(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)

the cube presents ignite 22. brought to you by Palo Alto Networks hey guys and girls welcome back to Las Vegas it's thecube we are live at Palo Alto networks ignite 22. this is day one of two days of cube coverage Lisa Martin here with Dave vellante Dave we've had great conversations today talking with Executives the partner ecosystem is evolving it's growing at Palo Alto networks going to be digging into that next well we heard a lot of talk about you know Palo Alto you know the goal 100 billion dollar you know market cap company and to me a way and I think a critical way in which you get there is partner with the ecosystem because you can't do it alone the power of many versus the resources of one agree completely agree we've got Carl Sutherland with us SVP of North America ecosystem sales at Palo Alto networks welcome to the cube thanks so much for having me it's great being here so here we are the first full day of the conference actually started yesterday with the partner Summit give the audience a flavor of the partner Summit who was there what was talked about what's the current voice of the partner these days yeah great questions so we had a 150 Partners from around the globe representing all of our different routes to Market and for us our partner Community is expanding we work with system integrators we work with gsis we work with service providers Distributors traditional value-added resellers so it was a whole host of partners that were there it was a c-level audience and we really talked about the direction of where we're going as a company how they can continue to invest with us and have greater success long term and so from a voice of the partner standpoint what they're here to do is share with us where they want to engage more how we can enable them to be successful you talked about the Power of One Versus a community we're really looking at a segment of the marketplace right now for us to scale and hit our aspirational goals we can't do it with Palo Alto Network employees we have an employee base of 12 000 people if you take our ecosystem it's over a hundred thousand employees so if we can get them aligned and selling and motivated it's going to be a good day for all of us what so what are they telling you where do they want to spend their time where do they want to add value where are they winning yeah that's a great question so there's a transformation that's going on right now in the partner Community what's happening is a lot of Partners going that are transitioning from what would be traditional transactional Partners or resale Partners to being services-led and the Market's driving them there and what I mean by that is that customers are in a desperate dire State needing assistance figuring out and solving these very complex security problems so if there is a subset of Partners out there that have the skill set and capabilities that can come in from a consultative standpoint help them to develop the structure through deployment a full-blown management and do life cycle management that's a tremendous value I mean the numbers you hear thrown around in the industry right now is up to seven million uh security I.T jobs right now that are out there the open head count is tremendous people can't hire people fast enough all of us in the industry are going through and trying to find early in career or college graduates so we can train quickly or cross-train from other segments to get them into cyber security so if our part of the community can continue to get skilled and expand it's only going to help and the cloud is obviously where does the cloud fit in Carl because you know a lot of the partners when the clouds really start on the Steep part of the s-curve are like we have an opportunity here and by the way if we don't transition our business we could get commoditized yes so that you know that but you were talking about the transactional we can help people move to the cloud and a big part of that has got to be we can secure them in the cloud because it's a more in a lot of ways you know Cloud security is great but in a lot of ways it adds complexity what are you hearing from the party yeah so we are fortunate at Palo Alto networks when you look across the three loud largest cloud service provider from a Google AWS and Microsoft Azure we're either their number one isv or absolutely their number one security ISP so we've got a great uh relationships with them now our partners are coming along and saying how do we transact how do we add value a lot of times that value to your question is wrapping services around it to make sure it's a successful deployment because exactly what you stated the complexity is an all-time high so how do we make sure that we can solve a complex problem in a short term while increasing their security posture and that's really the goal and so where there there's sometimes complexity and mystery there's opportunity and partners can be profitable in doing that I wrote a piece once chaos is cash I have a security you know the criminals and vendors as well yes yes where there is is challenge and complexity there is great opportunity yeah talk about some of the partner program Evolution and some of the things that were announced with respect to the next wave program just yesterday yeah so at next wave um the program's been around for 12 years we constantly are looking to make enhancements and how we make those enhancements are by going out and speaking with these partners and listening to what they need so I have the honor to get to represent what their needs are and how we bring it to market for them so a couple interesting announcements that we made yesterday first of all we announced a new structural format for the program which is really going to allow our different route to markets to have a program that's fit for them because in the past when we were just traditionally a firewall company when the ecosystem just meant resale it was an easy model to have it's complex right now sometimes it's resale sometimes it's influence sometimes its services only we really need to be flexible and credible so we announced a Services only path so if you are a consulting company if you are a insurance company and you want to bring opportunities and leads to Palo Alto Network and you want to provide the services if you're not interested in the transaction you don't want to get involved in that we now have a pathway for you to support you to enable you and Kennedy to give you recognition within Palo Alto networks from an alignment standpoint so we're super excited about that uh as I know you guys speak quite a bit about the managed Services industry so it's a red hot area within Palo Alto networks one of the needs out there was that all not all managed Service Partners are created equally and so some have fantastic capabilities some have gaps we were calling it a P2P part of the partner program within managed services so our two managed Services Partners can actually work together to solve the problem that the end user has and give them a better outcome and fill each other's gaps so candidly it's been going on for a while the partnering but we've never really recognized it so we really built a program around it and now are sponsoring and supporting it versus people doing it on a sidebar so those guys were here in force yesterday yes sir right and and so obviously a lot of energy I'm sure do you see a day where they're here in force on the show floor yeah and and how do you see that evolving so they are here enforcement just right here you see a few of them I'm looking at AWS who's our you know we are their largest isv I'm looking at CDW we had them on the floor is our if not largest second largest partner globally right now and continuing to grow at a rate well they will probably be our first billion dollar partner to think about the size and scale of that relationship and where we've come from um their name CDW don't they never really thought of CDW right as a as a security firm wow what a transformation but please carry on and think about that let's talk about CDW saying think about reach that CDW has it's a 23 billion dollar organization and in a way an inside out sales model meaning there's a tremendous reach they have from their inside sales team and the relationships that they have traditionally historically they were procurement relationships in a way and I said this to the CDW team they were the easy button in the past now what they're doing is they made Seven Acquisitions over the last two years all of them Services oriented so now they're coming in as a consultative Viewpoint and solving a lot of complex problems and I see Google Cloud right here another great partner for us that we continue to invest in we have a great amount of integration and Technology integration with them and so and those are the three that I'm seeing just looking over my left shoulder right if I turn around I'll probably name five more so the majority of this room are the partners that fall within our ecosystem today fantastic so okay so what's your vision for where you want to take this ecosystem because as I said at the top I mean ecosystems are sort of the Hallmark of a I guess you're not a cloud company see I think you of you as a cloud company and so okay good so and I know you don't own your own public cloud and you know your history is you had your own data centers but yeah but you're the security Cloud yeah and so a security Cloud any Cloud needs a great ecosystem so what's your vision for the ecosystem let's go you know five plus years out sure you we start with the end in mind and what I mean by that is we always start with the end user what's the end user's needs the end user today needs flexibility with how they consume the technology they need help in how they support and deploy the technology they need guidance in how they plan out for their future and what their growth is so what we're doing is building a very diverse set of Partners in our ecosystem that all have special skills that they bring to the table so when nikesh sits up here and talks about being a 10 billion or a 20 billion or a 50 billion dollar company we absolutely cannot do it without our ecosystem and without having a very diverse ecosystem that all has different skills that can help us scale because again Palo Alto does not want to be a services company right let's work with the people who are the best at that when we think about the deloittees and accentures and the value they have within the end user base and our joint customer base what a fantastic time to to partner together and solve those boardroom challenges and that's where I really see the vision is that at the boardroom we're building out a plan that's three to five years that's going to continue to increase their security posture because we're not thinking if we're not forward thinking like that will be left behind because the Bad actors are thinking about how they find the different areas to penetrate they're getting so sophisticated the badocracy adversaries they are well funded they're motivated Grant the ransomware attack numbers in terms of the Velocity the complexity yes no longer are we going to get if it's when yeah uh big challenge for organizations Acro across I mean really across an organization regardless of Industry are you guys having any conversations with boards in the partner organization to help align the board with the executive level and really not just have security as a board level initiative but actually being able to execute a strategy yeah and you you nailed it it's not an initiative the initiative to me means there's a beginning and an end right a strategy means there's going to be a comprehensive approach how you continue to improve and we are very fortunate that a lot of our largest Partners around the globe have that position within the boards where they are the trusted advisor so what we're doing now is enabling them and giving them the skills so they can have a more comprehensive conversation around our platform approach around the challenges you know BJ I knew who was with you earlier today likes to say that the average customer he goes and sees has 50 to 70 disparate Technologies within their environment how do you manage that how do you maintain it how do you do renewals oh and by the way most likely the people who actually initially procured that aren't with you anymore they're in a different company so the need for a platform approach is there more so than ever but the decision for the platform quite often has to come from the most senior levels within the organization because again