a good actor. They're trying to figure out what's best for the customers. And I think we just participate in that. >> Yeah. And I think they're trying to figure out >> Yeah. >> how to grow their ecosystem. Because they know they can't do it all, in fact, >> And we solve the key thing, they just can't do certain things. And we do that well. Yeah, I have SQL but that's where it ends. >> Yeah. >> I do the messy data and how to play with them. >> And when you talk to one of their founders, anyway, Benoit, he comes on the cube and he's like, we start with simple. >> Yeah. >> It reminds me of the guy's some Pure Storage, that guy Coz, he's always like, no, if it starts to get too complicated. So that's why they said all right, we're not going to start out trying to figure out how to do complex joins and workload management. And they turn that into a feature. So like you say, I think both can win. It's a big market. >> I think it's a good model. And I love to see Frank, you know, move. >> Yeah. I forgot So you AVMAR... >> In the day. >> You guys used to hate each other, right? >> No, no, no >> No. I mean, it's all good. >> But the thing is, look what he's done. Like I wouldn't bet against Frank. I think it's a good message. You can see clients trying to do it. Same thing with Databricks, same thing with BigQuery. We get a lot of same dynamic in BigQuery. It's good for a lot of things, but it's not everything you need to do. And there's ways for the ecosystem to play together. >> Well, what's interesting about BigQuery is, it is truly cloud native, as is Snowflake. You know, whereas Amazon Redshift was sort of Parexel, it's cobbled together now. It's great engineering, but BigQuery gets a lot of high marks. But again, there's limitations to everything. That's why companies like yours can exist. >> And that's why.. so back to the Supercloud. It allows me as a company to participate in that because I'm leveraging all the underlying pieces. Which we couldn't be doing what we're doing now, without leveraging the Supercloud concepts right, so... >> Ed, I really appreciate you coming by, help me wrap up today in RE:INFORCE. Always a pleasure seeing you, my friend. >> Thank you. >> All right. Okay, this is a wrap on day one. We'll be back tomorrow. I'll be solo. John Furrier had to fly out but we'll be following what he's doing. This is RE:INFORCE 2022. You're watching theCUBE. I'll see you tomorrow.

>> From theCUBE studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> Snowflake Summit 2022 underscored that the ecosystem excitement which was once forming around Hadoop is being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse and good marketing with the data cloud is evolving rapidly with new workloads of vertical industry focus, data applications, monetization, and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis," we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition, and some concerns that remain in many parts of the ecosystem and pockets of customers. First let's look at the overall event. It was held at Caesars Forum. Not my favorite venue, but I'll tell you it was packed. Fire Marshall Full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO Denise Persson on theCUBE describing how this event has evolved. >> Yeah, two, three years ago, we were about 1800 people at a Hilton in San Francisco. We had about 40 partners attending. This week we're close to 10,000 attendees here. Almost 10,000 people online as well, and over over 200 partners here on the show floor. >> Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera but then Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide would say. The headline it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, it probably could have stayed with Cloudera but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slootman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, all theCUBE events that we've done year but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slootman on theCUBE, was this ecosystem evolution by design or did Snowflake just kind of stumble into it? Here's what he said. >> Well, when you are a data clouding, you have data, people want to do things with that data. They don't want just run data operations, populate dashboards, run reports. Pretty soon they want to build applications and after they build applications, they want build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way, you know, we were very, very much focused on data operations. Then it becomes application development and then it becomes, hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many ways. >> So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, an open source version of that will come out but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. Now, I'm not going to do a deep dive on the announcements. Matt Sulkins from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called hybrid tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle and transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this for example, with MySQL HeatWave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added sequel, which was kind of interesting. Here's what Constellation Research analyst Doug Henschen said about Snowflake's moves into transaction data. Play the clip. >> Well with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. >> And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its stream lit acquisition, enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake is built in. Hence that Facebook analogy, or maybe the App Store, the Apple App Store as I propose as well. Python support also widens the aperture for machine intelligence workloads. We asked Snowflake senior VP of product, Christian Kleinerman which announcements he thought were the most impactful. And despite the who's your favorite child nature of the question, he did answer. Here's what he said. >> I think the native applications is the one that looks like, eh, I don't know about it on the surface but he has the biggest potential to change everything. That's create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >> Snowflake also announced support for Apache Iceberg, which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness, and they're building optionality into their cloud. They also showed some cost op optimization tools both from Snowflake itself and from the ecosystem, notably Capital One which launched a software business on top of Snowflake focused on optimizing cost and eventually the rollout data management capabilities, and all kinds of features that Snowflake announced that the show around governance, cross cloud, what we call super cloud, a new security workload, and they reemphasize their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Menninger, Sanjeev Mohan, and Tony Bear, roll the clip. >> Our research shows that the majority of organizations, the majority of people do not have access to analytics. And so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark and support for Python and other languages is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data, they're doing a job and they need analytics in that job. >> Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started but it's usually beneficial to the customers, to the users because now if you have large amount of data in paket files you can leave it on S3, but then you using the Apache Iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. And in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table and you can do store procedure, user defined function. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache Iceberg in a raw format, they don't have it, but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking, but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming. Although, you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. >> I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, and I think part of it, this kind of plays into it is what makes this different from the Hadoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to put this native. Now, obviously Snowflake acquired Streamlit. So we can expect that the Streamlit capabilities are going to be native. >> I want to share a little bit about the higher level thinking at Snowflake, here's a chart from Frank Slootman's keynote. It's his version of the modern data stack, if you will. Now, Snowflake of course, was built on the public cloud. If there were no AWS, there would be no Snowflake. Now, they're all about bringing data and live data and expanding the types of data, including structured, we just heard about that, unstructured, geospatial, and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PaaS layer. We call it a SuperPaaS layer, if you will, to attract application developers. Snowflake has a developer-focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail, monetization. We always talk about building data products and we saw a lot of that at this event, very, very impressive and unique. Now here's the thing. There's a lot of talk in the press, in the Wall Street and the broader community about consumption-based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary. But if you're a company building apps in Snowflake and monetizing like Capital One intends to do, and you're now selling in the marketplace, that is not discretionary, unless of course your costs are greater than your revenue for that service, in which case is going to fail anyway. But the point is we're entering a new error where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud the defacto place as to where you're going to build them. In our view they're well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps, this was a constant refrain and one that certainly makes sense from a physics point of view. But having a single source of data that is discoverable, sharable and governed with increasingly robust ecosystem options, it doesn't have to be moved. Sometimes it may have to be moved if you're going across regions, but that's unique and a differentiator for Snowflake in our view. I mean, I'm yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail, and media, all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go to market focus on verticals. It's really an attempt, and he talked about this in his keynote to align with the customer mission ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic. And I'll say this, if you map the seven pillars Snowflake talks about, Benoit Dageville talked about this in his keynote, but if you map those into Zhamak Dehghani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data, well, maybe with hybrid tables that becomes more of a reality. Global architecture means the data is globally distributed. It's not necessarily physically in one place. Self-managed is key. Self-service infrastructure is one of Zhamak's four principles. And then inherent governance. Zhamak talks about computational, what I'll call automated governance, built in. And with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit and to its credit, by the way, Snowflake doesn't use data mesh in its messaging anymore. But by the way, its customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Zhamak Dehghani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, supercloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to spin multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing but now it uses the term collaboration in its high level messaging, which is I think smart. Data sharing is kind of a geeky term. And also this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. And finally Snowflake doesn't say data marketplace anymore. It's now marketplace, accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR X-Y graph. Vertical access remembers net score or spending momentum and the x-axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guide it conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels but I wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor obviously at a strong spending momentum, as you can see. We didn't draw it here but we usually draw that 40% line or red line at 40%, anything above that is considered elevated. So you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble and it doesn't have nearly as deep and capable go-to market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless. But as a private company, you just naturally, more people are aware of Snowflake. Some analysts, Tony Bear in particular, believe Mongo and Snowflake are on a bit of a collision course long term. I actually can see his point. You know, I mean, they're both platforms, they're both about data. It's long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions even though they're quite in different markets today but they're definitely participating in similar tam. The cloud players are probably the biggest or definitely the biggest partners and probably the biggest competitors to Snowflake. And then there's always Oracle. Doesn't have the spending velocity of the others but it's got strong market presence. It owns a cloud and it knows a thing about data and it definitely is a go-to market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. 'Cause look, we've heard before how particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera. We're going to solve all of our data problems and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created. Like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard firsthand from some customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of Snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah this is all well and good but I still need skilled people to understand all these tools that I'm integrated in the ecosystem, the catalogs, the machine learning observability. A number of customers said, I just can't use one governance tool, I need multiple governance tools and a lot of other technologies as well, and they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value you know, when Snowflake was just a better data warehouse. But to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast, you got to keep innovating. If you don't, you're going to be left. Either if your customer you're going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine, new bottle. Well, this time around I certainly hope not given all the energy and investment that is going into this ecosystem. And the fact is Snowflake is unquestionably making it easier to put data to work. They built on AWS so you didn't have to worry about provisioning, compute and storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton so you don't have to, and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. And firm belief is the less expensive it is, the more data will get brought into the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, aka data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable, and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop zoo. Okay, that's it for today, thanks for watching. Thanks to my colleague, Stephanie Chan who helps research "Breaking Analysis" topics. Sometimes Alex Myerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters, and Rob Hof is our editor in chief over at Silicon, and Hailey does some wonderful editing, thanks to all. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com and you can email me at David.Vellante@siliconangle.com or DM me @DVellante. If you got something interesting, I'll respond. If you don't, I'm sorry I won't. Or comment on my LinkedIn post. Please check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time. (upbeat music)

>>Welcome back to the big apple, everybody. The Cube's continuous coverage here of MongoDB world 2022. We're at the new Javet center. It's it's quite nice. It was built during the pandemic. I believe on top of a former bus terminal. I'm told by our next guest Tony bear, who's the principal at DB insight of data and database expert, longtime analyst, Tony. Good to see you. Thanks for coming >>On. Thanks >>For having us. You face to face >>And welcome to New York. >>Yeah. Right. >>New York is open for business. >>So, yeah. And actually, you know, it's interesting. We've been doing a lot of these events lately and, and especially the ones in Vegas, it's the first time everybody's been out, you know, face to face, not so much here, you know, people have been out and about a lot of masks >>In, >>In New York city, but, but it's good. And, and this new venue is fantastic >>Much nicer than the old Javits. >>Yeah. And I would say maybe 3000 people here. >>Yeah. Probably, but I think like most conferences right now are kind of, they're going through like a slow ramp up. And like for instance, you know, sapphires had maybe about one third, their normal turnout. So I think that you're saying like one third to one half seems to be the norm right now are still figuring out how we're, how and where we're gonna get back together. Yeah. >>I think that's about right. And, and I, but I do think that that in most of the cases that we've seen, it's exceeded people's expectations at tenants, but anyway sure. Let's talk about Mongo, very interesting company. You know, we've been kind of been watching their progression from just sort of document database and all the features and functions they're adding, you just published a piece this morning in venture beat is time for Mongo to get into analytics. Yes. You know? Yes. One of your favorite topics. Well, can they expand analytics? They seem to be doing that. Let's dig into it. Well, >>They're taking, they've been taking slow. They've been taking baby steps and there's good reason for that because first thing is an operational database. The last thing you wanna do is slow it down with very complex analytics. On the other hand, there's huge value to be had if you would, if you could, you know, turn, let's say a smart, if you can turn, let's say an operational database or a transaction database into a smart transaction database. In other words, for instance, you know, let's say if you're, you're, you're doing, you know, an eCommerce site and a customer has made an order, that's basically been out of the norm. Whether it be like, you know, good or bad, it would be nice. Basically, if at that point you could then have a next best action, which is where analytics comes in. But it's a very lightweight form of analytics. It's not gonna, it's actually, I think probably the best metaphor for this is real time credit scoring. It's not that they're doing your scoring you in real time. It's that the model has been computed offline so that when you come on in real time, it can make a smart decision. >>Got it. Okay. So, and I think it was your article where I, I wrote down some examples. Sure. Operational, you know, use cases, patient data. There's certainly retail. We had Forbes on earlier, right? Obviously, so very wide range of, of use cases for operational will, will Mongo, essentially, in your view, is it positioned to replace traditional R D BMS? >>Well, okay. That's a long that's, that's much, it's >>Sort of a loaded question, but >>That's, that's a very loaded question. I think that for certain cases, I think it will replace R D BMS, but I still, I mean, where I, where I depart from Mongo is I do not believe