I'm going to go back to your what was your chaos line that you said chaos is Cash chaos is Cash well also chaos is job security so if you're at at the lower level within an organization that chaos and that magic gives you a little job security but that's a short term long term you really need to think about how you're protecting the environment holistically so it is a boardroom decision down that we need to have and you know that chaos the the motivation for that piece that I wrote was from the criminals standpoint right and then I was like okay but there's great opportunities for the technology industry but but I think that you know where we're headed I wonder if I get your thoughts on thoughts on this Carlos we always talk about the Board Room I think we're going now Beyond it here I am you know I'm hypersensitive about my security I got password managers two-factor authentication I don't want SMS based two-factor authentication I want my own authenticator and that's still not enough yeah I got air gaps yeah you know for my crypto you know and I'm super paranoid my point is I think the the individuals are getting much more Savvy about security why because we've all been hacked you know it's like when you lost your data in the because you weren't backed up you know that never happens anymore it's in the cloud or you know some people have multiple backups so it's it's becoming a cultural Trend beyond the board and it's because of the board lord said hey this is really important and so I think it's not only top down I think you're going to see bottom up and middle out and the exciting part for Palo Alto networks is and maybe for you as well is there any more exciting environment to talk about that's rapidly changing and constantly changing you could come back next week and our conversation is going to change as far as what we're doing we constantly need to be thinking three steps ahead of where we're going to move and be flexible and dynamic enough to change and that's what's going to keep us ahead of the economy yeah there's no segment as Dynamic I mean data is dynamic but not as fast changing as cyber I mean because of the adversary as you mentioned I mean so smart so now now they have open adversary ecosystems I mean the adversaries are building ecosystems right absolutely insane I've got peers that are bad guys yeah right right chaos is Cash what's your favorite partner story that you think really demonstrates the value of the ecosystem that Palo Alto networks has built yeah so without sharing names I'll talk about a large U.S national partner that was very uh that was founded on a networking business and partnered with a very large networking company and built that business and was successful doing that they wanted to Pivot into the security space and very early on they made a commitment to Paulo and Ulta networks to say we're going to learn we're going to invest we're going to align with your sales force and we're going to work together and right now they are our largest partner globally and they grew 70 year over year wow so think about that this is not on a small base we're talking about a half a billion dollars in Revenue growing at 70 year over year because to your point earlier it wasn't an initiative it was a strategy and they're executing on the strategy so I tell a lot of we call War Stories like that to other partners that are looking to invest from different markets it could be a large service provider that's you know trying to transform themselves into a security player and talk about the potential of what it could be in for their Marketplace and by the way I say publicly quite often Palo Alto networks will be your most profitable relationship that you have because of the total addressable Market that we're going after because of the solutions that we bring to Market and because of the opportunity within the end users right now and we're excited I want to come back to the mssp in that in its context so we've seen the rise of the mssp and particularly you know we were talking earlier I think it was with Wendy that uh no it was with CDW like 50 of the organizations in North America don't even have a sock yeah right so they need a service provider to come out so you said we you don't want to be in the services business right you're a product company right and that's from a financial standpoint that's phenomenal you're roughly 50 billion dollar market cap company let's let's call it six billion in Revenue so that's a nice Revenue multiple 8X you know and and and the Market's down so you're a 10x Revenue multiple company typically services companies are a 1x or a 2X are you seeing a change there where technology is giving these service providers operating leverage where they're able to scale whether it's because of the cloud because of the Partnerships the Eco would you call it before the the peer-to-peer ecosystem yes like the Gap fillers yes are you do you see the economics of services changing yeah from a baseline economic standpoint not looking at the valuations but let's look at it from a an opportunity to be profitable with Palo Alto networks we know if you are just doing the transaction you have a certain range of margin that you're going to make in the opportunity we know if you wrap services around it you're going to get 3x to 4X that margin we know that if it's managed services and there's life cycle management you're talking 5x to 8X that initial transaction and by the way it's recurring revenue for them so when you think about it if you just do a transaction you're only recurring revenue is a renewal that's predictable but it's not extremely profitable now we're saying the operating leverage you get is if you wrap that services and you're going to have an increased opportunity for a greater margin and it's sticky it's hard to replace a partner who's adding value to your team and A lot of times you walk in the end user you can't tell who the partner is and who the end user is because they are one team that's value yes and that's going to drive ebit yep for your partners and that's going to drive valuation you know you know I want to come back to valuation not that I'm not you can do that okay but because I was I predicted I do my prediction post every year and I predicted last year that we're going to see you know a Spate of MSS mssps I predicted you're going to see someone go public nobody's going public these days but I still think it's a great business yeah that's an untapped opportunity it's not an 8X or it's not a software marginal economics or but it's really sticky super high value yeah and I think it has you know long-term potential yeah to your point if you want to talk valuations for a second let's look at what's happened to the marketplace over the last 12 to 18 months the large majority of the non-public partners that we work with have taken on Capital from private Equity the private Equity that has come in has challenged them to go through a transformation that transformation is you we need you to be Services LED and that service is value because they believe there's going to is going to be a great greater evaluation from that end and they'll be able to scale and grow and stay ahead of the market doing that so when we have conversations when I have conversations yes I'm talking about the technology and the direction of the company but I'm also in there as a consultant saying where's the direction of your company and how do we have this great platform and how do we build it into your business and you wrap services around it and those are the conversations that CEOs want to have when I'm sitting down with our partner CEOs I bet they don't want to talk about our product being better than someone else's product they want to talk about the direction and health of their business yeah it's their business that's a business discussion business decision and they're thinking about okay what's my five-year strategic plan because they got to make bets yeah they're going to bet on a platform that they can add value to that creates that flywheel effect and they get a bet on your ecosystem as well correct oh correct absolutely good to be the leader it's good to be a leader and you know I'm sure as you've heard a few times we believe that economic headwinds are going to favor the market leaders and economic headwinds are going to favor the platform approach so we're going in more aggressive with our partner Community than ever before and there's just so much energy and excitement I feel like I keep on using that term over and over again but that's really what we walk away with last question for you is we have about 30 seconds left a lot of momentum in the partner ecosystem as you've described eloquently what's next what's next what's next yeah so when I I rolled out the strategy for what's next and what it is is a foundational platform that is going to allow flexibility for the partners and for them to decide where they want to invest and it can be in new areas it can be I went online closer with the cloud service providers it could be I want to build a managed Services business can you help us do this it could be I want to go through and I want to drive greater penetration into geographical areas we haven't been before so again we're almost acting as a consultant looking at what they're going from the direction and building a program and a platform where we can grow and work with them it's exciting it's fun it's great highly collaborative highly collaborative highly collaborative thank you for joining us on the program on the partner program the ecosystem Better Together what you guys are doing and ultimately how it benefits the end user customer we really appreciate your insights excellent thank you thank you so much appreciate it all right our pleasure for our guests and Dave vellante I'm Lisa Martin you're watching the cube the leader in live Enterprise and emerging Tech coverage [Music]

>> Announcer: TheCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Welcome back to Las Vegas. It's theCUBE covering Palo Alto Networks '22, from the MGM Grand, Lisa Martin with Dave Vellante. Dave, we are going to unpack in the next few minutes what we heard and saw at day one of Palo Alto Networks, Ignite. A lot of great conversations, some great guests on the program today. >> Yeah last event, CUBE event of the year. Probably last major tech event of the year. It's kind of an interesting choice of timing, two weeks after reInvent. But you know, this crowd is it's a lot of like network engineers, SecOps pros. There's not a lot of suits here. I think they were here yesterday, all the partners. >> Yeah. >> We talked to Carl Sunderland about, Hey, these, these guys want to know how do I grow my business? You know, so it was a lot of C level executives talking about their business, and how they partner with Palo Alto to grow. The crowd today is really, you know hardcore security professionals. >> Yeah. >> So we're hearing a story of consolidation. >> Yes. >> No surprise. We've talked about that and reported on it, you know, quite extensively. The one big takeaway, and I want, I came in, as you know, wanting to understand, okay, can you through m and a maintain, you know, build a suite of great, big portfolio and at the same time maintain best of breed? And the answer was consistent. We heard it from Nikesh, we heard it from Nir Zuk. The answer was you can't be best of breed without having that large portfolio, single data lake, you know? Single version of the truth, of there is such a thing. That was interesting, that in security, you have to have that visibility. I would imagine, that's true for a lot of things. Data, see what Snowflake and Databricks are both trying to do, now AWS. So to join, we heard that last week, so that was one of the big takeaways. What were your, some of your thoughts? >> Just impressed with the level of threat intelligence that Unit 42 has done. I mean, we had Wendy Whitmer on, and she was one of the alumni, great guest. The landscape has changed so dramatically. Every business, in any industry, nobody's safe. They have such great intelligence on what's going on with malware, with ransomware, with Smishing, that they're able to get, help organizations on their way to becoming cyber resilient. You know, we've been talking a lot about cyber resiliency lately. I always want to understand, well what does it mean? How do different organizations and customers define it? Can they actually really get there? And Wendy talked about yes, it is a journey, but organizations can achieve cyber resiliency. But they need to partner with Palo Alto Networks to be able to understand the landscape and