that they're going to replace all R D D BMSs. I think, for instance, like when you're doing financial transactions, you know, the world has been used to table, you know, you know, columns and rows and tables. That's, it's a natural form for something that's very structured like that. On the other hand, when you take a look, let's say OT data, or you're taking a look at home listings that tends to more naturally represent itself as documents. And so there's a, so it's kind of like documents are the way that let's say you normally see the world. Relational is the way that you would structure the world. >>Okay. Well, I like that. So, but I mean, in the early days, obviously, and even to this day, it's like the target for Mongo has been Oracle. Yeah. Right, right. And so, and then, you know, you talk to a lot of Oracle customers as do I sure. And they are running the most mission, critical applications in the world, and it's like banking and financial and so many. And, and, and, you know, they've kind of carved out that space, but are we, should we be rethinking the definition of, of mission critical? Is that changing? >>Well, number one, I think what we've traditionally associated mission critical systems with is our financial transaction systems and to a less, and also let's say systems that schedule operations. But the fact is there are many forms of operations where for instance, let's say you're in a social network, do you need to have that very latest update? Or, you know, basically, can you go off, let's say like, you know, a server that's eventually consistent. In other words, the, do you absolutely have, you know, it's just like when you go on Twitter, do you naturally see all the latest tweets? It's not the system's not gonna crash for that reason. Whereas let's say if you're doing it, you know, let's say an ATM banking ATM system, that system better be current. So I think there's a delineation. The fact is, is that in a social network, arguably that operational system is mission critical, but it's mission critical in a different way from a, you know, from, let's say a banking system. >>So coming back to this idea of, of this hybrid, I think, you know, I think Gartner calls it H tab hybrid, transactional analytics >>Is changed by >>The minute, right. I mean, you mentioned that in, in your article, but basically it's bringing analytics to transactions bringing those, those roles together. Right. Right. And you're saying with Mongo, it's, it's lightweight now take, you use two other examples in your article, my SQL heat wave. Right. I think you had a Google example as well, DB, those are, you're saying much, much heavier analytics, is that correct? Or >>I we'll put it this way. I think they're because they're coming from a relational background. And because they also are coming from companies that already have, you know, analytic database or data warehouses, if you will, that their analytic, you know, capabilities are gonna be much more fully rounded than what Mongo has at this point. It's not a criticism of a Mongo MongoDB per >>Per, is that by design though? Or ne not necessarily. Is that a function of maturity? >>I think it's function of maturity. Oh, okay. I mean, look, to a certain extent, it's also a function of design in terms of that the document model is a little, it's not impossible to basically model it for analytics, but it takes more, you know, transformation to, to decide which, you know, let's say field in that document is gonna be a column. >>Now, the big thing about some of these other, these hybrid systems is, is eliminating the need for two databases, right? Eliminating the need for, you know, complex ETL. Is, is that a value proposition that will emerge with, with Mongo in your view? >>You know, I, I mean, put it this way. I think that if you take a look at how they've, how Mongo is basically has added more function to its operations, someone talking about analytics here, for instance, adding streaming, you know, adding, adding, search, adding time series, that's a matter of like where they've eliminated the need to do, you know, transformation ETL, but that's not for analytics per se for analytics. I think through, you know, I mean through replication, there's still gonna be some transformation in terms of turning, let's say data, that's, that's formed in a document into something that's represented by columns. There is a form of transformation, you know, so that said, and Mongo is already, you know, it has some NA you know, nascent capability there, but it's all, but this is still like at a rev 1.0 level, you know, I expect a lot more >>Of so refin you, how Amazon says in the fullness of time, all workloads will be in the cloud. And we could certainly debate that. What do we mean by cloud? So, but there's a sort of analog for Mongo that I'll ask you in the fullness of time, will Mongo be in a position to replace data warehouses or data lakes? No. Or, or, or, and we know the answer is no. So that's of course, yeah. But are these two worlds on a quasi collision course? I think they >>More on a convergence course or the collision course, because number one is I said, the first principle and operational database is the last thing you wanna do is slow it down. And to do all this complex modeling that let's say that you would do in a data bricks, or very complex analytics that you would do in a snowflake that is going to get, you know, you know, no matter how much you partition the load, you know, in Atlas, and yes, you can have separate nodes. The fact is you really do not wanna burden the operational database with that. And that's not what it's meant for, but what it is meant for is, you know, can I make a smart decision on the spot? In other words, kinda like close the loop on that. And so therefore there's a, a form of lightweight analytic that you can perform in there. And actually that's also the same principle, you know, on which let's say for instance, you know, my SQL heat wave and Allo DBR based on, they're not, they're predicated on, they're not meant to replace, you know, whether it be exit data or big query, the idea there is to do more of the lightweight stuff, you know, and keep the database, you know, keep the operations, you know, >>Operating. And, but from a practitioner's standpoint, I, I, I can and should isolate you're saying that node, right. That's what they'll do. Sure. How does that affect cuz my understanding is that that the Mon Mongo specifically, but I think document databases generally will have a primary node. Right? And then you can set up secondary nodes, which then you have to think about availability, but, but would that analytic node be sort of fenced off? Is that part of the >>Well, that's actually what they're, they've already, I mean, they already laid the groundwork for it last year, by saying that you can set up separate nodes and dedicate them to analytics and what they've >>As, as a primary, >>Right? Yes, yes. For analytics and what they've added, what they're a, what they are adding this year is the fact to say like that separate node does not have to be the same instance class, you know, as, as, as, as the, >>What, what does that mean? Explain >>That in other words, it's a, you know, you could have BA you know, for instance, you could have a node for operations, that's basically very eye ops intensive, whereas you could have a node let's say for analytics that might be more compute intensive or, or more he, or, or more heavily, you know, configured with, with memory per se. And so the idea here is you can tailor in a node to the workload. So that's, you know what they're saying with, you know, and I forget what they're calling it, but the idea that you can have a different type, you can specify a different type of node, a different type of instance for the analytic node, I think is, you know, is a major step forward >>And that, and that that's enabled by the cloud and architecture. >>Of course. Yes. I mean, we're separating, compute from data is, is, is the starter. And so yeah. Then at that point you can then start to, you know, you know, to go less vanilla. I think, you know, the re you know, the, you know, the, I guess the fruition of this is going to be when they say, okay, you can run your, let's say your operational nodes, you know, dedicated, but we'll let you run your analytic nodes serverless. Can't do it yet, but I've gotta believe that's on the roadmap. >>Yeah. So seq brings a lot of overhead. So you get MQL, but now square this circle for me, cuz now you got Mago talking sequel. >>They had to start doing that some time. I mean, and I it's been a court take I've had from them from the, from the get go, which I said, I understand that you're looking at this as an alternative to SQL and that's perfectly valid, but don't deny the validity of SQL or the reason why we, you know, we need it. The fact is that you have, okay, the number, you know, according to Ty index, JavaScript is the seventh, most popular language. Most SQL follows closely behind at the ninth, most popular language you don't want to cl. And the fact is those people exist in the enterprise and they're, and they're disproportionately concentrated in analytics. I mean, you know, it's getting a little less, so now we're seeing like, you know, basically, you know, Python, the programmatic, but still, you know, a lot of sequel expertise there. It does not make, it makes no sense for Mongo to, to, to ignore or to overlook that audience. I think now they're, you know, you know, they're taking baby steps to start, you know, reaching out to them. >>It's interesting. You see it going both ways. See Oracle announces a Mongo, DB, Mongo. I mean, it's just convergence. You called it not, I love collisions, you know, >>I know it's like, because you thrive on drama and I thrive on can't. We all love each other, but you know, act. But the thing is actually, I've been, I wrote about this. I forget when I think it was like 2014 or 2016. It's when we, I was noticed I was noting basically the, you know, the rise of all these specialized databases and probably Amazon, you know, AWS is probably the best exemplar of that. I've got 15 or 16 or however, number of databases and they're all dedicated purpose. Right. But I also was, you know, basically saw that inevitably there was gonna be some overlap. It's not that all databases were gonna become one and the same we're gonna be, we're gonna become back into like the, you know, into a pan G continent or something like that. But that you're gonna have a relational database that can do JSON and, and a, and a document database that can do relational. I mean, you know, it's, to me, that's a no brainer. >>So I asked Andy Ja one time, I'd love to get your take on this, about those, you know, multiple data stores at the time. They probably had a thousand. I think they're probably up to 15 now, right? Different APIs, different S et cetera. And his response. I said, why don't you make it easier for, for customers and maybe build an abstraction or converge these? And he said, well, it's by design. What if you buy this? And, and what your thoughts are, cuz I, you know, he's a pretty straight shooter. Yeah. It's by design because it allows us as the market moves, we can move with it. And if we, if we give developers access to those low level primitives and APIs, then they can move with, with at market speed. Right. And so that again, by design, now we heard certainly Mongo poo pooing that today they didn't mention, they didn't call out Amazon. Yeah. Oracle has no compunction about specifically calling out Amazon. They do it all the time. What do you make of that? Can't Amazon have its cake and eat it too. In other words, extend some of the functionality of those specific databases without going to the Swiss army. >>I I'll put it this way. You, you kind of tapped in you're, you're sort of like, you know, killing me softly with your song there, which is that, you know, I was actually kind of went on a rant about this, actually know in, you know, come, you know, you know, my year ahead sort of out predictions. And I said, look, cloud folks, it's great that you're making individual SAS, you know, products easy to use. But now that I have to mix and match SAS products, you know, the burden of integration is on my shoulders. Start making my life easier. I think a good, you know, a good example of this would be, you know, for instance, you could take something like, you know, let's say like a Google big query. There's no reason why I can't have a piece of that that might, you know, might be paired, say, you know, say with span or something like that. >>The idea being is that if we're all working off a common, you know, common storage, we, you know, it's in cloud native, we can separate the computer engines. It means that we can use the right engine for the right part of the task. And the thing is that maybe, you know, myself as a consumer, I should not have to be choosing between big query and span. But the thing is, I should be able to say, look, I want to, you know, globally distribute database, but I also wanna do some analytics and therefore behind the scenes, you know, new microservices, it could connect the two wouldn't >>Microsoft synapse be an example of doing that. >>It should be an example. I wish I, I would love to hear more from Microsoft about this. They've been radio silent for about the past two or three years in data. You hardly hear about it, but synapse is actually those actually one of the ideas I had in mind now keep in mind that with synapse, you're not talking about, let's say, you know, I mean, it's, it's obviously a sequel data warehouse. It's not pure spark. It's basically their, it was their curated version of spark, but that's fine. But again, I would love to hear Microsoft talk more about that. They've been very quiet. >>Yeah. You, you, the intent is there to >>Simplify >>It exactly. And create an abstraction. Exactly. Yeah. They have been quiet about it. Yeah. Yeah. You would expect that, that maybe they're still trying to figure it out. So what's your prognosis from Mongo? I mean, since this company IP, you know, usually I, I tell and I tell everybody this, especially my kids, like don't buy a stock at IPO. You'll always get a better chance at a cheaper price to buy it. Yeah. And even though that was true with Mongo, you didn't have a big window. No. Like you did, for instance, with, with Facebook, certainly that's been the case with snowflake and sure. Alibaba, I mean, I name a zillion style was almost universal. Yeah. But, but since that, that, that first, you know, few months, period, this, this company has been on a roll. Right. And it, it obviously has been some volatility, but the execution has been outstanding. >>No question about that. I mean, the thing is, look what I, what I, and I'm just gonna talk on the product side on the sales side. Yeah. But on the product side, from the get go, they made a product that was easy for developers. Whereas let's say someone's giving an example, for instance, Cosmo CB, where to do certain operations. They had to go through multiple services in, you know, including Azure portal with Atlas, it's all within Atlas. So they've really, it's been kinda like design thinking from the start initially with, with the core Mongo DB, you know, you, the on premise, both this predates Atlas, I mean, part of it was that they were coming with a language that developers knew was just Javas script. The construct that they knew, which was JS on. So they started with that home core advantage, but they weren't the only ones doing that. But they did it with tooling that was very intuitive to developers that met developers, where they lived and what I give them, you know, then additional credit for is that when they went to the cloud and it wasn't an immediate thing, Atlas was not an overnight success, but they employed that same design thinking to Atlas, they made Atlas a good cloud experience. They didn't just do a lift and shift the cloud. And so that's why today basically like five or six years later, Atlas's most of their business. >>Yeah. It's what, 60% of the business now. Yeah. And then Dave, on the, on the earning scholar, maybe it wasn't Dave and somebody else in response to question said, yeah, ultimately this is the future will be be 90% of the business. I'm not gonna predict when. So my, my question is, okay, so let's call that the midterm midterm ATLA is gonna be 90% of the business with some exceptions that people just won't move to the cloud. What's next is the edge. A new opportunity is Mongo architecturally suited for the, I mean, it's certainly suited for the right, the home Depot store. Sure. You know, at the edge. Yeah. If you, if you consider that edge, which I guess it is form of edge, but how about the far edge EVs cell towers, you know, far side, real time, AI inferencing, what's the requirement there, can Mongo fit there? Any thoughts >>On that? I think the AI and the inferencing stuff is interesting. It's something which really Mongo has not tackled yet. I think we take the same principle, which is the lightweight stuff. In other words, you'll say, do let's say a classification or a prediction or some sort of prescriptive action in other words, where you're not doing some convolution, neural networking and trying to do like, you know, text, text to voice or, or, or vice versa. Well, you're not trying to do all that really fancy stuff. I think that's, you know, if you're keeping it SIM you know, kinda like the kiss principle, I think that's very much within Mongo's future. I think with the realm they have, they basically have the infrastructure to go out to the edge. I think with the fact that they've embraced GraphQL has also made them a lot more extensible. So I think they certainly do have, you know, I, I do see the edge as being, you know, you know, in, in, you know, in their, in their pathway. I do see basically lightweight analytics and lightweight, let's say machine learning definitely in their >>Future. And, but, and they would, would you agree that they're in a better position to tap that opportunity than say a snowflake or an Oracle now maybe M and a can change that. R D can maybe change that, but fundamentally from an architectural standpoint yeah. Are they in a better position? >>Good question. I think that that Mongo snowflake by virtual fact, I mean that they've been all, you know, all cloud start off with, I think makes it more difficult, not impossible to move out to the edge, but it means that, and I, and know, and I, and I said, they're really starting to making some tentative moves in that direction. I'm looking forward to next week to, you know, seeing what, you know, hearing what we're gonna, what they're gonna be saying about that. But I do think, right. You know, you know, to answer your question directly, I'd say like right now, I'd say Mongo probably has a, you know, has a head start there. >>I'm losing track of time. I could go forever with you. Tony bear DB insight with tons of insights. Thanks so much for coming back with. >>It's only one insight insight, Dave. Good to see you again. All >>Right. Good to see you. Thank you. Okay. Keep it right there. Right back at the Java center, Mongo DB world 2022, you're watching the cube.

>>Welcome back to the cubes coverage of H P S. Green Lake announcement. We've been following Green Lake and the cadence of announcements making. Now we're gonna talk about ransomware, ransomware become a household term. But what people really don't understand is that virtually any bad actor can become a ransomware criminal by going on the dark web hiring a ransomware as a service sticking, putting a stick into a server and taking a piece of the action and that is a really insidious threat. Uh, the adversaries are extremely capable, so we're going to dig into that with Omar assad, who's the storage platform, lead cloud data services at H P E and Deepak verma vice president of product Zito, which is now an H P E company Gentlemen, welcome to the cube. Good to see you. Thank you. >>Thank you. Welcome. Pleasure to be here. So >>over you heard my little narrative upfront. How does the Xarelto acquisition fit into that discourse? >>Thank you. Dave first of all, we're extremely excited to welcome Sir toe into the HP family. Uh, the acquisition of Puerto expands the Green Lake offerings from H P E uh, into the data protection as a service and ransomware protection as a service capabilities and it at the same time accelerates the transformation that the HP storage businesses going through as it transforms itself into more of a cloud native business, which sort of follows on from the May 4th announcements that you helped us cover. Uh, this enables the HP sales teams to now expand the data protection perimeter and to start offering data protection as a service and ransomware as a service with the best in class technologies uh, from a protection site as well as from ransomware recovery side of the house. And so we're all the way down already trying to integrate uh, you know, the little offerings as part of the Green lake offerings and extending support through our services organization. And the more of these announcements are gonna roll out later in the month. >>And I think that's what you want to see from it as a service offering. You want to see a fast cadence of new services that are not a box by a box that are applying. No, it's services that you want to access. So let's, let's talk about before we get into the tech, can we talk about how you're helping customers deal with ransomware? Maybe some of the use cases that you're seeing. >>First of all, extremely excited to be part of the HP family now. Um, Quick history and that we've been around for about 11 years. We've had about 9000 plus customers and they all benefit from essentially the same technology that we invented 11 years ago. First and foremost, one of the use cases has been continuous data protection. So were built on the CdP platform, which means extremely low RTO S and R P O S for recovery. I'll give you example there um, United Airlines is an application that cost them $1 million dollars for every hour that they're down. They use traditional approaches. That would be a lot of loss with Zito, we have that down two seconds of loss in case and the application goes down. So that's kind of core and fundamental to our plaque. The second uh critical use case that for us has been simplicity. A lot of customers have said we make the difficult, simple. So DRS is a complex uh process. Um, give you an example there. Hcea Healthcare Consolidated four different disaster recovery platforms into a single platform in Puerto and saved about $10 million dollars a year. So it's making that operations of having disaster recovery process is much simpler. Um the third kind of critical use case for us as uh, the environment has evolved as the landscape has involved has been around hybrid cloud. So being able to take customers to the platforms that they want to go to that's critical for us And for our customers an example, there is Kingston technology's so Kingston tried some competitive products to move to Azure, it would take them about 24 hours to recover 30 VMS or so with zero technology. They will get about all their 1000 VMS up in Azure instantaneously. So these are three use cases that were foundational. Built. Built the company in the tech. >>Nice. Thank you. Thank you for that. So simple works well these days, especially with all this complexity we have to deal with. Can we get into the secret sauce a little bit. I mean CdP has been around forever. What do you guys do that? That's different. Maybe you can talk about that. Sure. >>Um it's cdp based, I think we've perfected the technology. It's less about being able to just copy the data. It's more about what you do when things go bump. We've made it simpler with driven economies of scale lower and being platform agnostic. We've really brought that up across to whatever platforms once upon a time it was moving from physical to virtual or even across different virtualization platforms and then being able to move across to whatever cloud platform customer may want or or back >>to cbP continuous data protection by the way for the audience that may not know that go ahead. And >>one of the additional points that I want to add to the box comment over here is the the basics of platform independence is what really drew uh hp technologists into the technology because you know, one of the things we have many, we have the high end platform with the H B electra nine Kv of the electro six kids the midrange platform. Then we have a bunch of file and object offerings on the side. What zero does it University universally applies to all those technologies and along with, you know, as you pair them up with our computer offerings to offer a full stack but now the stack is disaster recovery capable. Natively with the integration of certo, you know, one of the things that, you know, Deepak talked about about the as your migrations that a lot of the customers are talking about cloud is also coming up as a D our use case for a lot of our customers, customers, you know, you know, as we went through thousands of customers interviews one of the, one of the key things that came back was investing in a D our data center which is just waiting there for a disaster to happen. It's a very expensive insurance policy. So absurd. Oh, through its native capabilities allows customers to do is to just use public cloud as a D our target and and as a service, it just takes care of all the format conversions and recoveries and although that's completely automated inside the platform and and we feel that, you know, when you combine this either at the high end of data center storage offering or the middle age offering with this replication, D. R. And ransomware protection built into the same package, working under the same hood, it just simplifies and streamlines the customers deployment. >>Come here a couple of things. So first of all historically, if you wanted to recover to appoint within let's say, you know, 10 seconds, five seconds you have to pay up. Big time. Number one. Number two is you couldn't test your D. R. It was too risky. So people just had it in, they had a checkbox on compliance but they actually couldn't really test it because they were afraid they were going to lose data. So it sounds like you're solving both of those problems or >>or you know we remember the D. R. Test where it was a weekend. It was an event right? It was the event and at the end of july that the entire I. T. Organizing honey >>it's not gonna be home this weekend. Exactly what >>we've changed. That is a click of a button. You can D. R. Test today if you want to you can have disaster recovery still running. You can D. R. Test in Azure bring up your environment an isolated network bubble, make sure everything's running and bring it and bring it down. The interesting thing is the technology was invented back when our fear in the industry was losing a data center was losing power was catastrophic, natural disasters. But the technology has lent itself very well to the new threats which which are very much around ransomware as you mentioned because it's a type of disaster. Somebody's going after your data. Physical servers are still around but you still need to go back to a point in time and you need to do that very quickly. So the technology has really just found itself uh appealing to new challenges. >>If a customer asks you can I really eliminate cyber attacks, where should I put my my if I had 100 bucks to spend. Should I spend it on you know layers and defense should I spend it on recovery. Both, what would you tell them? >>I think it's a balanced answer. I think prevention is 100% impossible. Uh It's really I'd say spend it in in thirds. You want to spend a third of it and and prevention a third of it maybe in detection and then a third of it in uh recovery. So it's really that balancing act that means you can't leave the front door open but then have a lot of recovery techniques invested in. It has to be it has to be a balance and it's also not a matter of if it's a matter of when so we invest in all three areas. Hopefully two of them will work to your advantage. >>You dave you you should always protect your perimeter. I mean that that goes without saying but then as you invest in other aspects of the business, as Deepak mentioned, recovery needs to be fast and quick recovery whether from your recovering from a backup disaster. Are you covering from a data center disaster a corrupted file or from a ransomware attack. A couple of things that zero really stitches together like journal based recovery has been allowed for a while but making journal based recovery platform independent in a seamless fashion with the click of a button within five seconds go back to where your situation was. That gives you the peace of mind that even if the perimeter was breached, you're still protected, you know, five minutes into the problem And, and that's the peace of mind, which along with data protection as a service, disaster recovery as a service and now integrating this, you know, recovery from ransomware along with it in a very simple, easy to consume package is what drew us into the >>more you can do this you said on the use the cloud as a target. I could use the cloud as an air gap if I wanted to. It sounds like it's cloud Native, correct? Just wrap your stack in kubernetes and shove it in the cloud and have a host and say we're cloud to No, really I'm serious. So >>absolutely, we we looked at that approach and that that's where the challenge comes in, Right? So I give you the example of Kingston technology just doesn't scale, it's not fast enough. What we did was developed a platform for cloud Native. We consume cloud services where necessary in order to provide that scalability. So one example in Azure is being able to use scale set. So think about a scenario where you just declare a disaster, you've got 1000 VMS to move over, we can spin up the workers that need to do the work to get 1000 VMS spin them down. So you're up and running instantaneously and that involves using cloud Native uh tools and technologies, >>can we stay on that for a minute, So take take us through an example of what life was like would be like without zero trying to recover and what it's like with Puerto resources, complexity time maybe you could sort of paint a picture. Sure. >>Let me, I'll actually use an example from a customer 10 Kata. They uh develop defensive fabrics, especially fabric. So think about firefighters, think about our men and women abroad that need protective clothing that developed the fibers behave. They were hit by ransomware by crypto locker. That this was before zero. Unfortunately it took they took about a two week uh data loss. It took them weeks to recover that environment, bring it back up and the confidence was pretty low. They invested in, they looked at our technology, they invested in the technology and then they were hit with a different variant of crypto locker immediately. The the IT administrators and the ITS folks there were relieved right, they had a sense of confidence to say yes we can recover. And the second time around they had data loss of about 10 seconds, they could recover within a few minutes. So that's the before and after picture giving customers that confidence to say yep, a breach happened, we tried our best but now it's up to recovery and I can recover without having to dig tapes out from some vault and hopefully have a good copy of data sitting there and then try that over and over again and there's a tolerance right before a time before which business will not be able to sustain itself. So what we want to do is minimize that for businesses so that they can recover as quickly as possible with as little data loss as possible. >>Thank you for that. So, Omar, there's a bigger sort of cyber recovery agenda that you have as part of, of green lake, I'm sure. What, what should we expect, what's next? Where do you want to take this? >>So uh excellent question point in the future day. So one of the things that you helped us, uh you know, unveil uh in May was the data services. Cloud console. Data services. Cloud console was the first uh sort of delivery as we took the storage business as it is and start to transform into more of a cloud native business. We introduced electra uh which is the cloud native hardware with the customers buy for persistent storage within their data center. But then data services, cloud console truly cemented that cloud operational model. Uh We separated the management from, from the devices itself and sort of lifted it up as a sas service into the public, public cloud. So now what you're gonna see is, you know, more and more data and data management services come up on the data services. Cloud console and and zero is going to be one of the first ones. Cloud physics was another one that we we talked about, but zero is the is the true data management service that is going to come up on data services, cloud console as part of the Green Lake services agenda that that HP has in the customer's environ and then you're gonna see compliance as a service. You're going to see data protection as a service. You're gonna see disaster recovery as a service. But the beautiful thing about it is, is choice with simplicity as these services get loaded up on data services, clown console. All our customers instantly get it. There's nothing to install, there's nothing to troubleshoot uh, there's nothing to size. All those capabilities are available on the console, customers go in and just start consuming Xarelto capabilities from a management control plane, Disaster recovery control plan are going to be available on the data services, cloud console, automatically detecting electro systems, rian Bear systems, container based systems, whichever our customers have deployed and from there is just a flip of a button. Another way to look at it is it sort of gives you that slider that you have data protection or back up on one side, you've got disaster recovery on one side, you've got ransomware protection on on the extreme right side, you can just move a slider across and choose the service level that you want without worrying about best practices, installation, application integration. All of that just takes control from the data services, cloud concepts. >>Great, great summary because historically you would have to build that right now. You can buy it as a service. You can programmatically, you know, deploy it and that's a game changer. Have to throw it over the fence to some folks. That's okay. Now, you know, make it make it work and then they change the code and you come back a lot of finger pointing. It's now it's your responsibility. >>Absolutely. Absolutely. We're excited to provide Zito continue provides the desert of customers but also integrate with the Green Green Lake platform and let the rest of Green Lake customers experience some of the sort of technology and really make that available as a service. >>That's great. This is a huge challenge for customers. I mean they do, I pay their ransom. Do not pay the ransom. If I pay the ransom the FBI is going to come after me. But if I don't pay the ransom, I'm not gonna get the crypto key. So solutions like this are critical. You certainly see the president pushing for that. The United States government said, hey, we got to do a better job. Good job guys, Thanks for for sharing your story in the cube and congratulations. Thank >>you. Thank you David. >>All right. And thank you for watching everybody. Uh this is the, I want to tell you that everything that you're seeing today as part of the Green Lake announcement is going to be available on demand as part of the HP discover more. So you got to check that out. Thank you. You're watching the cube. >>Mhm mm.

>> Paige: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled, Vertica next generation architecture. I'm Paige Roberts, open social relationship Manager at Vertica, I'll be your host for this session. And joining me is Vertica Chief Architect, Chuck Bear, before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment, in the question box that's below the slides and click submit. So as you think about it, go ahead and type it in, there'll be a Q&A session at the end of the presentation, where we'll answer as many questions, as we're able to during the time. Any questions that we don't get a chance to address, we'll do our best to answer offline. Or alternatively, you can visit the Vertica forums to post your questions there, after the session. Our engineering team is planning to join the forum and keep the conversation going, so you can, it's just sort of like the developers lounge would be in delight conference. It gives you a chance to talk to our engineering team. Also, as a reminder, you can maximize your screen by clicking the double arrow button in the lower right corner of the slide. And before you ask, yes, this virtual session is being recorded, and it will be available to view on demand this week, we'll send you a notification, as soon as it's ready. Okay, now, let's get started, over to you, Chuck. >> Chuck: Thanks for the introduction, Paige, Vertica vision is to help customers, get value from structured data. This vision is simple, it doesn't matter what vertical the customer is in. They're all analytics companies, it doesn't matter what the customers environment is, as data is generated everywhere. We also can't do this alone, we know that you need other tools and people to build a complete solution. You know our database is key to delivering on the vision because we need a database that scales. When you start a new database company, you aren't going to win against 30 year old products on features. But from day one, we had something else, an architecture built for analytics performance. This architecture was inspired by the C-store project, combining the best design ideas from academics and industry veterans like Dr. Mike Stonebreaker. Our storage is optimized for performance, we use many computers in parallel. After over 10 years of refinements against various customer workloads, much of the design held up and serendipitously, the fact that we don't store in place updates set Vertica up for success in the cloud as well. These days, there are other tools that embody some of these design ideas. But we have other strengths that are more important than the storage format, where the only good analytics database that runs both on premise and in the cloud, giving customers the option to migrate their workloads, in most convenient and economical environment, or a full data management solution, not just the query tool. Unlike some other choices, ours comes with integration with a sequel ecosystem and full professional support. We organize our product roadmap into four key pillars, plus the cross cutting concerns of open integration and performance and scale. We have big plans to strengthen Vertica, while staying true to our core. This presentation is primarily about the separation pillar, and performance and scale, I'll cover our plans for Eon, our data management architecture, Mart analytic clusters, or fifth generation query executer, and our data storage layer. Let's start with how Vertica manages data, one of the central design points for Vertica was shared nothing, a design that didn't utilize a dedicated hardware shared disk technology. This quote here is how Mike put it politely, but around the Vertica office, shared disk with an LMTB over Mike's dead body. And we did get some early field experience with shared disk, customers, well, in fact will learn on anything if you let them. There were misconfigurations that required certified experts, obscure bugs extent. Another thing about the shared nothing designed for commodity hardware though, and this was in the papers, is that all the data management features like fault tolerance, backup and elasticity have to be done in software. And no matter how much you do, procuring, configuring and maintaining the machines with disks is harder. The software configuration process to add more service may be simple, but capacity planning, racking and stacking is not. The original allure of shared storage returned, this time though, the complexity and economics are different. It's cheaper, even provision storage with a few clicks and only pay for what you need. It expands, contracts and brings the maintenance of the storage close to a team is good at it. But there's a key difference, it's an object store, an object stores don't support the API's and access patterns used by most database software. So another Vertica visionary Ben, set out to exploit Vertica storage organization, which turns out to be a natural fit for modern cloud shared storage. Because Vertica data files are written once and not updated, they match the object storage model perfectly. And so today we have Eon, Eon uses shared storage to hold Vertica data with local disk depot's that act as caches, ensuring that we can get the performance that our customers have come to expect. Essentially Eon in enterprise behave similarly, but we have the benefit of flexible storage. Today Eon has the features our customers expect, it's been developed in tune for years, we have successful customers such as Redpharma, and if you'd like to know more about Eon has helped them succeed in Amazon cloud, I highly suggest reading their case study, which you can find on vertica.com. Eon provides high availability and flexible scaling, sometimes on premise customers with local disks get a little jealous of how recovery and sub-clusters work in Eon. Though we operate on premise, particularly on pure storage, but enterprise also had strengths, the most obvious being that you don't need and short shared storage to run it. So naturally, our vision is to converge the two modes, back into a single Vertica. A Vertica that runs any combination of local disks and shared storage, with full flexibility and portability. This is easy to say, but over the next releases, here's what we'll do. First, we realize that the query executer, optimizer and