ensure that they've got security established across their organization, as it's now growingly Multicloud. >> Yeah, she's a blonde-haired Wonder Woman, superhero. I always ask security pros that question. But you know, when you talk to people like Wendy Whitmore, Kevin Mandy is somebody else. And the people at AWS, or the big cloud companies, who are on the inside, looking at the threat intelligence. They have so much data, and they have so much knowledge. They can, they analyze, they could identify the fingerprints of nation states, different, you know, criminal organizations. And the the one thing, I think it was Wendy who said, maybe it was somebody else, I think it was Wendy, that they're they're tearing down and reforming, right? >> Yes. >> After they're discovered. Okay, they pack up and leave. They're like, you know, Oceans 11. >> Yep. >> Okay. And then they recruit them and bring them back in. So that was really fascinating. Nir Zuk, we'd never had him on theCUBE before. He was tremendous founder and and CTO of Palo Alto Networks, very opinionated. You know, very clear thinker, basically saying, look you're SOC is going to be run by AI >> Yeah. >> within the next five years. And machines are going to do things that humans can't do at scale, is really what he was saying. And then they're going to get better at that, and they're going to do other things that you have done well that they haven't done well, and then they're going to do well. And so, this is an interesting discussion about you know, I remember, you know we had an event with MIT. Eric Brynjolfsson and Andy McAfee, they wrote the book "Second Machine Age." And they made the point, machines have always replaced humans. This is the first time ever that machines are replacing humans in cognitive functions. So what does that mean? That means that humans have to rely on, you know, creativity. There's got to be new training, new thinking. So it's not like you're going to be out of a job, you're just going to be doing a different job. >> Right. I thought Nir Zuk did a great job of explaining that. We often hear people that are concerned with machines taking jobs. He did a great job of, and you did a great recap, of articulating the value that both bring, and the opportunities to the humans that the machines actually deliver as well. >> Yeah so, you know, we didn't, we didn't get deep into the products today. Tomorrow we're going to have a little bit more deep dive on products. We did, we had some partners on, AWS came on, talked about their ecosystem. BJ Jenkins so, you know, BJ Jenkins again I mean super senior executive. And if I were Nikesh, he's doing exactly what I would do. Putting him on a plane and saying, go meet with customers, go make rain, right? And that's what he's doing is, he's an individual who really knows how to interact with the C-suite, has driven value, you know, over the years. So they've got that angle goin', they're driving go to market. They've got the technology piece and they've, they got to build out the ecosystem. That I think is the big opportunity for them. You know, if they're going to double as a company, this ecosystem has to quadruple. >> Yeah, yeah. >> In my opinion. And I, we saw the same thing at CrowdStrike. We said the same thing about Service Now in 2013. And so, what's happened is the GSIs, the global system integrators start to get involved. They start to partner with them and then they get to get that flywheel effect. And then there's a supercloud, I think that, you know I think Nir Zuk said, Hey, we are basically building out that, he didn't use the term supercloud. But, we're building out that cross cloud capability. You don't need another stove pipe for the edge. You know, so they got on-prem, they got AWS, Azure, you said you have to, absolutely have to run on Microsoft. 'Cause I don't believe today, right? Today they run on, I heard somebody say they run on AWS and Google. >> Yeah. >> I haven't heard much about Microsoft. >> Right. >> Both AWS and Google are here. Microsoft, the bigger competitor in security, but Nir Zuk was unequivocal. Yes, of course you have to run, you got to run it on an Alibaba cloud. He didn't say that, but if you want to secure the China cloud, you got to run on Alibaba. >> Absolutely. >> And Oracle he said. Didn't mention IBM, but no reason they can't run on IBM's cloud. But unless IBM doesn't want 'em to. >> Well they're very customer focused and customer first. So it'll be interesting to see if customers take them in that direction. >> Well it's a good point, right? If customers say, Hey we want you running in this cloud, they will. And, but he did call out Oracle, which I thought was interesting. And so, Oracle's all about mission critical data, mission critical apps. So, you know, that's a good sign. You know, I mean there's so much opportunity in cyber, but so much confusion. You know, sneak had a raise today. It was a down round, no surprise there. But you know, these companies are going to start getting tight on cash, and you've seen layoffs, right? And so, I dunno who said it, I think it was Carl at the end said in a downturn, the strongest companies come out stronger. And that's generally, generally been the case. That kind of rich get richer. We see that in the last downturn? Yes and no, to a certain extent. It's still all about execution. I mean I think about EMC coming out of the last downturn. They did come out stronger and then they started to rocket, but then look what happened. They couldn't remain independent. They were just using m and a as a technique to hide the warts. You know so, what Nir Zuk said that was most interesting to me is when we acquire, we acquire with the intent of integrating. ServiceNow has a similar philosophy. I think that's why they've been somewhat successful. And Oracle, for sure, has had a similar philosophy. So, and that idea of shifting labor into vendor R and D has always been a winning formula. >> I think we heard that today. Excited for day two tomorrow. We've got some great conversations. We're going to be able to talk with some customers, the chief product officer is on. So we have more great content coming from our last live show over the year. Dave, it's been great co-hosting day one with you. Look forward to doing it tomorrow. >> Yeah, thanks for doing this. >> All right. >> All right. For Dave Vellante, I'm Lisa Martin. You've been watching theCUBE, the leader in live enterprise and emerging tech coverage. See you tomorrow. (gentle music fades)

(upbeat music) >> TheCUBE presents UiPath Forward 5. Brought to you by UiPath. >> Hello and welcome back to TheCUBE's coverage of UiPath Forward 5. We're here in Las Vegas at the Venetian Convention Center. This is day two. We're wrapping up Dave Nicholson and Dave Vellante. This is the fourth time theCUBE has been at UiPath Forward. And we've seen the transformation of the company from, essentially, what was a really interesting and easy to adopt point product to now one through acquisitions, IPO, has made a number of enhancements to its platform. David Cardenas is here. Deputy Director of Operations for County of Los Angeles, the Department of Public Health. David, good to see you. Thanks for coming on theCUBE. >> Thanks for having me on guys. Appreciate it. >> So what is your role? What does it have to do with automation? >> So I had been, actually started off in the IT space within the public health. Had served as a CIO previously, but now been moving into broader operations. And I basically manage all of the back office operations for the department, HR, IT, finance, all that. >> So you've had a wild ride in the last couple of years. >> Yeah, I think, like I've been talking earlier, it's just been, the last two years have just been horrendous. It's been a really difficult experience for us. >> Yeah, and I mean, the scars are there, and maybe permanently. But it also had major effects on organizations, on operations that, again, seem to be permanent. How would you describe the situation in your organization? >> So I think it, the urgency that came along with the pandemic response, kind of required us to look at things, you know, differently. We had to be, realize we had to be a lot more nimble than when we were and try to figure out how to enhance our operations. But really look at the core of what we're doing and figure out how it is to be more efficient. So I think we've kind of seen it as an opportunity to really examine ourselves a little bit more deeply and see what things we need to do to kind of, to fix our operations and get things on a better path. >> You know, I think a lot of organizations we talked to say that. But I want to understand how you handle this is, you didn't have time to sit back in the middle of the pandemic. >> Yeah. >> And then as you exit, what I call the isolation economy, people are so burned out, you know? So how do you deal with that organizational trauma? Say, okay now, let's sit back and think about this. Do people, are they eager to do so? Do they have the appetite for it? What's that dynamic like? >> So I think certainly there's a level of exhaustion inside the organization. I can't say that there isn't because it's just been, you know, two years of 24/7/365 kind of work. And that's tough on any organization. But I think what we realize is that there's, you know, we need to move into action quickly 'cause we don't know what's going to come next, right? And we're expecting that this is just a sign of what's to come and that we're just at the start of that stage of, we're just going to see a lot more outbreaks, we're going to see a lot more conditions kind of hitting us. And if we're not prepared for that, we're not going to be able to respond for the, and preserve the health and safety of our citizens, right? So I think we're taking a very active, like, look at these opportunities and see what we've done and say how do we now make the changes that we made in response to the pandemic permanent so that the next time this comes at us, we won't have to be struggling the way that we were to try to figure things out because we'll have such a better foundation in place to be able to move things forward. >> I mean, I've never served in the military, but I imagine that when you're in the military, you're always prepared for some kind of, you know, in your world, code red, right? >> Yeah. >> So it's like this code red culture. And that seems to have carried through, right? People are, you know, constantly aware that, wow. We got caught off guard and we don't want that to happen again. Because that was a big part of the trauma was just the unknown- >> Right. >> and the lack of preparedness. So thinking about technology and its role in helping you to prepare for that type of uncertainty. Can you describe how you're applying technology to prepare for the next unknown? >> So I think, so that first part of what you said, I think the difficulty we've always had in the public health side is that there's the, generally the approach to healthcare is very reactionary, right? Your first interface with the healthcare system is, "I'm going to go see my doctor; I'm going to go to the hospital." The work that we do in public health is to try to do everything we can to keep you out of that, right? So it's broad-based messaging, social media now is going to put us out there. But also, to be able to surveil disease in a different way. And so the holy grail for us in healthcare has always been, at least on the public health side, has been to try to see how can we tap in more actively that when you go see the doctor or when you go to the hospital, how can I get access to that information very, very quickly so that I know, and can see, and surveil my entire county in my jurisdiction and know, oh, there's an outbreak of disease happening in this section of the county. We're 10 million people with, you know, hundreds of square miles inside of LA. There are places where we can see very, you know, specific targets that we know we have to hit. But the data's a little stale and we find out several months after. We need to figure out a way to do that more actively. Technology's going to be our path to be able to capture that information more actively and come up on something a little bit, so we can track things faster and be able to respond more quickly. So that's our focus for all our technology implementations, automation like UiPath has offered us and other things, is around how to gather that information more quickly and put that into action so we can do quick interventions. >> People have notoriously short memories. Please tell me (chuckles) any of the friction that you may have experienced in years past before the pandemic. That those friction points where people are thinking, "Eh, what are the odds?" >> Yeah. "Eh, I've got finite budget, I think I'm going to spend it on this thing over here." Do you, are you able to still ride sort of the wave of mind share at this point when putting programs together for the future? >> So whatever friction was there during the pandemic wiped away. I mean, we had amazing collaboration with the medical provider community, our hospital partners. The healthcare system in LA was working very closely with us to make sure that we were responding. And there is that wave that we are trying to make sure that we use this as an opportunity to kind of ride it so that we can implement all the things that we want. 