client drivers and so on, are already the same. Just the transaction handling and data management is different. But there's already more going on, we have peer-to-peer depot operations and other internode transfers. And enterprise also has a network, we could just get files from remote nodes over that network, essentially mimicking the behavior and benefits of shared storage with the layer of software. The only difference at the end of it, will be which storage hold the master copy. In enterprise, the nodes can't drop the files because they're the master copy. Whereas in Eon they can be evicted because it's just the cache, the masters, then shared storage. And in keeping with versus current support for multiple storage locations, we can intermix these approaches at the table level. Getting there as a journey, and we've already taken the first steps. One of the interesting design ideas of the C-store paper is the idea that redundant copies, don't have to have the same physical organization. Different copies can be optimized for different queries, sorted in different ways. Of course, Mike also said to keep the recovery system simple, because it's hard to debug, whenever the recovery system is being used, it's always in a high pressure situation. This turns out to be a contradiction, and the latter idea was better. No down performing stuff, if you don't keep the storage the same. Recovery hardware if you have, to reorganize data in the process. Even query optimization is more complicated. So over the past couple releases, we got rid of non identical buddies. But the storage files can still diverge at the fifth level, because tuple mover operations are synchronized. The same record can end up in different files than different nodes. The next step in our journey, is to make sure both copies are identical. This will help with backup and restore as well, because the second copy doesn't need backed up, or if it is backed up, it appears identical to the deduplication that is going to look present in both backup systems. Simultaneously, we're improving the Vertica networking service to support this new access pattern. In conjunction with identical storage files, we will converge to a recovery system that instantaneous nodes can process queries immediately, by retrieving data they need over the network from the redundant copies as they do in Eon day with even higher performance. The final step then is to unify the catalog and transaction model. Related concepts such as segment and shard, local catalog and shard catalog will be coalesced, as they're really represented the same concepts all along, just in different modes. In the catalog, we'll make slight changes to the definition of a projection, which represents the physical storage organization. The new definition simplifies segmentation and introduces valuable granularities of sharding to support evolution over time, and offers a straightforward migration path for both Eon and enterprise. There's a lot more to our Eon story than just the architectural roadmap. If you missed yesterday's Vertica, in Eon mode presentation about supported cloud, on premise storage option, replays are available. Be sure to catch the upcoming presentation on sizing and configuring vertica and in beyond doors. As we've seen with Eon, Vertica can separate data storage from the compute nodes, allowing machines to quickly fill in for each other, to rebuild fault tolerance. But separating compute and storage is used for much, much more. We now offer powerful, flexible ways for Vertica to add servers and increase access to the data. Vertica nine, this feature is called sub-clusters. It allows computing capacity to be added quickly and incrementally, and isolates workloads from each other. If your exploratory analytics team needs direct access to the source data, they need a lot of machines and not the same number all the time, and you don't 100% trust the kind of queries and user defined functions, they might be using sub-clusters as the solution. While there's much more expensive information available in our other presentation. I'd like to point out the highlights of our latest sub-cluster best practices. We suggest having a primary sub-cluster, this is the one that runs all the time, if you're loading data around the clock. It should be sized for the ETL workloads and also determines the natural shard count. Additional read oriented secondary sub-clusters can be added for real time dashboards, reports and analytics. That way, subclusters can be added or deep provisioned, without disruption to other users. The sub-cluster features of Vertica 9.3 are working well for customers. Yesterday, the Trade Desk presented their use case for Vertica over 300,000 in 5 sub clusters running in the cloud. If you missed a presentation, check out the replay. But we have plans beyond sub-clusters, we're extending sub-clusters to real clusters. For the Vertica savvy, this means the clusters bump, share the same spread ring network. This will provide further isolation, allowing clusters to control their own independent data sets. While replicating all are part of the data from other clusters using a publish subscribe mechanism. Synchronizing data between clusters is a feature customers want to understand the real business for themselves. This vision effects are designed for ancillary aspects, how we should assign resource pools, security policies and balance client connection. We will be simplifying our data segmentation strategy, so that when data that originate in the different clusters meet, they'll still get fully optimized joins, even if those clusters weren't positioned with the same number of nodes per shard. Having a broad vision for data management is a key component to political success. But we also take pride in our execution strategy, when you start a new database from scratch as we did 15 years ago, you won't compete on features. Our key competitive points where speed and scale of analytics, we set a target of 100 x better query performance in traditional databases with path loads. Our storage architecture provides a solid foundation on which to build toward these goals. Every query starts with data retrieval, keeping data sorted, organized by column and compressed by using adaptive caching, to keep the data retrieval time in IO to the bare minimum theoretically required. We also keep the data close to where it will be processed, and you clusters the machines to increase throughput. We have partition pruning a robust optimizer evaluate active use segmentation as part of the physical database designed to keep records close to the other relevant records. So the solid foundation, but we also need optimal execution strategies and tactics. One execution strategy which we built for a long time, but it's still a source of pride, it's how we process expressions. Databases and other systems with general purpose expression evaluators, write a compound expression into a tree. Here I'm using A plus one times B as an example, during execution, if your CPU traverses the tree and compute sub-parts from the whole. Tree traversal often takes more compute cycles than the actual work to be done. Especially in evaluation is a very common operation, so something worth optimizing. One instinct that engineers have is to use what we call, just-in-time or JIT compilation, which means generating code form the CPU into the specific activity expression, and add them. This replaces the tree of boxes that are custom made box for the query. This approach has complexity bugs, but it can be made to work. It has other drawbacks though, it adds a lot to query setup time, especially for short queries. And it pretty much eliminate the ability of mere models, mere mortals to develop user defined functions. If you go back to the problem we're trying to solve, the source of the overhead is the tree traversal. If you increase the batch of records processed in each traversal step, this overhead is amortized until it becomes negligible. It's a perfect match for a columnar storage engine. This also sets the CPU up for efficiency. The CPUs look particularly good, at following the same small sequence of instructions in a tight loop. In some cases, the CPU may even be able to vectorize, and apply the same processing to multiple records to the same instruction. This approach is easy to implement and debug, user defined functions are possible, then generally aligned with the other complexities of implementing and improving a large system. More importantly, the performance, both in terms of query setup and record throughput is dramatically improved. You'll hear me say that we look at research and industry for inspiration. In this case, our findings in line with academic binding. If you'd like to read papers, I recommend everything you always wanted to know about compiled and vectorized queries, don't afraid to ask, so we did have this idea before we read that paper. However, not every decision we made in the Vertica executer that the test of time as well as the expression evaluator. For example, sorting and grouping aren't susceptible to vectorization because sort decisions interrupt the flow. We have used JIT compiling on that for years, and Vertica 401, and it provides modest setups, but we know we can do even better. But who we've embarked on a new design for execution engine, which I call EE five, because it's our best. It's really designed especially for the cloud, now I know what you're thinking, you're thinking, I just put up a slide with an old engine, a new engine, and a sleek play headed up into the clouds. But this isn't just marketing hype, here's what I mean, when I say we've learned lessons over the years, and then we're redesigning the executer for the cloud. And of course, you'll see that the new design works well on premises as well. These changes are just more important for the cloud. Starting with the network layer in the cloud, we can't count on all nodes being connected to the same switch. Multicast doesn't work like it does in a custom data center, so as I mentioned earlier, we're redesigning the network transfer layer for the cloud. Storage in the cloud is different, and I'm not referring here to the storage of persistent data, but to the storage of temporary data used only once during the course of query execution. Our new pattern is designed to take into account the strengths and weaknesses of cloud object storage, where we can't easily do a path. Moving on to memory, many of our access patterns are reasonably effective on bare metal machines, that aren't the best choice on cloud hyperbug that have overheads, page faults or big gap. Here again, we found we can improve performance, a bit on dedicated hardware, and even more in the cloud. Finally, and this is true in all environments, core counts have gone up. And not all of our algorithms take full advantage, there's a lot of ground to cover here. But I think sorting in the perfect example to illustrate these points, I mentioned that we use JIT in sorting. We're getting rid of JIT in favor of a data format that can be treated efficiently, independent of what the data types are. We've drawn on the best, most modern technology from academia and industry. We've got our own analysis and testing, you know what we chose, we chose parallel merge sort, anyone wants to take a guess when merge sort was invented. It was invented in 1948, or at least documented that way, like computing context. If you've heard me talk before, you know that I'm fascinated by how all the things I worked with as an engineer, were invented before I was born. And in Vertica , we don't use the newest technologies, we use the best ones. And what is noble about Vertica is the way we've combined the best ideas together into a cohesive package. So all kidding about the 1940s aside, or he redesigned is actually state of the art. How do we know the sort routine is state of the art? It turns out, there's a pretty credible benchmark or at the appropriately named historic sortbenchmark.org. Anyone with resources looking for fame for their product or academic paper can try to set the record. Record is last set in 2016 with Tencent Sort, 100 terabytes in 99 seconds. Setting the records it's hard, you have to come up with hundreds of machines on a dedicated high speed switching fabric. There's a lot to a distributed sort, there all have core sorting algorithms. The authors of the paper conveniently broke out of the time spent in their sort, 67 out of 99 seconds want to know local sorting. If we break this out, divided by two CPUs and each of 512 nodes, we find that each CPU so there's almost a gig and a half per second. This is for what's called an indy sort, like an Indy race car, is in general purpose. It only handles fixed hundred five records with 10 byte key. There is a record length can vary, then it's called daytona sort, a 10 set daytona sort, is a little slower. One point is 10 gigabytes per second per CPU, now for Verrtica, We have a wide variety ability in record sizes, and more interesting data types, but still no harm in setting us like phone numbers, comfortable to the world record. On my 2017 era AMD desktop CPU, the Vertica EE5 sort to store about two and a half gigabytes per second. Obviously, this test isn't apply to apples because they use their own open power chip. But the number of DRM channels is the same, so it's pretty close the number that says we've hit on the right approach. And it performs this way on premise, in the cloud, and we can adapt it to cloud temp space. So what's our roadmap for integrating EE5 into the product and compare replacing the query executed the database to replacing the crankshaft and other parts of the engine of a car while it's been driven. We've actually done it before, between Vertica three and a half and five, and then we never really stopped changing it, now we'll do it again. The first part in replacing with algorithm called storage merge, which combines sorted data from disk. The first time has was two that are in vertical in incoming 10.0 patch that will be EE5 or resegmented storage merge, and then convert sorting and grouping into do out. There the performance results so far, in cases where the Vertica execute is doing well today, simple environments with simple data patterns, such as this simple capitalistic query, there's a lot of speed up, when we ship the segmentation code, which didn't quite make the freeze as much like to bump longer term, what we do is grouping into the storage of large operations, we'll get to where we think we ought to be, given a theoretical minimum work the CPUs need to do. Now if we look at a case where the current execution isn't doing as well, we see there's a much stronger benefit to the code shipping in Vertica 10. In fact, it turns a chart bar sideways to try to help you see the difference better. This case also benefit from the improvements in 10 product point releases and beyond. They will not happening to the vertical query executer, That was just the taste. But now I'd like to switch to the roadmap first for our adapters layer. I'll start with a story about, how our storage access layer evolved. If you go back to the academic ideas, if you start paper that persuaded investors to fund Vertica, read optimized store was the part that had substantiation in the form of performance data. Much of the paper was speculative, but we tried to follow it anyway. That paper talked about the WS with RS, The rights are in the read store, and how they work together for transaction processing and how there was a supernova. In all honesty, Vertica engineers couldn't figure out from the paper what to do next, incase you want to try, and we asked them they would like, We never got enough clarification to build it that way. But here's what we built, instead. We built the ROS, read optimized store, introduction on steep major revision. It's sorted, ordered columnar and compressed that follows a table partitioning that worked even better than the we are as described in the paper. We also built the last byte optimized store, we built four versions of this over the years actually. But this was the best one, it's not a set of interrelated V tree. It's just an append only, insertion order remember your way here, am sorry, no compression, no base, no partitioning. There is, however, a tuple over which does what we call move out. Move the data from WOS to ROS, sorting and compressing. Let's take a moment to compare how they behave, when you load data directly to the ROS, there's a data parsing operation. Then we finished the sorting, and then compressing right out the columnar data files to stay storage. The next query through executes against the ROS and it runs as it should because the ROS is read optimized. Let's repeat the exercise for WOS, the load operation response before the sorting and compressing, and before the data is written to persistent storage. Now it's possible for a query to come along, and the query could be responsible for sorting the lost data in addition to its other processes. Effect on query isn't predictable until the TM comes along and writes the data to the ROS. Over the years, we've done a lot of comparisons between ROS and WOS. ROS has always been better for sustained load throughput, it achieves much higher records per second without pushing back against the client and hasn't Vertica for when we developed the first usable merge out algorithm. ROS has always been better for predictable query performance, the ROS has never had the same management complexity and limitations as WOS. You don't have to pick a memory size and figure out which transactions get to use the pool. A non persistent nature of ROS always cause headaches when there are unexpected cluster shutdowns. We also looked at field usage data, we found that few customers were using a lot, especially among those that studied the issue carefully. So how we set out on a mission to improve the ROS to the point where it was always better than both the WOS and the profit of the past. And now it's true, ROS is better than the WOS and the loss of a couple of years ago. We implemented storage bundling, better catalog object storage and better tuple mover merge outs. And now, after extensive Q&A and customer testing, we've now succeeded, and in Vertica 10, we've removed the whys. Let's talk for a moment about simplicity, one of the best things Mike Stonebreaker said is no knobs. Anyone want to guess how many knobs we got rid of, and we took the WOS out of the product. 22 were five knobs to control whether it didn't went to ROS as well. Six controlling the ROS itself, Six more to set policies for the typical remove out and so on. In my honest opinion is still wasn't enough control over to achieve excess in a multi tenant environment, the big reason to get rid of the WOS for simplicity. Make the lives of DBAs and users better, we have a long way to go, but we're doing it. On my desk, I keep a jar with the knob in it for each knob in Vertica. When developers add a knob to the product, they have to add a knob to the jar. When they remove a knob, they get to choose one to take out, We have a lot of work to do, but I'm thrilled to report that in 15 years 10 is the first release with a number of knobs ticked downward. Get back to the WOS, I've said the most important thing get rid of it for last. We're getting rid of it so we can deliver our vision of the future to our customer. Remember how he said an Eon and sub-clusters we got all these benefits from shared storage? Guess what can't live in shared storage, the WOS. Remember how it's been a big part of the future was keeping the copies that identical to the primary copy? Independent actions of the WOS took a little at the root of the divergence between copies of the data. You have to admit it when you're wrong. That was in the original design and held up to the a selling point of time, without onto the idea of a separate ROS and WOS for too long. In Vertica, 10, we can finally bid, good reagents. I've covered a lot of ground, so let's put all the pieces together. I've talked a lot about our vision and how we're achieving it. But we also still pay attention to tactical detail. We've been fine tuning our memory management model to enhance performance. That involves revisiting tens of thousands of satellite of code, much like painting the inside of a large building with small paintbrushes. We're getting results as shown in the chart in Vertica nine, concurrent monitoring queries use memory from the global catalog tool, and Vertica 10, they don't. This is only one example of an important detail we're improving. We've also reworked the monitoring tables without network messages into two parts. The increased data we're collecting and analyzing and our quality assurance processes, we're improving on everything. As the story goes, I still have my grandfather's axe, of course, my father had to replace the handle, and I had to replace the head. Along the same lines, we still have Mike Stonebreaker Vertica. We didn't replace the query optimizer twice the debate database designer and storage layer four times each. The query executed is and it's a free design, like charted out how our code has changed over the years. I found that we don't have much from a long time ago, I did some digging, and you know what we have left in 2007. We have the original curly braces, and a little bit of percent code for handling dates and times. To deliver on our mission to help customers get value from their structured data, with high performance of scale, and in diverse deployment environments. We have the sound architecture roadmap, reviews the best execution strategy and solid tactics. On the architectural front, we're converging in an enterprise, we're extending smart analytic clusters. In query processing, we're redesigning the execution engine for the cloud, as I've told you. There's a lot more than just the fast engine. that you want to learn about our new data support for complex data types, improvements to the query optimizer statistics, or extension to live aggregate projections and flatten tables. You should check out some of the other engineering talk that the big data conference. We continue to stay on top of the details from low level CPU and memory too, to the monitoring management, developing tighter feedback cycles between development, Q&A and customers. And don't forget to check out the rest of the pillars of our roadmap. We have new easier ways to get started with Vertica in the cloud. Engineers have been hard at work on machine learning and security. It's easier than ever to use Vertica with third Party product, as a variety of tools integrations continues to increase. Finally, the most important thing we can do, is to help people get value from structured data to help people learn more about Vertica. So hopefully I left plenty of time for Q&A at the end of this presentation. I hope to hear your questions soon.

(exciting music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California. This is a CUBE conversation. >> Hello and welcome to this special CUBE Conversation, here in Palo Alto, CA CUBE Studios. I'm John Furrier your host of the CUBE. We're here with James Segil President and Co-Founder of Openpath Security. Hot start-up in a very cutting edge area that everyone can relate to physical security. But as that grows with the internet, the convergence of physical security with how people work online. It's been a huge issue, we've been covering IOT, we've been covering cloud security, we've been covering internet security. James, thanks for joining me today. >> It's great to be here, John. >> So, you guys are a young company in a very hot area. Great investors, you have a great background, we interviewed in the CUBE before, CUBE Alumni. Before we get into it, this is a super important area, I wanted you to take a minute to explain what you guys do. How long you've been around, what is Openpath? >> Sure, so you know, my partners and I are serial tech entrepreneurs out of L.A. this is our fourth company together over the last twenty years. You interviewed me when we were running EdgeCast. So, it's great to be back. You know, Openpath came from our own frustration. We're an Access Control company so we allow folks to enter office buildings, physical space, work space, using a security tool. That is not a badge. So, this is how we used to enter our prior buildings. So, this is actually my business partners badge pack just to get in and out of our offices, and we were basically tired of wearing dog tags or dog collar, however you want to call it, right? The whole idea was you can use your phone, your phone is your key. So, the credential to get into the office, into the building is on your phone, and mobile was a technology that hadn't really been introduced into the physical, sort of, property technology space before. And by bringing mobile to Bear as well as cloud technology, 'cause all the software's in the cloud. We were able to improve this value proposition and offer a cool solution. >> So, just quickly how, how long have you guys been out with the product and when was the company founded? >> So, we started the company three years ago and launched commercially about a year ago. You know, we spent two years building the technology, getting our patents, really getting everything, figured out. We have software and hardware, it's part of our solution. And so, when we launched a year ago, it was kind of like drinking from a fire hose. We literally had people coming and saying, finally, somebody figured out how to get rid of the badge and use my phone just so it will let me in. And since then we've raised a good amount of money and have been, you know just selling to basically everyone, yeah. >> Congratulations, this is a hot story, so I want to get into it. So, the origination story is, obviously you had to be a successful entrepreneur in the past. Being a serial entrepreneur has it's ups and downs, but you know, with the cloud, everyone thinks, Oh, Security is just a cloud problem. You guys are attacking a physical property, physical security, kind of bringing a DevOps ethos to this. I mean, when you hold those badges up, reminds me of the old janitor key ring. This is the digital ring. You know, all your access. So, clearly an opportunity to automate. >> Yeah. >> So clearly, kind of, obviously, the cloud mentality here. But, your impact is to, kind of, the kind of older industry. Explain this trend of property technology. I mean, most people can relate to their office space. >> Yeah. >> You know, waving the badge to get in, maybe VDI on the desktop or whatever's happening. I mean, talk about the the market place and the trend. >> So, you know, buildings, real estate for the most part, are very slow to move in adopting new technology. And I think, you've seen that in a lot of different industries. Certainly in real estate, there was a sort of slowness or unwillingness to move on past old techs. So, this works, it's an RFID badge. And you can use it and people are comfortable with it. It's worked for forty years. Prop-tech, Property Technology, is really a focus around innovating how you work with, interact with, and spend time at work, in office buildings. But it extends well beyond office, it extends into multi-family residential, health care, any building you really go to. And so, there is a lot money and there is a lot of entrepreneurs who are focused on, how do I improve the quality of every experience we have? When I go into an apartment building, when I got into a hospital, when I go to school, when I go to work and that's really what were focused. We're sort of thinking about that whole experience and reducing the friction in every step of how you interact with that building. >> You know, this used to be an IT problem, if your with big company you sign in, you on board, you get your laptop, you get your badge, someone probably enters your name into a database. And then if you leave it has to be deleted. Is you guys addressing that area? Talk about that piece of it because I think this is more real time, more person without the phone, for instance, your bridging the physical and the logical. Talk about the IT versus the old way of doing it. >> Yeah, so, you know, typically in the real estate world, there's an office manager, a facilities person, maybe, a physical security person, or even like real estate person and they're in charge, at least within the enterprise, of thinking about physical security. But what's happened is, there is a lot of exposure that we have to our data, to our personal safety, to everything really in the office. If you don't protect the physical space, from the thieves or bad actors who want to steal your data or hurt you. And so, all this money has gone into Cyber Security, the chief security officer, the IT department, they have unlimited budgets to go out and solve that problem, to protect the network. But they are literally leaving the front door open. And so, a lot of what is happening today in the enterprise is that the CISO, the Chief Security Team, the IT Team is starting to really gain denomination over this real estate and facilities space, and sort of say, hey, these systems need to work together. If I have a single source of truth to hold all my users and my employees in a single database, I want that to connect, not just to my salesforce.com instance but I want it to connect my Access Control system and how people enter the the building. >> Access Control also an IOT problem, Industrial IOT, we hear that area. Clearly a use case for that opportunity so clearly why you got some funding and I want to cover that in a second on origination story. But the question I have for you is, when you guys started the company and now that you are in market with customers, what's the main problem that you solve? What's like, I mean, you have to solve that one problem, what problem do you solve and where is the growth from there? >> So, I have two groups of sort of customers who I talk to. The first group are tenants or enterprise customers, and these folks who need to move into an office, and most of the choice around when to buy Access Control comes because you're building out space or your moving into an office. You need Access Control. It's not on the list of nice to haves, you need to be able to lock the door. So, when you move into a new office, you need to have internet connectivity, alright, you need to have Access Control, maybe an alarm system, sparkletts water or whatever it's going to be. And we're on that list. So, when people are investing in that capital infrastructure. They're going to future proof that investment, they're are going to choose Openpath. The second group we talk to are folks that are building buildings or renovating buildings. And that's asset managers, developers, property managers, landlords. And those constituents are looking to build a physical space that's both safe but allows them to attract folks to their building as tenants. And so, if you offer amenities, you offer a gym, a cool, sort of, you know, work space, and Access Control Technology it becomes an incentive for folks to want to come and office in your space. >> So, you know, you and I are techies. We love to buy that shinny new toy. The property type tech world, they not as innovative or have a propensity to just at the next thing because, they're about security, they're about that, locking doors. So, I got to ask you, what are some of the things, and they're getting more savvy now, I can see that, so it's clear. You can see most of the digital amenities. First, a start with WIFI, we don't have WIFI, you're done. Now, you're starting to see much more app, centric things happening on these locations. What are some of the areas that people are gravitating in terms that they like, in terms of features with Access Control? What is it enabling from a value stand point? Is it differentiate services, is it access to certain amenities, you mentioned some of that. What is some of the new things that are being created? >> Well, I think the first thing is that we're reducing some level of friction in interacting with you workspace. So, the fact that you can basically, keep your phone in your pocket or keep talking on your phone or keep it in your purse and just walk up to the door and have the door unlock because it knows you're there. That's not just kind of cool that's really just helping out the quality of your day to day experience. You know, ever since 9/11 when we upgraded the security experience almost everywhere. Whether you're entering an arena, a plane or a building that friction is something we are used to now and there is a push back that people want a little bit less friction even though they want that higher level of security. >> Not that I want to get doom day scenario. You mentioned 9/11, they were told to stay in their buildings when they could have been evacuated, everyone in New York knows that tragic story. Huge active shooter environment right now, it's just my kids went to an event in San Francisco. Literally, what is on the mind of people is, oh my God, is there going to be an active shooter? These are examples of things that could go wrong and in security this becomes an Apocalypse scenario that we've been talking about it takes that to get people to take action. So, can you help in those scenarios? How do you help someone either thwart those kinds of security attacks or help them get through them if somethings happening? Let's just say an active shooter comes into a building? >> Yeah, so we've thought a lot about that. And we have kids in schools and we actually have a lot of schools and houses of worship that are buying and installing our system. So, we have a couple different capabilities, lockdown is our latest release. And this is the capability from anyone, anywhere on any mobile phone in that building to enable a lockdown procedure. What I think is particularly valuable here is that if you're basically no where near the fire alarm which is where the lock down button might be as well, and you're stuck in a closet and or hidden away tryna to make sure you're not going to get shot. If you have your phone on you can enable a lockdown and because our plans are kind customized, you can enable a lockdown that let's say locks all the doors in the zone. But lifts up the garage gate so that first responders can get there. And we've seen proven the faster the first responders can get to the problem, whether it's, you know, an EMS person that's tryna to stem the bleeding on someone who is injured or whether it's a SWAT team-- >> Well that's actually proven you saw Gilroy, you saw the response in Dayton. Literally minutes taking those active shooter. >> Well, every second counts, so being able to have a lockdown that works fast, that's effective and that allows people to get through and the bad guys to sort of be isolated is important. The second thing is, we actually have integration with video systems, so you can send a live video feed instantly of every door that's locked down to the first responders. And they can actually see it right there on their iPhone where the bad guy is, what he is doing, real time, from the video systems. They can take over the video system, so it's a pretty-- >> So, it augments the security environment for good and bad scenarios. So, let's get a kind of more realistic scenario. Doomsday scenarios is kind of depressing, but it's real. Our people are planning and are protecting around that. One basic concept, and I got reprimanded at VMware was, I've been at the VMware campuses since they've been building it. But recently I was going to a meeting, and I knew it was building number four, or whatever it was. And I'm sitting there waiting at the door. Someone comes out and I went in and they call it tailgating. Turns out I didn't have a badge and the new person who was there really kind of got in my face and said, You tailgated, I'm like, I do it all the time, I'm like, okay, stop. So, okay, you don't tailgate a VMware anymore and I now know that. But this happens all the time. This is another common problem, I could be stealing laptops, I could be getting the plans at VMworld. I mean, whatever's going on. And this, bad things are happening with tailgating. That's a big thing isn't it? >> It is a big thing. Security experts are telling us it is one of the top three physical security challenges that enterprise CISO's are running into, tailgating. And what's happening is, people just like you, are well meaning are sneaking in. But, there's some bad actors that are sneaking in as well. So, we've got technology that have deployed with partners that actually count the people that are coming in through the door. And if there's two entries when you're only supposed to have one, we can actually track that and instantly make the meter go beep, beep, beep, beep and send an email alert to a security desk or to the individual themself with a video and a picture of the person who snuck in behind you. >> That is a great example, and I mentioned VMware in all seriousness. That actually had happened. There's a huge campus and the reason why, I just didn't want to go to the front I parked at the wrong garage and I didn't want to walk five buildings over. A little bit lazy but that's the point of the large buildings, where the security access comes in. For large campuses, whether it's Universities or corporate, that's the big challenge, right? Not just Access Control but management. >> It's management and so the idea, of sort giving and empowering people to be able to really quickly change, configure and access places. The fact that from your phone you can actually, as a manager change access privileges and give someone who's visiting a temporary pass. That's not one of these, but it's actually a virtual pass on your phone. That's really empowering. So, if you were coming to visit me at VMware, I'd send you a guest pass that gives you one hour access to five different doors and so that you wouldn't have to sneak in. You would basically be able to just use your phone to get in as a visitor for one hour. And after an hour you're not going to be able to get in. >> All right, so let's talk about the company. Openpath Security, you guys obviously targeting the physical space, Access Control, logical physical coming together seamless frictionless environment. Business model? How much funding did you get? What kind of investors do you have? Employee count? Product shipping status? Give us through the numbers. Give us the data. >> Sure, so we started the company three years ago, we came out a stealth mode a year ago and launched commercially, we had actually done our series A internally, we led that ourselves as the founders. And then, when we came out of stealth mode, we had a lot of great attention in the space. Emergence Capital is our lead investor in our series B. We raised $27 millions total. We've got a great team of folks, just under 16 employees. We are based in Los Angeles but we have offices in Indianapolis as well 'cause why not? It's the best place to be. And we're growing fast. We actually sell focused on commercial real estate, but have expanded to multi-family residential. Also, to schools, churches, houses of worship. And we are here in the U.S. now and we're growing internationally over the next two or three years. >> And the product is the a SaaS, managed service, physical? What's the story of the product? >> Yeah, so there's a combination of physical hardware but there is a 100% attached software to it. So, you install a reader at the door, a panel in the IT closet and it's wired as most traditional Access Control systems are but our software is all hosted in the cloud. As well, as the credential that is on the phone. And so, we sort of sell the hardware upfront and then you buy sort of a recurring annual fee associated with the number of doors you own. >> And so you get on the spec that be on the new building, so you do a little go to, you go to market as it is, getting on the design side, suppliers to the building. >> Yup, so, there's the developers, the architects, who put us into the spec. There the system integrators, these are the folks who are low voltage electricians, security system integrators who go out and actually deploy all the wiring you have in this building. They'll go ahead and do the WIFI network, the CCTV camera system, the alarm system and the Access Control system. And so, we have a national network of certified installers who go out, and that's actually how we go to market. We sell through them. >> And you have the software, it's a nice margin. And is there a cloud play here too? Is data stored in the cloud? >> Yeah. >> How are you guys handling some of the backend stuff? >> So, yeah, all the information is stored in the cloud. What's kind of important in a life safety environment is that you have a cloud system that runs it but that you can work if the internet is down. 