'Cause we don't know how long that's going to last us. The last time that I saw anything this large was after the anthrax attacks and the bioterrorism attacks that we had after 9/11. >> How interesting. >> Public health was really in lens at that point. And we had a huge infusion of funding, a lot of support from stakeholders, both politically and within the healthcare system. And we were able to make some large steps in movement at that point. This feels the same but in a larger scale because now it touched every part of the infrastructure. And we saw how society really had to react to what was going on in a different way than anyone has ever prepared for. And so now is we think is a time where we know that people are making more investments. And our success is going to be their success in the longterm. >> And you have to know that expectations are now set- >> Extremely high. >> at a completely different level, right? >> Yes, absolutely. >> There is no, "Oh, we don't have enough PPE." >> Correct. >> Right? >> David: Correct. >> The the expectation level is, hey, you should have learned from all of- >> We should have it; we can deliver it, We'll have it at the ready when we need to provide it. Yes, absolutely. >> Okay, so I sort of mentioned, we're, David cubed on theCUBE (all laughing). So three Daves. You spoke today at the conference? >> Actually I'm speaking later actually in the session in an hour or so. >> Oh Okay. My understanding is that you've got this concept of putting humans at the center of the automation. What does that mean? Why is that important? Help us understand that. >> So I think what we found in the crisis is that the high demand for information was something we hadn't seen before, right? We're one of the largest media markets in the United States. And what we really had trouble with is trying to figure out how to serve the residents, to provide them the information that we needed to provide to them. And so what we had traditionally done is press releases, you know, just general marketing campaigns, billboards, trying to send our message out. And when you're talking about a pandemic where on a daily basis, hour-by-hour people wanted to know what was going on in their local communities. Like, we had to change the way that we focused on. So we started thinking about, what is the information that the residents of our county need? And how can we set up an infrastructure to sustain the feeding of that? Because if we can provide more information, people will make their own personal decisions around their personal risk, their personal safety measures they need to take, and do so more actively. More so than, you know, one of us going on camera to say, "This is what you should do." They can look for themselves and look at the data that's in front of them and be able to make those choices for themselves, right? And so we needed to make sure that everything that we were doing wasn't built around feeding it to our political stakeholders, which are important stakeholders. We needed to make sure that they're aware and are messaging out, and our leadership are aware. But it's what could we give the public to be able to make them have access to information that we were collecting on an every single day basis to be able to make the decisions for their lives. And so the automation was key to that. We were at the beginning of the pandemic just had tons and tons of resources that we were throwing at the problem that was, our systems were slow, we didn't have good ability to move data back and forth between our systems, and we needed a stop-gap solution to really fill that need and be able to make the data cycles to meet the data cycles. We had basically every day had to deliver reports and analytics and dashboards by like 10 o'clock in the morning because we knew that the 12 an hour and the five-hour news cycles were going to hit and the press were going to then take those and message out. And the public started to kind of come in at that same time and look at 10 and 11 o'clock and 12 o'clock. >> Yeah. >> We could see it from how many hits were hitting our website, looking for that information. So when we failed and had a cycle where that data cycle didn't work and we couldn't deliver, the public would let us know, the press would let us know, the stakeholders would let us know. We had never experienced anything like that before, right. Where people had like this voracious appetite for the information. So we needed to have a very bulletproof process to make sure that every single 24 hours we were delivering that data, making it available at the ready. >> Software robots enabled that. >> Exactly. >> Okay. And so how were you able to implement that so quickly within such a traumatic environment? >> So I think, I guess necessity is always the mother of invention. It kind of drove us to go real quickly to look at what we had. We had data entry operations set up where we had dozens and dozens of people whose sole job in life on a 24-hour cycle was to receive medical reports that we we're getting, interview data that's coming from our case interviews, hospitalization data that was coming in through all these different channels. And it was all coming in in various forms. And they were entering that into our systems of record. And that's what we were using, extracts from that system of record, what was using to generate the data analyses in our systems and our dashboards. And so we couldn't rely on those after a while because the data was coming in at such high volume. There wasn't enough data entry staff to be able to fit the need, right? And so we needed to replace those humans and take them out of that data entry cycle, pop in the bots. And so what we started to look at is, let's pick off the, where it is that that data entry cycle starts and see what we could do to kind of replace that cycle. And we started off with a very discreet workload that was focused on some of our case interview data that was being turned into PDFs that somebody was using to enter into our systems. And we said, "Well before you do that," since we can't import into the systems 'cause it wasn't working, the import utilities weren't working. We got 'em into simple Excel spreadsheets, mapped those to the fields in our systems and let the bots do that over and over again. And we just started off with that one-use case and just tuned it and went cycle after cycle. The bots just got better and better to the point where we had almost like 95% success rates on each submission of data transactions that we did every day. >> Okay, and you applied that automation, I don't know, how many bots was it roughly? >> We're now at like 30; we started with about five. >> Okay, oh, interesting. So you started with five and you applied 'em to this specific use case to handle the velocity and volume of data- >> Correct. >> that was coming in. But that's obviously dynamic and it's changed. >> Absolutely. >> I presume it's shifted to other areas now. So how did you take what you learned there and then apply it to other use cases in other parts of the organization? >> So, fortunately for us, the process that was being used to capture the information to generate the dashboards and the analyses for the case interview data, which is what we started with- >> Yeah. >> Was essentially being used the same for the hospitalization data that we were getting and for tracking deaths as they were coming in as well. And so the bots essentially were just, we just took one process, take the same bots, copy them over essentially, and had them follow the very same process. We didn't try to introduce any different workflow than what was being done for the first one so we could replicate quickly. So I think it was lucky for us a lot- >> Dave V.: I was going to say, was that luck or by design? >> It was the same people doing the same analyses, right? So in the end they were thinking about how to be efficient themselves. So they kind of had coalesced around a similar process. And so it was kind of like fortunate, but it was by design in terms of how they- >> Dave V.: It was logical to them. >> Logical to them to make it. >> Interesting. >> So for us to be able to insert the bots became pretty easy on the front end. It's just now as we're trying to now expand to other areas that were now encountering like unique processes that we just can't replicate that quickly. We're having to like now dig into. >> So how are you handling that? First of all, how are you determining which processes? Is it sort of process driven? Is it data driven? How do you determine that? >> So obviously right now the focus still is COVID. So the the priorities scale that we've set internally for analyzing those opportunities really is centered around, you know, which things are really going to help our pandemic response, right? We're expecting another surge that's going to happen probably in the next couple of weeks. That'll probably take us through December. Hopefully, at that point, things start to calm down. But that means high-data volume again; these same process. So we're looking at optimizing the processes that we have, what can we do to make those cycles better, faster, you know, what else can we add? The data teams haven't stopped to try to figure out how else can they turn out new data reports, new data analysis, to give us a different perspective on the new variants and the new different outbreaks and hotspots that are popping up. And so we also have to kind of keep up with where they're going on these data dashboards. So they're adding more data into these reports so we know we have to optimize that. And then there's these kind of tangential work. So for example, COVID brought about, unfortunately, a lot of domestic violence reports. And so we have a lot of domestic violence agencies that we work with and that we have interactions with and to monitor their work, we have certain processes. So that's kind of like COVID-adjacent. But it's because it's such a very critical task, we're looking at how we can kind of help in those processes and areas. Same thing in like in our substance abuse area. We have substance use disorder treatment services that we provide. And we're delivering those at a higher rate because COVID kind of created more of a crisis than we would've liked. And so that's how we're prioritizing. It's really about what is the social need, what does the community need, and how can we put the technology work in those areas? >> So how do you envision the future of automation in your organization and the future of your organization? What does that look like? Paint a picture for us. >> So I'm hoping that it really does, you know, so we're going to take everything that's COVID related in the disease control areas, both in terms of our laboratory operations, in terms of our clinic operations, the way we respond, vaccination campaigns, things of that nature. And we're going to look at it to see what can efficiencies can we do there because it's a natural outgrowth of everything we've done on COVID up to this point. So, you know, it's almost like it's as simple as you're just replicating it with another disease. The disease might have different characteristics, but the work process that we follow is very similar. It's not like we're going to change everything and do something completely different for a respiratory condition as we would for some other type of foodborne condition or something else that might happen. So we certainly see very easy opportunities to just to grow out what we've already done in terms of the processes is to do that. So that's wave one, is really focus on that grow out. The second piece I think is to look at these kind of other general kind of community-based type of operations and see what operations we can do there to kind of implement some improvements there. And then I'm certainly in my new role of, in Deputy Director of Operation, I'm