'Cause imagine if the Internet's down and you can't even get into the office to fix the internet. So, our system works offline as well as online. We store all the credentials locally. >> I remember interviewing Ring's founder at an Amazon event. Simple concept use the cloud. Same thing for you? Not a simple concept but you're in the spec use the cloud with a hundred percent attach rate. >> Exactly. >> All right, so what's the coolest thing that you see happening in this market for you guys? What's going on that you would say that's notable that you would think is important that people should pay attention to. >> There is a number of big trends. You know, we talked about one, right? Which is the whole change of, you know, combining physical security with cyber security and having those two really come together. I'd say the transition of IOT from just the home into the workspace is another big trend we are watching. People are just used to having an NEST on their wall or a Ring on their doorbell and the want Openpath on their door at work. And that's something else that we've seen as a big transition. People are getting used to having an easier experience and I think the final thing is how people use the workspace, right? People work all over the space now. It's not just at their cubicle and that's impacting. >> I got to get some commentary and understanding around the name Openpath because most people in these kind of areas that you're in have closed systems. You know, the HVAC system, I'm running an IOT like an operational technology. Information technology is a protocol based OSI model, open source. So, those worlds are colliding, we're covering that in the whole IOT, industrial IOT trend. Openpath Security? If it's open can I hack it, what's going the Openpath name? Tell us why Openpath? How are you open? Tell us the story behind the name? >> I'm really glad you asked. We were really frustrated when we analyzed the space, as investors and entrepreneurs in this category. We saw that all the systems that are out there, are incredibly closed. Their proprietary systems, they work on old protocols and they're not open. Ours is open. It's built on open API's. Every element of our technology can be connected to, right? And we have tons of developers who are integrating, just like they do in the web, with Openpath. And that's something you can't really do in the old physical Access Control World. So open is just correlating that. >> So, you that's from an ecosystems stand point, you guys enabling others to build on top of your stuff. >> Oh yeah, we've got Envoy the visitor management company. They've got an integration with our Access Control. Density, which is a really cool people counting tool. We've got Camo, a video integration tool. All these folks are integrating with us because it's open and it's really easy to do. >> Okay, so I got to ask the question. I'm now, I'm a building person designing the specs for the new campus, open? That sounds insecure. How do you guarantee that you're going to to be secure? I'm worried about security. How can a hacker get in, take over the physical space, shut it down, that's my concern. How do you address that? >> Yeah, no it's legit. So, what I often say to people is, let's see. You can have a badge, like this, right? And you can pick up my badge and find it anywhere you want, right? And now you're James, right? You can go take that, and you can get in anywhere you want. But I challenge you to try to use my phone. Try to unlock right now, right? >> There it is. (laughs) >> That super computer is encrypted, there's no way you're going to break that. This is the most secure way to enter anywhere. >> But if I get, that's an iPhone but with an Android I'd get some Malware on there. >> But the Malware that you get on your Android isn't necessarily going to allow you to authenticate our system. >> So, you're content, even though you might be on an open device, you guys are containing the app, security app on the device. >> Yeah, so the same protocols that we use on the internet to have secure HTTPS communication between any kind of client, your computer and a website. We're using that same hand off. Where we have rotating security certificates on this, as well as in the cloud, as well as on the panel. So, everything is fully encrypted end to end. And that gives us a level of security that's unmatched and unrivaled actually, in the Access Control space. >> James, thanks for coming on theCUBE, final just give a plug for the company. What's new, what's happening? What's going on Openpath? What's next for you guys? >> Well, if it's a plug openpath.com that's an easy one. But, I think for us, we're really growing in a way that people are excited about. I want to change the work day experience. So, everybody who's out there, who's tired of using a keycard and a badge, I want them to go to their boss and say, why can't we upgrade to Openpath? Go to your landlord and say, hey, I'm negotiating this into my tenant improvement. I want Openpath as a part of how I sort of access the building. The trends that we're really excited about, this lockdown technology, the Anti-Tailgating Technology. Those are really cool, sort of advantages that we give the enterprise and we're just excited to be helping people improve the quality of the workday. >> And what's the reason why you're winning deals? What's the one factor or two factors? Ease of use, open-ness, convince features? What's your-- >> I love it, you're selling my product for me. It's ease of use, it's the fact that it reduces a number of steps in the friction you experience personally everyday. And that the enterprise or the landlord experiencing managing a system, is less expensive and more secure. Kind of all the things you want. Plus, I mean, how much sense does it make that you don't have to carry around ten badges that you can actually just have it all on your phone. It just makes sense. >> Soon series C funding around the corner. (laughs) >> If you're interested, we should have a conversation. >> TheCUBE fund's not yet setup but when we get theCUBE venture capital fund will be in. >> That's good, you let me invest in your company, I'll let you invest in mine. >> We'll talk. James Segil, entrepreneur President, Co-Founder Openpath Security, hot start up here inside theCUBE. Featured startup here. Thanks for watching. I'm John Furrier. (exciting music)

>> Narrator: Live, from Washington DC, it's theCUBE. Covering AWS Public Sector Summit. Brought to you by Amazon Web Services. >> Welcome back everyone to theCUBE's live coverage of the AWS Public Sector Summit here in our nation's capital. I'm your host, Rebecca Knight. Co-hosting along side John Furrier. We are joined by Jamil Jaffer, he is the VP Strategy and Partnerships at IronNet. Thanks so much for coming on theCUBE. >> Thanks for having me Rebecca. >> Rebecca: I know you've been watching us for a long time so here you are, soon to be a CUBE alumn. >> I've always wanted to be in theCUBE, it's like being in the octagon but for computer journalists. (laughing) I'm pumped about it. >> I love it. Okay, why don't you start by telling our viewers a little bit about IronNet and about what you do there. >> Sure, so IronNet was started about 4 1/2 years ago, 5 years ago, by General Kieth Alexander, the former director of the NSA and founding commander of US Cyber command. And essentially what we do is, we do network traffic analytics and collective defense. Now I think a lot of people know what network traffic analytics are, you're looking for behavioral anomalies and network traffic, trying to identify the bad from the good. Getting past all the false positives, all the big data. What's really cool about what we do is collective defense. It's this idea that one company standing alone can't defend itself, it's got to work with multiple companies, it's got to work across industry sectors. Potentially even with the governments, and potentially across allied governments, really defending one another. And the way that works, the way we think about that, is we share all the anomalies we see across multiple companies to identify threat trends and correlations amongst that data, so you can find things before they happen to you. And so the really cool idea here is, that something may not happen to you, but it may happen to your colleague, you find about it, you're defended against it. And it takes a real commitment by our partners, our companies that we work with, to do this, but increasingly they're realizing the threat is so large, they have no choice but to work together, and we provide that platform that allows that to happen. >> And the premise is that sharing the data gives more observational space to have insights into that offense, correct? >> That's exactly right. It's as though, it's almost like you think about an air traffic control picture, or a radar picture, right? The idea being that if you want to know what's happening in the air space, you got to see all of it in real time at machine speed, and that allows you to get ahead of the threats rather than being reactive and talking about instant response, we're talking about getting ahead of the problems before they happen so you can stop them and prevent the damage ahead of time. >> So you're an expert, they're lucky to have you. Talk about what you've been doing before this. Obviously a lot of experience in security. Talk about some about some of the things you've done in the past. >> So I have to admit to being a recovering lawyer, but you have to forgive me because I did grow up with computers. I had a Tandy TRS-80 Color computer when I first started. 4K of all more RAM, we upgraded to 16K, it was the talk of the rainbow computer club, what are you doing, 16K of RAM? (laughing) I mean, it was-- >> Basic programming language, >> That's right. (laughing) Stored on cassette tapes. I remember when you used to have to punch a hole in the other side of a 5 1/4 floppy disc to make it double sided. >> Right, right. >> John: Glory days. >> Yeah, yeah. I paid my way through college running a network cable, but I'm a recovering lawyer, and so my job in the government, I worked at the House Intelligence Committee, the Senate Foreign Relations Committee and then the Bush administration on the Comprehensive National Cybersecurity Initiative, both the Justice Department and the White House. >> You've seen the arc, you've seen the trajectory, the progress we're making now seems to me slower than it should be, obviously a lot of inertia as Amy Chasity said today about these public sector government agencies, what not. But a real focus has been on it, we've been seeing activity. Where are we with the state of the union around the modernization of cyber and awareness to what's happening? How critical are people taking this threat seriously? >> Well I think I variety of things to say on that front. First, the government itself needs modernize its systems, right? We've seen that talked about in the Obama administration, we've seen President Trump put out an executive order on modernization of federal infrastructure. The need to move to the cloud, the need to move to shared services, make them more defensible, more resilient long-term. That's the right move. We've seen efforts at the Department of Defense and elsewhere. They aren't going as fast as the need to, more needs to happen on that front. IT modernization can really be accelerated by shifting to the cloud, and that's part of why that one of the things that IronNet's done really aggressively is make a move into the cloud space, putting all of our back end in the cloud and AWS. And also, ability, capability to do surveillance and monitoring. When I say surveillance I mean network threat detection not surveillance of the old kind. But network threat detection in the cloud, and in cloud-enabled instances too. So both are important, right? Classic data centers, but also in modern cloud infrastructure. >> Yeah, one of the things people want to know about is what your enemy looks like, and now with the democratization with open source, and democratization of tools, the enemies could be hiding through obscure groups. The states, the bad actors and the state actors can actually run covert activities through other groups, so this is kind of a dynamic that creates confusion. >> No, in fact, it's their actual mode of operating, right? It's exactly what they do, they use proxies, right? So you'll see the Russians operating, looking like a criminal hacker group operating out of the eastern Europe. In part because a lot of those Russian criminal rings, in actuality. You see a lot of patriotic hackers, right? I would tell most people, if you see a patriotic hacker there's probably a government behind that whole operation. And so the question becomes, how do you confront that threat, right? A lot of people say deterrence doesn't work in cyberspace. I don't believe that. I think deterrence can and does work in cyberspace, we just don't practice it. We don't talk about our capabilities, we don't talk our red lines, we don't talk about what'll happen if you cross our red lines, and when we do establish red lines and they're crossed, we don't really enforce them. So it's no surprise that our enemies, or advisories, are hitting us in cyberspace, are testing our boundaries. It's cause we haven't really give them a sense of where those lines are and what we're going to do if they cross them. >> Are we making an progress on doing anything here? What's the state of the market there? >> Well the government appears to have gotten more aggressive, right? We've seen efforts in congress to give the Department of Defense and the US Intelligence Committee more authorities. You can see the stand up of US Cyber Command. And we've seen more of a public discussion of these issues, right? So that's happening. Now, is it working? That's a harder question to know. But the real hard question is, what do you do on private sector defense? Because our tradition has been, in this country, that if it's a nation-state threat, the government defends you against it. We don't expect Target or Walmart or Amazon to have service to air missiles on the roof of your buildings to defend against Russian Bear bombers. We expect the government to do that. But in cyberspace, the idea's flipped on its head. We expect Amazon and every company in America, from a mom and pop shop, all the way up to the big players, to defend themselves against script kiddies, criminal hacker gangs, and nation-states. >> John: And randomware's been taking down cities, Baltimore, recent example, >> Exactly. >> John: multiple times. Hit that well many times. >> That's right, that's right. >> Talk about where the US compares. I mean, here as you said, the US, we are starting to have these conversations, there's more of an awareness of these cyber threats. But modernization has been slow, it does not quite have the momentum. How do we rate with other countries? >> Well I think in a lot of ways we have the best capabilities when it comes to identifying threats, identifying the adversary, the enemy, and taking action to respond, right? If we're not the top one, we're in the top two or three, right? And the question, though, becomes one of, how do you work with industry to help industry become that good? Now our industry is at the top of that game also, but when you're talking about a nation-state, which has virtually unlimited resources, virtually unlimited man-power to throw at a problem, it's not realistic to expect a single company to defend itself, and at the same time, we as a nation are prepared to say, "Oh, the Department of Defense should be sitting on "the boundaries of the US internet." As if you could identify them even, right? And we don't want that. So the question becomes, how does the government empower the private sector to do better defense for itself? What can the government do working with industry, and how can industry work with one another, to defend each other? We really got to do collective defense, not because it makes sense, which it does, but because there is no other option if you're going to confront nation-state or nation-state enabled actors. And that's another threat, we've seen the leakage of nation-state capabilities out to a lot broader of an audience now. That's a problem, even though that may be 2013 called and wants it's hack back, those things still work, right? What we saw in Baltimore was stuff that has been known for a long time. Microsoft has released patches long ago for that, and yet, still vulnerable. >> And the evolution of just cyber essential command, and Cyber Command, seems to be going slow, at least from my frame. Maybe I'm not in the know, but what is the imperative? I mean, there's a lot of problems to solve. How does the public sector, how does the government, solve these problems? Is cloud the answer? What are some of the things that people of this, the top minds, discussing? >> Well and I think cloud is clearly one part of the solution, right? There's no question that when you move to a cloud infrastructure, you have sort of a more bounded perimeter, right? And that provides that ability to also rapidly update, you could update systems in real time, and in mass. There's not going around and bringing your floppy disc and loading software, and it sounds like that's sort of a joke about an older era, but you look at what happened with NotPetya and you read this great Wired article about what happened with NotPetya, and you look at Maersk. And the way that Maersk brought its systems back up, was they had domain controller in Africa that had gone down due to a power surge, where they were able to recover the physical hard drive and re-image all their world-wide domain controls off of that one hard drive. You think about a major company that runs a huge percentage of the world's ports, right? And this is how they recovered, right? So we really are in that, take your disc and go to computers. In a cloud infrastructure you think about how you can do that in real time, or rapidly refresh, rapidly install patches, so there's a lot of that, that's like a huge part of it. It's not a complete solution, but it's an important part. >> Yeah, one of the things we talk about, a lot of tech guys, is that this debate's around complexity, versus simplicity. So if you store your data in one spot, it's easy to audit and better for governing compliance, but yet easier for hackers to penetrate. From an IQ standpoint, the more complex it is, distributed, harder. >> Yeah I think that's right. >> John: But what's the trade off there? How are people thinking about that kind of direction? >> No that's a great question, right? There's a lot of benefits to diversity of systems, there's a lot of benefit to spreading out your crown jewels, the heart of your enterprise. At the same time, there's real resilience in putting it in one place, having it well defended. Particularly when it's a shared responsibility and you have partial responsibility for the defense, but the provider to, I mean, Amazon, and all the other cloud providers, Microsoft and Google, all have it in their own self interest to really defend their cloud really well. Because whether or not you call it shared responsibility, it's your stock price that matters if you get hit, right? And so, instead of you, Amazon, and all the other cloud players have an incentive to do the right thing and do it really well. And so this shared responsibility can work to both side's benefits. That being said, there's an ongoing debate. A lot of folks want to do there stuff on-prem in a lot of ways. You know, a lot of us are old school, right? When you touch it, you feel it, you know it's there. And we're working through that conversation with folks, and I think that at the end of the day, the real efficiency gains and the power of having super computing power at your fingertips for analytics, for consumer purposes and the like. I really think there's no way to avoid moving to a cloud infrastructure in the long run. >> I know you said you were a recovering lawyer, but you are the founding director of the National Security Institute at the Antonin Scalia School of Law. How are you thinking about educating the next generation of lawyers who could indeed become policy makers or at least work on these committees, to think about these threats that we don't even know about yet? >> That's a great question. So one of the things we're doing, is we're working through the process with the state commission on establishing a new LLM and cyber intelligence national security law. That'll be a great opportunity for lawyers to actually get an advanced degree in these issues. But we're also training non-lawyers. One of the interesting things is, you know, One of the challenges DC has, is we make a lot of tech policy, a lot of it not great, because it's not informed by technologists, so we've got a great partnership with the Hewlett Foundation where we're bringing technologists from around the country, mid-career folks, anywhere from the age of 24 to 38. We're bringing them to DC and we're educating them on how to talk to policy makers. These are technologists, these are coders, data scientists, all the like, and it's a real opportunity for them to be able to be influential in the process of making laws, and know how to involve themselves and talk that speak. Cause, DC speak is a certain thing, right? (laughing) And it's not typically consistent with tech speak, so we're trying to bridge that gap and the Hewlett Foundation's been a great partner in that effort. >> On that point about this collaboration, Silicon Valley's been taking a lot of heat lately, obviously Zuckerberg and Facebook in the news again today, more issues around irresponsibility, but they were growing a rocket ship, I mean, company's only 15 years old roughly. So the impact's been significant, but tech has moved so fast. Tech companies usually hire policy folks in DC to speak the language, educate, a little bit different playbook. But now it's a forcing function between two worlds colliding. You got Washington DC, the Silicon Valley cultures have to blend now. What are some of the top minds thinking about this? What are some of the discussions happening? What's the topic of conversations? >> Well look, I mean, you've see it in the press, it's no surprise you're hearing this talk about breaking up big tech companies. I mean, it's astounding. We used to live in world in which being successful was the American way, right? And now, it seems like at least, without any evidence of anti-trust concerns, that we're talking about breaking up companies that have otherwise hugely successful, wildly innovative. It's sort of interesting to hear that conversation, it's not just one party, you're hearing this in a bipartisan fashion. And so it's a concern, and I think what it reveals to tech companies is, man, we haven't be paying a lot of attention to these guys in DC and they can cause real trouble. We need to get over there and starting talking to these folks and educating them on what we do. >> And the imperative for them is to do the right thing, because, I mean, the United States interest, breaking up, say, Facebook, and Google, and Apple, and Amazon, might look good on paper but China's not breaking up Alibaba anytime soon. >> To the contrary. They're giving them low-interest loans and helping them all to excel. It's crazy. >> Yeah, and they have no R&D by the way, so that's been- >> Jamil: Right, because they stole all of our IP. >> So the US invests in R&D that is easily moving out through theft, that's one issue. You have digital troops on our shores from foreign nations, some will argue, I