a CIO before. Now that I'm in this operations role, I have access to the full administrative apparatus for the department. And believe me, there's enough to keep me busy there. (Dave V. Laughing) And so that's going to be kind of my third prong is to kind of look at the implement there. >> Awesome. Go ahead, Dave. >> Yeah, so, this is going to be taking a step back, kind of a higher level view. If we could direct the same level of rigor and attention towards some other thing that we've directed towards COVID, if you could snap your fingers and make that happen, what would that thing be in the arena of public health in LA County in particular, or if you want California, United States. What is something that you feel maybe needs more attention that it's getting right now? >> So I think I touched on it a little bit earlier, but I think it's the thing we've been always been trying to get to is how to really become just very intentional about how we share data more actively, right? I don't have to know everything about you, but there are certain things I care about when you go to the doctor for that doctor and that physician to tell me. Our physicians, our healthcare system as you know, is always under a lot of pressure. Doctors don't have the time to sit down and write a form out for me and tell me everything that's going on. During COVID they did because they were, they cared about their patients so much and knew, I need to know what's going on at every single moment. And if I don't tell you what's going on in my office, you'll never know and can't tell us what's going on in the community. So they had a vested interest in telling us. But on a normal day-to-day, they don't have the time for that. I got to replace that. We got to make sure that when we get to, not me only, but everyone in this public health community has to be focused and working with our healthcare partners to automate the dissemination and the distribution of information so that I have the information at my fingers, that I can then tell you, "Here's what's going on in your local community," down to your neighborhood, down to your zip code, your census tracked, down to your neighbors' homes. We'll be able to tell you, "This is your risk. Here are the things that are going on. This is what you have to watch out for." And the more that we can be more that focused and laser-focused on meeting that goal, we will be able to do our job more effectively. >> And you can do that while preserving people's privacy. >> Privacy, absolutely. >> Yeah, absolutely. But if people are informed then they can make their own decisions. >> Correct. >> And they're not frustrated at the systems. David, we got to wrap. >> Sure. >> But maybe you can help us. What's your impression of the, first of all, is this your first Forward? You've been to others? >> This is my first time. >> Okay. >> My first time. >> What's your sort of takeaway when you go back to the office or home and people say, "Hey, how was the show? What, what'd you learn?" What are you going to say? >> Well, from just seeing all the partners here and kind of seeing all the different events I've been able to go to and the sessions there's, you don't know many times I've gone to and say, "We've got to be doing that." And so there's certainly these opportunities for, you know, more AI, more automation opportunities that we have not, we just haven't even touched on really. I think that we really need to do that. I have to be able to, as a public institution at some point our budgets get capped. We only have so much that we're going to receive. Even riding this wave, there's only so much we're going to be able to get. So we have to be very efficient and use our resources more. There's a lot more that we can do with AI, a lot more with the tools that we saw, some of the work product that are coming out at this conference that we think we can directly apply to kind of take the humans out of that, their traditional roles, get them doing higher level work so I can get the most out of them and have this other more mundane type of work, just have the systems just do it. I don't need anybody doing that necessarily, that work. I need to be able to leverage them for other higher level capabilities. >> Well thank you for that. Thanks for coming on theCUBE and really appreciate. Dave- >> It's been great talking to you guys, thank you. >> Dave, you know, I love software shows because the business impact is so enormous and I especially love cool software shows. You know, this first of all, the venue. 3,500 people here. Very cool venue. I like the fact that it's not like booth in your face, booth competition. I mean I love VMware, VMworld, VMware Explore. But it's like, "My booth is bigger than your booth." This is really nice and clean, and it's all about the experience. >> A lot of steak, not as much sizzle. >> Yeah, definitely. >> A lot of steak. >> And the customer content at the UiPath events is always outstanding. But we are entering a new era for UiPath, and we're talking. We heard a lot about the Enterprise platform. You know, the big thing is this company's been in this quarterly shock-lock since last April when it went public. And it hasn't all been pretty. And so new co-CEO comes in, they've got, you know, resetting priorities around financials, go to market, they've got to have profitable growth. So watching that that closely. But also product innovation so the co-CEOs will be able to split that up, split their duties up. Daniel Dines the product visionary, product guru. Rob Enslin, you know- making the operations work. >> Operations execution business, yeah. >> We heard that Carl Eschenbach did the introduction. Carl's a major operator, wanted that DNA into the company. 'Cause they got to keep product innovation. And I want to, I want to see R&D spending, stay relatively high. >> Product innovation, but under the heading of platform. And that's the key thing is just not being that tool set. The positioning has been, I think, accurate that, you know, over history, we started with these RPA tools and now we've moved into business process automation and now we're moving into new frontiers where, where truly, AI and ML are being leveraged. I love the re-infer story about going in and using natural national (chuckles) national, natural language processing. I can't even say it, to go through messaging. That's sort of a next-level of intelligence to be able to automate things that couldn't be automated before. So that whole platform story is key. And they seem to have made a pretty good case for their journey into platform as far as I'm concerned. >> Well, yeah, to me again. So it's always about the customers, want to come to an event like this, you listen to what they say in the keynotes and then you listen to what the customers say. And there's a very strong alignment in the UiPath community between, you know, the marketing and the actual implementation. You know, marketing's always going to be ahead. But, we saw this a couple of years ago with platform. And now we're seeing it, you know, throughout the customer base, 10,000+ customers. I think this company could have, you know, easily double, tripled, maybe even 10x that. All right, we got to wrap. Dave Nicholson, thank you. Two weeks in a row. Good job. And let's see. Check out siliconangle.com for all the news. Check out thecube.net; wikibon.com has the research. We'll be on the road as usual. theCUBE, you can follow us. UiPath Forward 5, Dave Vellante for Dave Nicholson. We're out and we'll see you next time. Thanks for watching. (gentle music)

>> Male: TheCUBE presents, UIPATH, Forward 5 brought to you by, UIPATH. >> Okay the party has started here at forward 5 UIPATH big customer event if you're watching the cube. We're wrapping up day one with the co-CE0 segment. Daniel Dines is here. He's the founder and Co-CEO of UIPATH and Rob Enslin, is co-CEO. Gents, great to see you. Thanks for spending some time with us. I know you're super busy. >> Thanks Dave. >> So I've been looking forward to this. Daniel you know I've followed the company for a long time. The really interesting path you took, to get to where you are today. How did you guys meet? And why did you decide to hire Rob? >> Male: (laughs) >> Rob: Well let me start. I uh, I was looking for a partner. Actually, in our work to your stand here, we are talking about how, how you feel in this job. You feel so alone. Because you are the center of all pressure points. And having a partner, having someone that has your back, it's kind of awesome. So I was looking for a partner. And our current friend, Carl Escenbach, he introduced us to each other, and we instantly clicked. And this is the type of job where it's uh either work well or it doesn't. It cannot be anything in the middle. >> Right, okay with Carl, we know Carl well. Awesome operator. Knows the business super well. So Rob, what attracted you to UIPATH? You had a great situation at google. You guys were growing like crazy. Why did you decide to come here? What did you see that attracted you? >> Yeah you know when I, when I went to google, I went to google because I really believed that data and AI was necessary for companies. And business is to be competitive in the future. And we did some great stuff at google cloud in the 3 years. But I knew UIPATH from a couple of years ago when they were mainly a RPA space. And I just felt that there was a place in time when automation was going expand. And as I sat down with Carl a couple of times, spoke to carl. And then I sat down with Daniel, I knew that there was something special with UIPATH, that could be a generational opportunity. Not any for myself but for the company in the future. And then I, you know I got to know Daniel. And at this stage of my career I was like, I'm pretty fussy about what I want to do and what I want and where I want to go. First of all, I want to go to a company that had great product, had a great culture, and I wanted to work with somebody that we could shake the future together and you know, Daniel and I just hit it off from the very first time we met. He got to meet my family, my dogs and we did the whole, we did the whole courting thing before we actually decided this was going to be a good thing for both of us. >> Dave: That's good. >> Rob: Yeah. >> Dave: You got to meet the family. That's very good. >> We just had, John Furrier and I just had, Mohit Aron and Sanjay Poonen into out studio. Cause Mohit, you know, formal google. Long time. And they decided to kind of split duties. Mohit's going into product, he didn't keep his CEO title. He walked. How are you guys splitting you time? What are each of you going to, responsible for? >> Daniel: Well its, its kind of similar. On a day by day operation I, I rely heavily on Rob. We do it together. Strategic decisions about the company's destiny. I'm doing mostly the product these days. Which is a big relief for me. And I think we also split a bit of customers visit. Which is great. I still enjoy meeting customers. I need, customers are food for my cause. >> Dave: (laughs) yeah and your awesome product visionary. You've been there since day one. Now Rob, you said in the key note today that you've seen around about a hundred customers. You've transverse the world. What did you learn from them that informed you? That gave you confidence that the the move to the internet platform, even though you had already started that. >> Male: Yeah. >> But you're really doubling down on that >> Rob: You know when I... >> from a stand point. >> Rob: You know Dave, when you think about it, like I was, I was so impressed that Daniel had the vision to create a platform 3 years ago. >> Dave: Yeah. >> All right. And as we went around the world. As I went around the world, and it was one of the very first things I've seen. I've got to understand how customers see UIPATH, from their advantage point. What are they looking for from us? Why is this company, why doe customers like this company so much? And as I went around the world. I went to Asia a couple, I went to Asia, Australia, Singapore, Japan. I was in Europe twice. We did the trip together. We went to visit customers. And it was very much the same thing. Helps us expand automation faster. And we are so surprise, at the break of your platform. We never knew that. And so it kind of just had, for me, it was conviction. It's like, this walls is the right decision you've made. There's so much opportunity there. And that's, you know that's kind of what I've learned through the last four five months. >> Dave: Now as you know Daniel, I've written a lot about your company. One of the things I've said is that, that start ups, if I can call you that back pre-IPO, typically don't have as much international exposure as UIPATH had. I mean you sort of, you sort of started as an international company and became more US centric. You said, in the, in the key note today, you're talking to Ray Wong about people may don't understand that challenges of FX. Point being, when you convert international dollars into US dollars there are less of them cause the dollars stronger. But still, I've always felt like that international footprint is an advantage. Rob you came from SAP, you know, again European based company. I don't, (stutters), do you regret that? Now? I mean I know it's technical, I'm sure you don't, but talk about that sort of international exposure? Why that's a long term benefit. >> Well, you, first of all, you expand faster. I think we expanded faster than our competition because our global footprint was larger. And we had the courage. Go in Japan, for instance. Everybody told me, it's impossible to make for such a small starter. It's impossible to make a business in Japan. But we didn't believe it. We're just crazy and we went there, and be built a very sizable business in Japan. Fifty-five percent of our revenue, even today, it's outside U.S. Now of course that has a down side. When uh, When the local currencies, you know, are losing the value compared to the dollars, we're impacted. As we go to... to investors, until now, so we are seeing like a (indistinct) in terms of ARI. It's huge. Only because (indistinct) and losing the business in Russia. But it still, it's the strength of our company. Things will come back. And then, you know, the growth engine will re-accelerate again. >> Dave: Yeah but when the dollars weakens that'll be in your favor. Rob I want to pick up on something you said today in your keynote. You went back and started, you know the cycles of ERP and you know, internet, et cetera. I kind of have a love hate with ERP. I have to be honest. >> Male: (laughing) >> But it, but but (chuckles) but if I go back to that. Late eighties nineties, you wouldn't have be able to pick SAP as the winner. And then SAP emerged. You know, very clearly. But the more interesting thing, is that the customers who are implementing ERP well. The practitioners did better than their peers, and dominated their industries. And their stocks went up. Their evaluations went up. Different worlds obviously but, do you see the same thing happening with RPA and automation? What gives you confidence that that's the case? >> I absolutely do see the same thing happening with automation and RPA being a part of, in being a part of that. The reason, the reason I believe that is speed is so critical. (stutters) And if you think about how hard it is for a CIO or a c level executive to consume the technology coming at them, plus all the changes in the world being thrown at them. It's compiling and compiling and compiling. We have an incredible solution, that can help companies. And there comes certain times, the love outcomes to the business. Like no one else gets. And when I see that, I view that as just like the beginning of what's going to happen in the future so, in many ways, and I've said this to many of my friends, it feels like 1992, 1993 to me. And it's interesting because no one really understood then why SAP would be great in 1992 and 93. And they got a couple of things right. They got the eco system right. Their new partners were important. And the knew they needed to drive business outcome for companies, in which they did. And so I feel like we are in a very similar place. Very different technology obviously. And the speed of change now is so dramatic, compared to what it was. And there's very few technology that can provide that level of speed and accomodation to their customers. >> All right, let's talk about priorities. You guys got a lot of work to do and you've, you've laid it out to the financial community. You've got to have profitable growth, because of FX, it part, you've lowered your forecast. But I think there's some conservative in their as well. Um, but you got to do that balance. You've given some guidance on gross margins. Cloud maybe brings that down a little bit. RnD I saw wide range. Thirteen to seventeen percent. I hope you keep spending on RnD. Big fan of that. You know stock buybacks and, RnD if in your position are going to be better. And the product priorities, continue to build that out. But question, let's start with the product. So you've got an on-prem stack and you've got a cloud stack that's emerging, how do you balance those out? How do you do the integration? You've done a great job with the integration. Does it, are you concerned about your ability to continue to work at that speed with two code bases? I wonder if you could address that? >> Daniel: We've become a cloud first company. We deliver all of our products first in the cloud. We've deliver on the two week (indistinct) in the cloud. So that helps us integrate quite fast. I think we made a very good business decision to build our cloud team in Seattle. In Bellevue to be specific. And we have access to great talent that knows how to build serious cloud service. Which is hard to find dollar. And uh, so, and also we, we have, we benef- one of our only benefits was, we have the really good architecture. We have an architecture that work easily on-prem and on the cloud. And even today, our work flow foundation, our local designers, were easy to modernize. So right now we are launching studio weapon. But behind the scene, it's the same workflow engine. Our customers don't have to rewrite anything. It just works. And it does the same to take our own brand product and brand it in the multicloud. So, it's, there is no friction at all. Actually cloud is just helping us accelerate. But we benefit then again of a really solid architectural foundation. >> Daniel: Architecture matters. We've seen that in this industry. We got the B52s rocking out in the background, I love it, but I've got so many questions for you guys. I want to talk about the go to market. Because Rob, it's obviously a strength of yours. You've come in. You've communicated to the street, that you're reshaping the sales floors. Are they lowering the ratios of sales? People, the customers at the high end, mid range as well, using digital. I mean the numbers are one to ten now. At the top. One to maybe fifty at the mid range. Where are you in terms of that journey? You've got to find people, you got to train them, how do you get the productivity out of those guys? Take us through your thinking there? >> Rob: Yeah firstly, I think we have enough resources. Having resources is not an issue. Um, we have an incredible vehicle to acquire customers inside the company. Our digital sales motion, it's probably the best I've seen. And so we have the ability to acquire customers really fast. And we get the first workload in really fast. The challenge is we need to, we need to be able to drive a (indistinct) model and we graduate customs when we acquire them into the direct sales floors. And then direct sales floors, we're not going to go one to thirty, we're talking one to ten for the direct sales floor. And even the high up in the pyramid, we want to have an even denser model than that. And the whole purpose is to drive the time to consumption much quicker, much faster. So we know exactly if we acquire a customer, will they spend? Do they have a (indistinct) spend? On what level do they have a (indistinct) spend? And therefore when we capture them, we can immediately surround them, and put the right resources so we can grow faster. We think this will have a significant impact on the organization. We'll start to implement certain pieces in the next quarter. Um, things like packaging solutions. Putting them in, enabling the sales organization. And buy the beginning of next year, we'll be ready to actually go full board, globally. We already put some pieces in place when I joined. Chris Weber, my chief business officer, did a great job doing some of those pieces. So we're on the journey already. >> Dave: Yeah and even before you guys were public and you weren't publishing your NRR numbers. Our ETR survey partner, we, we always thought you had very low churn. And I think you broke out just yesterday. The, the NRR for overseas vs U.S, U.S I think was 140 plus percent. >> Male: Yeah >> Very very strong. A little, a little less overseas but the churn is still very low. >> Male: Yep. >> Okay so that's super positive. Customer affinity, I was wanted to code these events. I listen to the key notes very carefully, and then interview customers on the cube, and I try to identify, is there alignment there? And I see very strong alignment, I have to say, and strong customer affinity. So that's in your favor. I have, Daniel, I got another question for you on product. What is Symantec automation? What the heck is that? Can you explain that? I don't understand >> Dave, have you seen the demo in my (indistinct)? >> Dave: You know, I had to leave and do interviews, so I, uh, I missed it. >> I think, I think that demo answer complete your question. So in the s-, you know there saying that great, you can not distinguish great technology by magic. I think technology should be simple. And we, we show today, one of the simplest demo that you can imagine. But it's so, such a complex technology behind the scene, that you also can not imagine. So what was demo? We show how one business user, without any technical skills, can build any type of document. Can be a passport, can be an invoice, can be a legal (indistinct), and just go, "I want to copy data from here, and I want to paste data there". Can be a spreadsheet, can be another obligation, and like a human user, without understanding, without having prior knowledge about data, document layout, about screens, screens layouts, nothing, we analyze real time. Document. We discover, we discover the meaning of the information. We analyze the screen. We understand the screen but we understand the meaning of the screen. And we understand how the information in one side relate to the other side. And we just connects the dots and we copy the information and we paste it. A job that you'll do as a human user, maybe three minutes, is done in ten seconds. This is powerful. >> Yeah that is powerful. Thank you for that. I mean, and you take the date, whether it's transaction data or unstructured data and and and bring meaning out of it. That's powerful. Last question and I'll let you guys go. Rob, you got traders, and you've got long term investors. All right traders going to be defensive, today. I get that. Make the case for UIPATH, for long term investors. >> Rob: I think we're going to be a multi-gern- multi-billion company and we're going to be a generational company of our time. And we will define enterprise automation. And it's going to be a long term game and we feel like really strong that we'll be the lead in that game. >> Dave: Guys, thanks so much for coming to the cube. Great show. Always fun at UiPath Forward. Really appreciate your time. Thank you. >> Thanks dave. >> Appreciate it as well. >> Okay wrap it up, day one, we're here tomorrow, first thing, Dave Vellante and Dave Nicholson. Thanks for watching, forward 5, Uipath big customer event, we'll see you tomorrow. (music)