would say yes. >> Jamil: Inside the border. >> Inside the border, inside the interior, with access to the power grids, our critical infrastructure, this is happening now. So is the government now aware of the bigger picture around what we have as capabilities and criticalities that were needed now for digital military? What is that conversation like? >> Well I think they're having this conversation, right? I think the government knows it's a problem, they know that actually in a lot of ways a partnership with tech is better than an adversary relationship. That doesn't change the fact that, for some reason, in the last three, four years, we really have seen what some people are calling a "techlash", right? A backlash against technology. It kind of strikes me as odd, because of course, the modern economy that we've so benefited from is literally built on the back of the innovations coming out of the Silicon Valley, out of the west coast, and out of the DC metro area, where a lot these tech companies are developing some of the most innovative new ideas. Now they're, frankly, helping government innovate. So Amazon's a key part of that effort, right? Here in the public sector. And so I'm hoping that education will help, I know that the arrival of tech companies here to really have that conversation in an open and sensible way, I hope will sort of waft back some of this. But I worry that for too long the tech and the policies have ignored on another. And now they're starting to intersect as you say, and it has the possibility of going wrong fast, and I'm hoping that doesn't happen. >> You know, one of the things that Rebecca and I were talking about was this talent gap between public sector and private sector. These agencies aren't going to go public anytime soon, so maybe they should get equity deals and get a financial incentive. (laughing) You know what I mean? Shrink down the cost, increase the value. But as you get the collaboration between the two parties, the cloud is attracting smart people, because it gives you an accelerant of value. So people can see some entry points to land, some value out of the gate, verus giving up and abandoning it through red tape, or in other processes. So you starting to see smart people get attracted to cloud as a tool for making change. How is that working? And how is that going to work? Cause this could be coming to the partnership side of it. People might not want to work for the government, but could work with the government. This is a dynamic that we see as real. What's your thoughts? >> I think that's exactly right. Having these cloud infrastructures gives the ability to one, leverage huge amounts of computing power, but also to leverage insights and knowledge from the private sector in ways that you never could have imagined. So I really do think the cloud is an opportunity to bring real benefits from private sector innovation into the public sector very rapidly, right? So, broad-clouded option. And that's part of why John Alexander, my boss, and I have been talking a lot about the need for broad-clouded option. It's not just innovative in technology, it's benefits to the war fighter, Right? I mean, these are real, tangible benefits pushing data in real time, the war fighter, You know John Alexander had one of the biggest innovations in modern war fighting, where he's able to take civil intelligence down from weeks and months, down to minutes and seconds, that the naval and our war fighters in Iraq and Afghanistan to really take the fight to the enemy. The cloud brings that power scaled up to a huge degree, right? By orders of magnitude. And so the government recognizes this and yet today we don't see them yet moving rapidly in that direction. So I think the EO was a good move, a good first step in that direction, now we got to see it implemented by the various agencies down below. >> Well we'll kep in touch, great to have you on. I know we're wrapping up the day here, they're breaking down, we're going to pull the plug literally. (laughing) We'll keep in touch and we'll keep progress on you. >> Thank you so much, I appreciate it. >> Rebecca: Jamil, you are now a CUBE alumn, >> I love it, thank you. >> Rebecca: So congrats, you've joined the club. >> I love it. >> I'm Rebecca Knight for John Furrier you have been watching theCUBE's live coverage of the AWS Public Sector Summit. (electronic music)

>> Live from Las Vegas. It's theCUBE. Covering Informatica World 2019, brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World. I am your host, Rebecca Knight, along with co-host, John Furrier. We have two guests for this segment. We have Abhiman Matlapudi. He is the Product Master at Deloitte. Welcome. >> Thanks for having us. >> And we have Kubalahm Rajeev Krishnan, Specialist Leader at Deloitte. Thank you both so much for coming on theCUBE. >> Thanks Rebecca, John. It's always good to be back on theCUBE. >> Love the new logos here, what's the pins? What's the new take on those? >> It looks like a honeycomb! >> Yeah, so interesting that you ask, so this is our joined Deloitte- Informatica label pin. You can see the Deloitte green colors, >> Nice! They're beautiful. >> And the Informatica colors. This shows the collaboration, the great collaboration that we've had over, you know, the past few years and plans, for the future as well. Well that's what we're here to talk about. So why don't you start the conversation by telling us a little bit about the history of the collaboration, and what you're planning ahead for the future. Yeah. So, you know, if we go like you know, ten years back the collaboration between Deloitte and Informatica has not always been that, that strong and specifically because Deloitte is a huge place to navigate, and you know, in order to have those meaningful collaborations. But over the past few years, we've... built solid relationships with Informatica and vise versa. I think we seek great value. The clear leaders in the Data Management Space. It's easy for us to kind of advise clients in terms of different facets of data management. You know, because no other company actually pulls together you know, the whole ecosystem this well. >> Well you're being polite. In reality, you know where it's weak and where it's real. I mean, the reality is there's a lot of fun out there, a lot of noise, and so, I got to ask you, cause this is the real question, because there's no one environment that's the same. Customers want to get to the truth faster, like, where's the deal? What's the real deal with data? What's gettable? What's attainable? What's aspirational? Because you could say "Hey, well I make data, data-driven organization, Sass apps everywhere." >> Yeah. Yeah absolutely. I mean every, every company wants to be more agile. Business agility is what's driving companies to kind of move all of their business apps to the Cloud. The uh, problem with that is that, is that people don't realize that you also need to have your data management governance house in order, right, so according to a recent Gartner study, they say by next year, 75% of companies who have moved their business apps to the Cloud, is going to, you know, unless they have their data management and data assets under control, they have some kind of information governance, that has, you know, context, or purview over all of these business apps, 50% of their data assets are going to erode in value. So, absolutely the need of the hour. So we've seen that great demand from our clients as well, and that's what we've been advising them as well. >> What's a modern MDM approach? Because this is really the heart of the conversation, we're here at Informatica World. What's- What does it look like? What is it? >> So I mean, there are different facets or functionalities within MDM that actually make up what is the holistic modern MDM, right. In the past, we've seen companies doing MDM to get to that 360-degree view. Somewhere along the line, the ball gets dropped. That 360 view doesn't get combined with your data warehouse and all of the transaction information, right, and, you know, your business uses don't get the value that they were looking for while they invested in that MDM platform. So in today's world, MDM needs to provide front office users with the agility that they need. It's not about someone at the back office doing some data stewardship. It's all about empowering the front office users as well. There's an aspect of AIML from a data stewardship perspective. I mean everyone wants cost take out, right, I mean there's fewer resources and more data coming in. So how how do you manage all of the data? Absolutely you need to have AIML. So Informatica's CLAIRE product helps with suggestions and recommendations for algorithms, matching those algorithms. Deloitte has our own MDM elevate solution that embeds AIML for data stewardship. So it learns from human data inputs, and you know, cuts through the mass of data records that have to be managed. >> You know Rajeev, it was interesting, last year we were talking, the big conversation was moving data around is really hard. Now there's solutions for that. Move the data integrity on premise, on Cloud. Give us an update on what's going on there, because there seems to be a lot of movement, positive movement, around that. In terms of, you know, quality, end to end. We heard Google up here earlier saying "Look, we can go into end to end all you want". This has been a big thing. How are you guys handling this? >> Yeah absolutely, so in today's key note you heard Anil Chakravarthy and Thomas Green up on the stage and Anil announced MDM on GCP, so that's an offering that Deloitte is hosting and managing. So it's going to be an absolutely white-glove service that gives you everything from advice to implement to operate, all hosted on GCP. So it's a three-way ecosystem offering between Deloitte, Informatica, and GCP. >> Well just something about GCP, just as a side note before you get there, is that they are really clever. They're using Sequel as a way to abstract all the under the hood kind of configuration stuff. Smart move, because there's a ton of Sequel people out there! >> Exactly. >> I mean, it's not structured query language for structured data. It's lingua franca for data. They've been changing the game on that. >> Exactly, it should be part of their Cloud journey. So organizations, when they start thinking about Cloud, first of all, what they need to do is they have to understand where all the data assets are and they read the data feeds coming in, where are the data lakes, and once they understand where their datas are, it's not always wise, or necessary to move all their data to the Cloud. So, Deloitte's approach or recommendation is to have a hybrid approach. So that they can keep some of their legacy datas, data assets, in the on premise and some in the Cloud applications. So, Informatica, MDM, and GCP, powered by Deloitte, so it acts as an MDM nimble hub. In respect of where your data assets are, it can give you the quick access to the data and it can enrich the data, it can do the master data, and also it can protect your data. And it's all done by Informatica. >> Describe what a nimble hub is real quick. What does a nimble hub mean? What does that mean? >> So it means that, in respect of wherever your data is coming in and going out, so it gives you a very light feeling that the client wouldn't know. All we- Informatica, MDM, on GCP powered by Deloitte, what we are saying is we are asking clients to just give the data. And everything, as Rajeev said, it's a white-glove approach. It's that from engagement, to the operation, they will just feel a seamless support from Deloitte. >> Yeah, and just to address the nimbleness factor right, so we see clients that suddenly need to get into new market, or they want to say, introduce a new product, so they need the nimbleness from a business perspective. Which means that, well suddenly you've got to like scale up and down your data workloads as well, right? And that's not just transactional data, but master data as well. And that's where the Cloud approach, you know, gives them a positive advantage. >> I want to get back to something Abhiman said about how it's not always wise or necessary to move to the Cloud. And this is a debate about where do you keep stuff. Should it be on on prem, and you said that Deloitte recommends a hybrid approach and I'm sure that's a data-driven recommendation. I'm wondering what evidence you have and what- why that recommendation? >> So, especially when it depends on the applications you're putting on for MDM, and the sources and data is what you are trying to get, for the Informatica MDM to work. So, it's not- some of your social systems are already tied up with so many other applications within your on premise, and they don't want to give every other data. And some might have concerns of sending this data to the Cloud. So that's when you want to keep those old world legacy systems, who doesn't want to get upgrades, to your on premise, and who are all Cloud-savy and they can all starting new. So they can think of what, and which, need a lot of compute power, and storage. And so those are the systems we want to recommend to the Cloud. So that's why we say, think where you want to move your data bases. >> And some of it is also driven by regulation, right, like GDPR, and where, you know, which providers offer in what countries. And there's also companies that want to say "Oh well my product strategy and my pricing around products, I don't want to give that away to someone." Especially in the high tech field, right. Your provider is going to be a confidere. >> Rajeev, one of the things I'm seeing here in this show, is clearly that the importance of the Cloud should not be understated. You see, and you guys, you mentioned you get the servers at Google. This is changing not just the customers opportunity, but your ability to service them. You got a white-glove service, I'm sure there's a ton more head room. Where do you guys see the Cloud going next? Obviously it's not going away, and the on premise isn't going away. But certainly, the importance of the Cloud should not be understated. That's what I'm hearing clearly. You see Amazon, Azure, Google, all big names with Informatica. But with respect to you guys, as you guys go out and do your services. This is good for business. For you guys, helping customers. >> Yeah absolutely, I think there's value for us, there's value for our clients. You know, it's not just the apps that are kind of going to the Cloud, right? I mean you see all data platforms that are going to the Cloud. For example, Cloudera. They just launched CDP. Being GA by July- August. You know, Snowflake's on the Cloud doing great, getting good traction in the market. So eventually what were seeing is, whether it's business applications or data platforms, they're all moving to the Cloud. Now the key things to look out for in the future is, how do we help our clients navigate a multi Cloud environment, for example, because sooner or later, they wouldn't want to have all of their eggs invested in one basket, right? So, how do we help navigate that? How do we make that seamless to the business user? Those are the challenges that we're thinking about. >> What's interesting about Databricks and Snowflake, you mentioned them, is that it really is a tell sign that start-ups can break through and crack the enterprise with Cloud and the ecosystem. And you're starting to see companies that have a Sass-like mindset with technology. Coming into an enterprise marketed with these ecosystems, it's a tough crowd believe me, you know the enterprise. It's not easy to break into the enterprise, so for Databricks and Snowflake, that's a huge tell sign. What's your reaction to that because it's great for Informatica because it's validation for them, but also the start-ups are now growing very fast. I mean, I wouldn't call Snowflake 3 billion dollar start-up their unicorn but, times three. But it's a tell sign. It's just something new we haven't seen. We've seen Cloudera break in. They kind of ramped their way in there with a lot of raise and they had a big field sales force. But Data Bear and Snowflake, they don't have a huge set in the sales force. >> Yeah, I think it's all about clients and understanding, what is the true value that someone provides. Is it someone that we can rely on to keep our data safe? Do they have the capacity to scale? If you can crack those things, then you'll be in the market. >> Who are you attracting to the MDM on Google Cloud? What's the early data look like? You don't have to name names, but whats some of the huge cases that get the white glove service from Deloitte on the Google Cloud? Tell us about that. Give us more data on that. >> So we've just announced that, here at Informatica World, we've got about three to four mid to large enterprises. One large enterprise and about three mid-size companies that are interested in it. So we've been in talks with them in terms of- and that how we want to do it. We don't want to open the flood gates. We'd like to make sure it's all stable, you know, clients are happy and there's word of mouth around. >> I'm sure the end to end management piece of it, that's probably attractive. The end to end... >> Exactly. I mean, Deloitte's clearly the leader in the data analytics space, according to Gartner Reports. Informatica is the leader in their space. GCP has great growth plans, so the three of them coming together is going to be a winner. >> One of the most pressing challenges facing the technology industry is the skills gap and the difficulty in finding talent. Surveys show that I.T. managers can't find qualified candidates for open Cloud roles. What are Deloitte's thought on this and also, what are you doing as a company to address it? >> I mean, this is absolutely a good problem to have, for us. Right, which means that there is a demand. But unless we beat that demand, it's a problem. So we've been taking some creative ways, in terms of addressing that. An example would be our analytics foundry offering, where we provide a pod of people that go from data engineers you know, with Python and Sparks skills, to, you know, Java associates, to front end developers. So a whole stack of developers, a full stack, we provide that full pod so that they can go and address a particular business analytics problem or some kind of visualization issues, in terms of what they want to get from the data. So, we teach Leverate that pod, across multiple clients, I think that's been helping us. >> If you could get an automated, full time employee, that would be great. >> Yeah, and this digital FD concept is something that we'd be looking at, as well. >> I would like to add on that, as well. So, earlier- with the data disruption, Informatica's so busy and Informatica's so busy that Deloitte is so busy. Now, earlier we used plain Informatica folks and then, later on because of the Cloud disruption, so we are training them on the Cloud concepts. Now what the organizations have to think, or the universities to think is that having the curriculum, the Cloud concepts in their universities and their curriculum so that they get all their Cloud skills and after, once they have their Cloud skills, we can train them on the Informatica skills. And Informatica has full training on that. >> I think it's a great opportunity for you guys. We were talking with Sally Jenkins to the team earlier, and the CEO. I was saying that it reminds me of early days of VMware, with virtualization you saw the shift. Certainly the economics. You replaced servers, do a virtual change to the economics. With the data, although not directly, it's a similar concept where there's new operational opportunities, whether it's using leverage in Google Cloud for say, high-end, modern data warehousing to whatever. The community is going to respond. That's going to be a great ecosystem money making opportunity. The ability to add new services, give you guys more capabilities with customers to really move the needle on creating value. >> Yeah, and it's interesting you mention VMware because I actually helped, as VMware stood up there, VMCA, AW's and NSA's offerings on the Cloud. We actually helped them get ready for that GA and their data strategy, in terms of support, both for data and analytics friendliness. So we see a lot of such tech companies who are moving to a flexible consumption service. I mean, the challenges are different and we've got a whole practice around that flex consumption. >> I'm sure Informatica would love the VMware valuation. Maybe not worry for Dell technology. >> We all would love that. >> Rajeem, Abhiman, thank you so much for joining us on theCube today. >> Thank you very much. Good talking to you. >> I'm Rebecca Knight for John Furrier. We will have more from Informatica World tomorrow.