>>Hello, everyone. Welcome to the cubes presentation of the a startup showcase. This is our season two episode four of the ongoing series covering exciting hot startups from the a AWS ecosystem. And here we talk about cybersecurity. I'm John furrier, your host we're joined by Carl Mattson, CISO, chief information security officer of no name security, keep alumni. We just chatted with you at reinforce a business event. We're here to talk about securing APIs from code to production. Carl, thanks for joining. >>Good to see you again. Thanks for the invitation, John. >>You know, one of the hottest topics right now about APIs is, you know, it's a double edged sword, you know, on one hand, it's the goodness of cloud APIs make the cloud. That's the API first. Now you're starting to see them all over the place. Is APIs everywhere, securing them and manage them. It's really a top conversation at many levels. One, you're gonna have a great API, but if you're gonna manipulate the business logic, that's a problem too. So a lot going on with APIs, they're the underpinnings of the modern enterprise. So take us through your view here. How are you guys looking at this? You want to continue to use APIs, they're critical connective tissue in the cloud, but you also gotta have good plumbing. Where, what do you do? How do you secure that? How do you manage it? How do you lock it down? >>Yeah, so the, the more critical APIs become the more important it becomes to look at the, the API as really a, a, a unique class of assets, because the, the security controls we employ from configuration management and asset management, application security, both testing and, and protection like, like EDR, the, the, the platforms that we use to control our environments. They're, they're, they're poorly suited for APIs. And so >>As the API takes prominence in the organization, it goes from this sort of edge case of, of, of a utility now to like a real, a real crown jewel asset. And we have to have, you know, controls and, and technologies in place and, and, and skilled teams that can really focus in on those controls that are, that are unique to the API, especially necessary when the API is carrying like business critical workloads or sensitive data for customers. So we really have to, to sharpen our tools, so to speak, to, to focus on the API as the centerpiece of a, of an application security program, >>You know, you guys have a comprehensive view. I know the philosophy of the company is rooted in, in, in API life cycle development management runtime. Can you take a minute to explain and give an overview of no name security? And then I wanna jump into specifically the security platform and the capabilities. >>Sure. So we're an API security company just under three years old now. And, and we we've taken a new look at the API, looking at it from a, from a, a full lifecycle perspective. So it, it, isn't new to application security professionals that APIs are, are a software asset that needs to be tested for security, vulnerabilities, security testing prior to moving into production. But the reality is, is the API security exposures that are hitting the news almost every day. A lot of those things have to do with things like runtime errors and misconfigurations or changes made on the fly, cuz APIs are, are changed very rapidly. So in order for us to counter API risks, we have to look at the, the full life cycle from, from the moment the developer begins, coding the source code level through the testing gates, through the, the operational configuration. And then to that really sophisticated piece of looking at the business logic. And, and as you mentioned, the, the business logic of the API is, is unique and can be compromised with, with exploits that, that are specific to an API. So looking at the whole continuum of API controls, that's what we focused on. >>It's interesting, you know, we've had APIs for a while. I mean, I've never heard and seen so much activity now more than ever around APIs and security. Why is it recently we're seeing this conversation increase with specific solutions and why are we seeing more breaches and concerns about security? Because APIs are hardened. I mean, like, what's the big deal. Why now what's the big focus? Why is APIs becoming more in the conversation for CSOs and companies to secure? And why is it a problem? >>Well, take, take APIs that we had, you know, eight, 10 years ago, most of those were, were internally facing APIs. And so there were a lot of elements of the API design that we would not have put in place if we had intended that to be public facing authentication and authorization. That that was, is we kind of get away with a little bit of sloppy hygiene when it's internal to the network. But now that we're exposing those APIs and we're publishing APIs to the world, there's a degree of precision required. So when we, when we put an API out there for public consumption, the stakes are just much higher. The level of precision we need the business criticality, just the operational viability and the integrity of that API has to be precise in a way that really wasn't necessary when the API was sort of a general purpose internal network utility as it was in the past. And then the other, other area of course, is then just the sheer use of a API at the infrastructure layer. So you think about AWS, for example, most of the workloads in the modern cloud, they communicate and talk via API. And so those are even if they're internally facing APIs misconfigurations can occur and they could be public facing, or they could be compromised. And so we wanna look at all, all of the sort of facets of APIs, because now there's so much at stake with getting API security, right. >>You know, this brings up the whole conversation around API to API, and you guys talk about life cycle, right? The full life cycle of an API. Can you take me through that and what you mean by that? Because, you know, some people will say, Hey, APIs are pretty straightforward. You got source code, you can secure it. Code scanning, do a pen test. We're done why the full cycle approach is it because APIs are talking to third parties? Is it because what I mean, what's the reason what, what's the focus, why full life cycle of an API? Why should a company take this approach? >>Sure. So there's, there's really three sort of primary control areas that we look at for, for APIs as like what I call the traditional controls. There would be those to, to test and ensure that the source code itself has as quality or is, is secure. And that can, that can, of course, usually a step one. And that's, that's an important thing to, to do, but let's say let's for the sake of discussion that API that is designed securely is deployed into production, but the production environment in which it's deployed, doesn't protect that API the way that the developer intended. So a great example would be if an API gateway doesn't enforce the authentication policy intended by the developer. And so there we have, there's not the developer's fault. Now we have a misconfiguration in production. And so that's a, that's a type of example also where now a, an attacker can send a sort of a single request to that API without authentication or with, you know, misformed authentication types and, and succeed resulting in data. >>The waft didn't protect against it. It was secure code. And so when we look at the sequence of API controls, they all really have to be in sync because source code is really the first and most important job, but good, good API design and source code doesn't solve all challenges for their production environment. We have to look at the whole life cycle in order to counter the risk IBM's research last year in its X worth survey, estimated that 60% of all API breaches are due to misconfiguration, not to source code design. And so that's really where we have to marry the two of the runtime protection configuration management with the, the, the source code testing and design. >>It's, it's interesting, you know, we've all been around the block, we've seen the early days and you know, it was really great back in the day you sling an API, Hey, you know, Carl, you have an API for that. Oh, sure. I'll bang it out tonight. You know? So, so the, you know, they've gotten better, I'm over simplifying, but you get the idea they've been kind of really cool to work with and connect with systems. It's now plumbing. Okay. So organizations have, are dealing with this, they're dealing with APIs and more of them, how do they know where they stand? Is there like a API discovery capability? What do they do? What does a CSO do? What does a staff do saying, okay, you know what? We don't wanna stop the API movement cuz that's key to the cloud. How do we reign it in? How do we reign in the chaos? What do they do? Is there playbook? What does, how does an organization know exactly where it stands with the state of their APIs? >>Yeah. That, and that's usually where we started a discussion with a, with a customer is, is, is a diagnosis, right? Because when we, when we look at sort of diagnosing what our API risk exposure, the, you know, the, the first critical control is always know your assets and, and that we, we have to discover them. So we, we, we employ usually discovery as the very first step to see the full ecosystem of APIs, whether they're internal, external facing, whether they're routed through a gateway or whether they're routed through a WF, we have to see the full picture and then analyze that API footprint in terms of its network context, it's vulnerabilities, it's configuration qualities so that we can see a picture of where we are now in, in any particular organization, we may find that there's a, a, a, a high quality of source code. >>Perhaps the gaps are in configuration, or we may see the reverse. And so we, we don't necessarily make an assumption about what we'll find, but we know that that observability is really the, the first step in that, in that process is just to really get a firm sort of objective understanding of, of where the APIs are. And, and the really important part about the, the observability to the API inventory is to do it with the context also of the sense of the data types. Because, you know, for example, we see organizations, our own research showed that for organizations over 10,000 employees, the average population of APIs is over 25,000 in each organization, 25,000 AP thousand APIs is an extraordinary amount to, to even contemplate a human understanding of. So we have to fingerprint our APIs. We have to look at the sensitive data types so that we can apply our intellect and our resources towards protecting those APIs, which have, which are carrying sensitive data, or which are carrying critical workloads, because there are a lot of APIs that still remain today, even sort of internally facing utilities, work courses that keep the lights on, but not particularly high risk when it comes to sensitive data. >>So that, that, that triage process of like really honing in on the, on the high risk activity or the high risk APIs that they're carrying sensitive data, and then then sort of risk exposure assessing them and to see where an organization is. That's always the first step, >>You know, it's interesting. I like your approach of having this security platform that gives the security teams, the ability to kinda let the developers do their thing and, and then have this kind of security ops kind of platform to watch and monitor and any potential attacks. So I can see the picture there. I have to ask you though, as a CSO, I mean, what's different now, because back in the old days where API's even on the radar and two, there's a big discussion around software supply chain. This kind of this API is now a new area. As you'd been referring to people, stealing data, things are in transit with APIs. What is the, the big picture, if you had to kind of scope out the magnitude of like the API problem and, and relevance for a fellow CSO, how, how would you have that conversation? You'd be like, Hey, APIs are outta control. You gotta reign it in. Or is it a 10 and a 10? Is it a eight? I mean, yep. Take me through a conversation you're having with security teams or other CSOs around the magnitude of the scoped scoping the problem. >>Yeah. So I, I think of the, the, the API sort of problem space has a lot of echoes to the, to the conversations and the thought processes we were having about public cloud adoption a few years ago. Right. But there was, there were early adopters of public cloud and, and over the course of time, there was sort of a, an acquiescence to public cloud services. And now we have like actually like robust enterprise grade controls available in public cloud. And now we're all racing to get there. If we, if we have anything in the data center left, we're, we're trying to get to the public cloud as fast as possible. And so I think organization by organization, you'll, you'll see a, a, a reminiscent sort of trajectory of, of API utilization, because like an application we're out of gone are the days of the monolithic application, where it's a single, you know, a single website with one code base. >>And I kind of compare that to the data center, this comparison, which is the monolithic application is now sort of being decomposed into microservices and APIs. There are different differences in terms of how far along that decomposition into microservices and organization is. But we definitely see that the, that that trend continues and that applications in the, you know, three to five to 10 year timeframe, they increasingly become only APIs. So that an organization's app development team is almost exclusively creating APIs as, as the, as the output of software development. Whereas there's a, there's a journey to, towards that path that we see. And so, so a security team looking at this problem set, what I, you know, advise for, for a CISO. The looking at this maybe for the first time is to think about this