>> Live from London England, it's The Cube, covering .Next Conference Europe 2018, brought to you by Nutanix. >> Welcome back to The Cube, I'm Stu Miniman with my co-host Joep Piscaer. And you're watching The Cube, and actually Bear Grylls is going to be on the keynote shortly, but we're gonna talk a little bit more tech first. First of all I wanna welcome back to the program Chris Kaddaras is the senior vice president and general manager for EMEA with Nutanix, and welcome to the program for the first time, Sylvain Siou, senior director of Systems Engineering, also for EMEA with Nutanix. Gentlemen, thanks so much for joining us. >> Thank you for having me. >> Alright so Chris, we were thinking back, two years ago, the first European show in Vienna, I had you on the program, and you were fresh on, I always loved getting people when they're fresh into the company because they have the why they're joining in, why they think they're doing things. So, bring us up to speed. Two years, couple things have changed in Nutanix, couple things have changed in the industry, but why don't you bring us up to speed? >> Sure, no I'm happy to do that. First I'll tell you that some of the things I told you on the show two years ago actually proved true. I could see the energy in Vienna at that time in regards to what I call kind of a religious following in Nutanix because of the compelling-ness of the technology and the solution, and that hasn't stopped. One thing that I wasn't quite prepared for is just the rate of growth of this company, and how our customers really embraced us in the market. Now in the EMEA market we've had some success I would say. The team's done a really good job. When I started we had less than a thousand customers, now we have over 3,000 customers. When I started with Nutanix, in the region we had about 200 employees, now we have almost 800 employees in the region. So collectively as a region we're growing a bit faster than the rest of the world which is a good thing for us, and customers are showing their appreciation for us, so it's been a really good experience, but something like the hyper-growth that we have at Nutanix takes some getting used to when you come from other companies, but it's been a really good thing for our customers. The thing that I think I'm the most proud of is we've done that hyper-growth and we've still kept our NPS score above 90 for our customers, so our customers are getting a really good experience both from our sales teams, our product, our implementation teams, and our support teams, that it's kept everything in check for our customers which I'm really proud of. >> Well congratulations on that. Sylvain I have to think that your team has something to do with that NPS score. In my career, I have great respect for the SEs, they're the one that have to not only know the product inside and out, but they need to be working closely with the customers, have a good viewpoint on the customers. Being here at a European show, I wanna get your viewpoint. Tell us, what's different here compared to what you hear from people back at Corporate, what are some of the differences here your team sees? >> So we have a very good relationship with Corporate, so we're really aligned and we're involved in the project in same way as any other region. I think we were faster on some very big accounts, and that was really surprising and also the, I think the timing for the need of the customer to solve situation after virtualization was the exact timing when we start in EMEA, the product was mature enough so that was exactly the right timing, it's five years ago when I joined, so really we solved this first situation and after that everything we promised in term of making this platform a true cloud platform for enterprise is there, I think all these services on top of it, who have the same kind of services you can see on public cloud, is there, we show it this morning, and now giving the ability to the customer to manage situation with this cloud from different providers and what is on premise is there, so I think all the control, the costs on the compliance and so on have done a lot to manage the situation and take you through the control everyday. >> So, what is the adoption maybe compared to the US for the core products that you have now versus the additional services? Is there a big change or a big difference between the US and Europe or, what are you seeing with your customers? >> So, we follow the same path. There is some region and maybe I will relay on Chris, some region that we invest later than the others so, of course France, Germany, UK, Northern Europe was really the beginning and after that we have more southern regions or eastern region that come after, but we are surprised sometimes because people can jump to the last technology faster than the others, so I don't think there are really rules, there is really people who is painpoint, we have the solution, and when it fits, they go faster. >> Yeah I think from a solution perspective we are thriving at the same rate our emerging technologies into the market as our other regions in the world. In some cases we're ahead, things like IoT, what was originally called Sherlock, we're ahead, we have like first customer, second customer to start coming to adopt, so we do have markets within the EMEA region that are much earlier adopters compared to other regions. Think of places like the Middle East, the Nordics, France, adopting much quicker than some other regions of the world. So we see our new products starting to roll, we're really excited about Xi Leap, I know that the first instantiation went live, I think yesterday or today within the Americas, we're looking forward to going live within London, and then moving in to mainland Europe from there, and I think that will be a huge difference-maker for us in the markets as well. >> So looking at those regions specifically, I know there's a couple of markets in Europe, especially Germany, that have such strict data sovereignty laws that it makes it really difficult to actually do business from a DR or cloud perspective. How's Nutanix dealing with that? >> I think that's where we... When we have our SAS-based products, that's a challenge. When we have our cloud-based products, that's a challenge.` So, for our cloud-based products we have a plan really quickly to go into places that have data sovereignty compliance regulations that they have to adhere to. So Germany, we have a plan to go into Germany really quickly; we obviously have a plan to go into some other markets, Amsterdam, we have a plan to go into London for cloud. For SAS, a lot of customers are consuming SAS and they're okay if there's a good security problem, parameter around SAS, and they're consuming Salesforce.com without data centers, they're consuming other products that way so, as long as we put the right security parameters in place, then their consumption model around SAS is typically gonna work, I don't see us distributing SAS data centers all throughout every market in the world to do that. Our core product right now consumption is mostly local, and it's consumed either in an appliance way or it's consumed in a software way, so that's not something that we have to worry about. >> Yeah it's interesting, you wonder if North America has a greater adoption of public cloud, if that actually gets you an advantage in the EMEA region here to get deeper with some of the core and essential offerings. >> It does; customers will adopt a private cloud because of those data sovereignty regulations. But a lot of the uber-clouds have come in and solved that, they've come in into country, they've created gov clouds, they've done it in Germany, they've done it in the UK, so they're starting to solve that, but they have to put out a lot of investment to do that. But it has given us a lead in the marketplace, but there are certain markets that are very much like the US market, so the UK, it's very similar to the US market with regards to uber-cloud or public cloud adoptions so in that market we have a lot of opportunities with somebody like Beam, because they've consumed a lot of the other uber-clouds, whether it's AWS, UCP, or... And we have that opportunity to sit down and provide them with solutions. >> Sylvain, what else are you hearing from your customers, what are some of the pain points that they're feeling that your team's able to help with? >> Clearly in the past we saw the proliferation of the VM, and we find a way to control that, but with the cloud the proliferation is without any limits. So really this is something important for the customer to take back control, take control of the shuttle IT and so on, and it's very lowly. And also I want to take a specific point really the R&D are really taken care of when we see in the field, I will take just an example, the synchronus replication, metro-culturing and stuff like this to high availability, between (inaudible) and so on, it's typically European, because we have fiber, we are really city close to each other and so on, in America, that makes no sense, and really at really early stage of the company we get the R&D taking care of that, developing specifically for our market what is needed for our market, and it means that we're a really global company and not really American company, we have also R&D in different places, we have in Serbia with Frame, we have in India, and so on, so really to be really taking care of each issue or pain point of the customer is really our main driver. >> So one of those other differences I see a lot is the scale of the organization, the size. So what is an SMB in the Americas might be an enterprise in Europe. So what are the solutions you have for those types of customers, for that problem? >> So definitely we need, so we are talking to customers we have a critical science, they need to have a minimum of VM to face the issue of the bottom neck of the storage or the management part and so on, but also we have example of small customers just need a platform that works, and don't want to have anyone taking care of it. And so now it's like you phone, you don't take care of the storage and CPU, it's just your application and that's it, could be internal, external, and so on, so really the SMB of course is not the main market for us, it's more the big account and so on, but we have all kinds of customers in any verticals, there is no specific one that we cover, and it's really because the platform is something that has become just normal to be invisible. >> Yeah I would add on that, if you don't mind, I'd say that the nice thing about the product is it's in a form factor in a pricing mechanism that can be consumed from SMB all the way up to global accounts. That's the nice thing. Now, maybe we spend a lot of our field resource on mid-market up, because that's where we get larger transactions from customers, and it's just a value conversation with regards to return on investment, but the nice thing is our product can be consumed at the smallest customer. We have just released new pricing mechanisms that allow our customers to now consume at much smaller levels, so we're not allow for SMB but for ROBO, because if you think about it if you just have a one size fits all pricing structure how does that work in the data center, that same price doesn't work in the ROBO area, so you have to give the customers the ability to look at the same experience in the remote office or the small sites compared to a data center, and that's something that we've just kinda brought to the market in the last three to four months, and I think that's a real advantage of not only the product but the pricing structure. >> Chris, we wanna give you the final word. If EMEA customers, what do you want them taking away from this week? >> Sure. I think, they've already told me, and I'll tell you, which is good, 'cause it's what I want them to take away, is just the credibility that Nutanix is here for the enterprise work load, they can look at their entire data center delivery mechanism on a Nutanix platform. But also Nutanix is a company they should be looking for for their cloud-based platform. There is a decision in the marketplace to be had right now around what do you use for your cloud, lack of a better word, orchestration layout, cloud automation layout? And there's only a few choices in the market today, some of them are more open source, some of them are specific vendors, and what I want them to take way is Nutanix is an option for that, leave it up to me and my team to prove why we think we're the best option for it, but that's really what I want them to take away, the credibIlity of tier one platforms running Nutanix in their data center, and then two, Nutanix for the cloud-based platform. >> Congratulations on the progress. I wanna say some feedback I've heard from customers is despite how fast Nutanix has been growing, they still feel that they're getting the personal touch, don't feel like just a number for some fast-growing company so congrats on that, I know a lot of effort goes into that. Alright so we're at the end of the Day 1 for Joep Piscaer, I'm Stu Minimn, be sure to join us tomorrow for a full day of wall-to-wall coverage. Of course go to theCube.net for all the websites to watch us live and on demand for all the shows we're doing and once again thank you for watching the cube. (digital music)

(news theme music) >> Hi, I'm Stu Miniman and welcome to theCube's preview of Nutanix.next London 2018. Happy to welcome back to the program two friends of the program, Julie O'Brien who's the Senior Vice President of Corporate Marketing and Sunil Potti who's the Chief Product and Development Officer, both of Nutanix. Thanks so much for joining us. >> Yeah, it's great to be here again. >> Alright, so, we've been there since day one. I was actually, just recently down at the Fontainebleau in Miami reliving one of my favorite sets that we did. It was beautiful Miami colors, which match the bright green and blue of Nutanix with theCube. I've been to every single one of em. You have. The European version, which is the third year. We did Vienna. We did Nice. And now London. So Julie, start us in as what we can expect this year. >> Sure, we actually just finished our .next tour in APJ in the Americas. We were from Beijing to Boston. Over 20,000 registrants and 44 cities. So, now we're coming off of that and heading into the conference, which is our multi-day event. First time being in London for the multi-day conference. We have a great lineup of speakers. From a main stage perspective, Bear Grylls. Who you may be familiar with. "Man vs. Wild" He's a well known survivalist. I'm sure he'll have tips to connect what we survive every day in technology with what he survives in the wilderness. We're going to have Jane Goodall joining us. Renowned anthropologist. She's giving back to conservation. A phenomenal woman who's going to be on stage with me in a fireside chat. Cannot wait for that. Anna Alex from a startup in Berlin, called Outfittery. We always like to bring in some fun, interesting companies from the region. They're actually using a mashup of AI with their clothing business, to figure out how to dress elegant professionals, such as yourselves, with all of the right clothing items. So she should be a lot of fun. And then I did want to share something really special today. There's breaking news that we haven't shared anywhere else yet on one of our new main stage speakers. For those of you who are football fans, this gentleman was one of the top performing German national football team members, when he played. And his name is Michael Ballack. So, he'll be joining us and we're really excited about that. For all the Germans out there, hopefully they'll be thrilled. >> We'll do some light juggling on the keynote. (Julie laughs) >> One of the things I always love about this show is customers always want to expand their horizons, learn new products, get to know what they have even better, help their job, but also expand your mind some. You've had some great thought leaders on the program. I've had the opportunity of interviewing some of them on theCUBE, which is great. Authors I've read. Professors that you read their research. Thought leaders in the space. It's always fun. But, the main reason most people go to Sunil is to learn about the solutions that they have, learn about some of the cool new stuff, and you're always well dressed on stage, and helping the customers understand where things are today and where they're goin. So what can they expect from you? >> I think this time around, just like prior times, is going to be a bit of the continuation of the journey, which is what is practical about the company, is that the vision continues to be consistently evolving. In a sense that we've embarked on this two-part re-architecture of the enterprise cloud. And in the first act it was all about converging various silos of infrastructure. We called it the Invisible Infrastructure Era. And then we believe, and you'll see a lot of this in .next London, is that a little more light around the reality that we are on the cusp of the world of many clouds. From going from the world of many silos of infrastructure to the world of many clouds. And a lot more depth of products, beyond what we've done in the first act around invisible infrastructure transforming to invisible clouds, is what's going to be the underpinning of the keynote. >> You bring up something we've been watching at a lot of the shows and in our research, cloud was supposed to be, many people thought it's going to be simple and and it's going to be inexpensive, and what we've found is that it's often neither of those. We live in a multi-cloud world. Absolutely. The question I have for many users is, how did you get there? Was it by choice? Do you have a good plan and who's going to help you get your arms around things or have we recreated, through multiple clouds and applications everywhere, the silos that we were trying to collapse in our data centers before? >> And I think some of this is also going to be, just like in any problem-solving, define the problem well is 50% of the solution. So in some cases, in the world of multi-cloud, one of the things that we've had to give some time and it's right of passage, is to really characterize, when we say multi-cloud, most people think it's just public and private. So it's to really characterize the problem of the multiple clouds, or the multi-cloud era, actually is a construct of many public clouds, but the "private cloud" is becoming increasingly more dispersed or distributed. All the way into the remote office branch offices. But also all the way into what we are calling the edge. Part of what we're going to be talking about is a pretty reasonable understanding of how we've seen some of our early customers templatize their different kinds of clouds and then overlay the solution, to say it's not one size fits all, but you need, from an operational perspective, at least, something that can be a single control play. >> You're absolutely right. If you follow the applications and you follow the data, it's becoming even more dispersed. I remember the early days when I first spoke to Dheeraj, it was, oh are we taking a bunch of boxes and collapsing it? And what it came down to is the premise is the challenge of our time is software for distributed architectures. Five years ago we weren't talking about edge computing and IOT and all those things, but that's following along those trends. >> And I think one of the core technical themes you're going to see is that the last ten years of cloud has been about the era of scaling out. And that's proven now and there's more to be done. I think to really fulfill this next ten years, you're going to see this thematic view of scaling in. Especially when you scale small, which is a different art than scaling out, to some extent. Especially if you want to solve problems at the edge, you want to do it consistently, so that you can actually follow the app, as the apps transform. Some of these newer architectural paradigms have to be understood. So that's going to be an underlying theme there. >> And edge computing, we know, is a really hot topic amongst our customers and this year we're going to have an API accelerator lab. So in New Orleans we had a hackathon, now we're going to do it a little bit differently. This is going to be really focused on giving people an opportunity to get their hands involved in our IOT product, along with some nooks as well. So it should be a lot of fun for people. This is a great area and it is a great application for that multi-cloud, distributed edge kind of environment. >> Great, so November 27th through 29th, in London. We're going to have two days of theCUBE, of course go to thecube.net and watch the program. Nextconf has always been the hashtag. I want to give you both the final takeaways, what people should tune into, other than, of course, watching your keynotes and theCUBE coverage. >> I think you'll see a lot on social media, hopefully to stay involved with all of the innovation that we're going to be announcing. You're going to hear a lot from the breakout sessions. People will be tweeting from those sessions. We have more than 60 breakout sessions across a range of topics, for people that are in different phases of their journey with us. Whether it's just hyperconverged infrastructure, whether it's blockchain, whether it's IOT and they're starting to think about the multi-cloud hybrid environment too. So there's going to be a lot of great information coming out of the events. Sunil? >> I think you covered it all, but in general there's going to be a lot of cool stuff, both people-wise, as well as technology-wise. But I think, hopefully, the common theme that every body will participate in is this construct of this whole Nutanix-vibe of dreaming big, acting fast, and having fun. >> Okay, good. Julie and Sunil, thank you so much. And also breaking news, we're actually going to have a first on the program. We've got my first European cohost for a multi-day event, Joep Piscaer, who's cube alumn, been on a couple of times. And what I'm actually looking for our audience, I'd like to do my first non-english interview on theCUBE. Joep is fluent in Dutch. He's going to be taking the train into London. I would love to be able to do a short segment, preferably a user, but would welcome a thought leader, a partner, or somebody in there to be able to. As we've expanded our coverage, we did our first Chinese event last year. We've done many in Europe. We did our first Middle East show in Bahrain just a couple of weeks ago. So look for that. Like Nutanix, we're all over the globe with what we've done. Julie and Sunil, thank you so much. For Stu Miniman, once again, thank you for watching theCUBE. (news theme music)