as this is the competency that we, our security teams need to have. That competency may, may be at different degrees of criticality, depending on where that company is in transition. But it's not a, it's not a question of if it's a question of when and how fast do we need to develop this competency in a team because our applications will become almost exclusively APIs over time, just like our infrastructures are on the way to becoming almost exclusively public cloud hosted over time. >>Yeah. I mean, get on the API bus basically is the message like, look it, if you're not on this, you're gonna have a lot of problems. So in a way there's a proactive nature here for security teams at the same time, it's still out there and growing, I mean, the DevOps movement was essentially kind of cavalier, very Maverick oriented, sling APIs around no problem, Linga Franco connecting to other systems and API to an endpoint to another application. That's what it was. And so as it matures, it becomes much more of a, as you say, connective tissue in the cloud native world, this is real. You agree with that obviously? >>Yeah, absolutely. I mean, I think that the, I think that these, these API connections are, are, are the connective tissue of most of what we do right now. Even if we are, are not, you know, presently conscious of it, but they're, they're increasingly gonna become more and more central. So that's, that's, that's a, that's a journey whether, whether the, the focus on API security is to let's say, put the toothpaste back in the tube for something that's already broken, or whether it is preventative or prep preparing for where the organization goes in the future. But both of those, both of those are true. Or both of those are valid reasons to emphasize the investment in API security as a, as a talent processes, technologies all the above. >>Okay. You sold me on I'm the customer for a minute. Okay. And now I'm gonna replay back to you. Hey, Carl, love it. You sold me on this. I'm gonna get out front we're we're in lift and shift mode, but we can see APIs as we start building out our cloud native. And, but I'm really trying to hire a team. I got a skills gap here too. Yep. That's one customer. Yep. The other customers, Hey man, we've been on this train for a while. Kyle. We, we, we feel you, we in DevOps pioneer, we're now scaling out. We got all kinds of sprawl, API sprawl. How do I reign it in? And what do you guys do? What's your answer to those scenarios from a security platform perspective and how does that, what's the value proposition in those scenarios? >>I think the value proposition of what we've done is really to, to lean into the API as the, as the answer key to the problem set. So, you know, whether it's integrating security testing into a code repo, or a C I C D pipeline, we can automate security testing and we can do that very efficiently in, in such a way that one applic when a one API security specialist with the right tools, it ins insulates the organization from having to go out and hire 10 more people, because they've all, all of a sudden have this explosive growth and development. There's so much about API security that can capitalize on automation and capitalize on API integrations. So the API integrations with web application firewalls, with SIM systems, those types of workflows that we can automate really do empower a team to, to use automation to scale and to approach the problem set without needing to go to the, the, sort of the impossible ask of growing these growing teams of people with special skills and, and who aren't available anyways, or they're extremely expensive. So we definitely see ourselves as, as a, as a sort of leaning into the API as, as part of the answer and creating opportunities for automation. >>Yeah. So I got one more kind of customer role play here. I says, I love this. This is a great conversation. You know, there's always the, the person in the room, Carl, hold on, boss. This is gonna complicate everything on the network layer, application changes. There's a lot of risks here. I'm nervous. What's your, how do you guys handle that objection that comes up all the time. You know, the, the person that's always blocking deals like, oh, it's risky implementing no name or this approach. How do you, how do you address the frictionless nature of developers? Wanna try stuff now they wanna get it in and they wanna try things. How do you answer the quote, complication or risk to network and application changes? >>Sure. Two, two really specific answers. The, the first is, is for the developers. We wanna put a API security in their hands because when they can, when they can test and model the security risks on their APIs, while they're developing, like in their IDE and in their code repos, they can iterate through security fixes and bugs like lightning fast. And they, and developers Le really appreciate that. They appreciate having the instant feedback loop within their workspace, within their workbench. So developers love being able to self-service security. And we want to empower developers to, to do that. Self-service rather than tossing code over the fence and waiting two weeks for the security team to test it, then tossing it back with a list of bugs and defects that annoys everybody. It's an inefficient. So >>For the record, just for the record, you guys are self-service to the developers. >>Yeah. Self-service to the developers. And that's really by customer sort of configuration choices. There are configuration choices that have, for example, the security team, establishing policy, establishing boundaries for testing activities that allow the developers to test source code iterate through, you know, defect, fixes, things like that. And then perhaps you establish like a firm control gate that says that, you know, vulnerabilities of, of medium and above are a, have to be remediated prior to that code committing to the next gate. That's the type of control that the security policy owner can can apply, but yes, the developers can self-service service and the, and the security team can set the threshold by which the, the, the, the source code moves through the SDLC. Everybody will. Yep. Exactly. And, and, but we're, we have to, we have to practice that too, because that's a, that's a new way of, of, of the security team and the developers interacting. >>So we, we, we, we have to have patterns that that teams can then adopt procedurally because we aren't, we aren't yet accustomed to having a lot of procedures that work that way. So yeah, we, we have templates, we've got professional services that we want to help those teams get that, that equation, right? Because it it's a, it's a truly win-win situation when you can really stick the landing on getting the developers, the self-service options with the security team, having the confidence level that the controls are employed. And then on, on the network side, by the way, I, I too am mortified of breaking infrastructure and, and which is exactly why, you know, what, what we do architecturally out of band is, is really a, a game changer because there are technologies we can put in, in line, there are disruptors and operational risks that we can incur when we are, where we utilizing a technology that, that can break things, can break business, critical traffic. >>So what we do is we lean into the, the, the sort of the network nodes and the, and the hosts that the organization already has identifying those APIs, creating the behavioral models that really identify misuse in progress, and then automate, blocking, but doing that out of, out of band, that's really important. That's how I feel about our infrastructure. I, I don't want sort of unintended disruption. I want, I want to utilize a platform that's out of band that I can use. That's much more lightweight than, you know, putting another box in, in the network line. Yeah, >>What's interesting is what you're talking about is kind of the new school of thought. And the script has flipped. The old school was solve complexity with more complexity, get in the way, inject some measurements, software agents on the network, get in the way and the developer, Hey, here's a new tool. We agreed in a, in a vacuum, go do this. I think now more than ever, developers are setting the agenda on, on, on the tooling, if it's, and it has to be self-service at our super cloud event that was validated across the board. That if it's self-service, it's gotta be self-service for the developer. Otherwise they won't use it pretty much. >>Oh, well, I couldn't agree more. And the other part too, is like, no matter what business we're in the security business is, is yeah, it has to honor like the, the, the business need for innovation. We have to honor the business need for, for, for speed. And we have to do our best to, to, to empower the, the sort of the strategy and empower the intent that the developers are, are delivering on. And yes, we need to be, we need to be seeking every opportunity to, to lift that developer up and, and give them the tools sort of in the moment we wanna wrap the developer in armor, not wake them down with an anchor. And that's the, that's the thing that we, we want to keep striving towards is, is making that possible for the security team. >>So you guys are very relevant right now. APIs are the favorite environment for hackers was seeing that with breaches and in the headlines every day, I love this comprehensive approach, developer focused op security team enablement, operationally relevant to all, all, all parties. I have to ask you, how do you answer and, and talk about the competition, cuz with the rise of this trend, a lot of more people entering this market, how should a customer decide between no name and everyone else pitch in API security? What's the, is there nuances? Is there differences? How do you compare what's the differentiation? >>Yeah, I think, you know, the, the, the first thing to mention is that, you know, companies that are in the space of API security, we, we have a lot more in common. We probably have differences cause we're focused on the same problems, but there's, there's really two changes that we've made bringing to market an API platform. Number one is to look full lifecycle. So it used to be that you could buy, you know, DAST and SAS software testing tools, no name has API testing in, so, you know, for source code and for pipeline integrations along with then the runtime and posture management, which is really the production network. And so we really do think that we span east west a much broader set of controls for the API. And then the second characteristic is, is architectural fit. Particularly in a runtime production environment, you have to have a solution that does, does not create significant disruptions. >>It doesn't require agent deployment that can maximize the, the, the infrastructure that an organization already has. So we think our, you know, a big advantage for us in, in the production environment is that we can, we can adapt to the contour of the customer. We don't have to have the customer adapt to the contour of our architecture. So that flexibility really serves well, particularly with complex organizations, global organizations or those that have on, you know, data centers and, and, and public cloud and, and multiple varieties. So our ability to sort of adapt to a customer's architecture really makes us sort of like a universal tool for organizations. And we think that's really, you know, bears out in the, in the customers, in the large organizations and enterprises that have adapted us because we can adapt really any condition. >>Yeah. And that's great alignment too, from an execution consumption standpoint, it's gotta be fast with a developer. You gotta be frictionless as much as possible. Good stuff there. I have to ask you Carl, as, as you are a CISO chief information security officer, you know, your peers are out there. They're they're, they got, man there's so much going on around them. They gotta manage the current, protect the future and architect, the next level infrastructure for security. What do you, what do you see out there as a CSO with your peers in the marketplace? You know, practitioners, you know, evaluating companies, evaluating technologies, managing the threat landscape, unlimited surface area, evolving with the edge coming online, what's on their mind. How do you see it? What's your, what's your view there? What's your vision if you were, if you were in the hot seat in a big organization, I mean, obviously you're got a hot seat there with no name, but you're also, you know, you're seeing both sides of the coin at no name, you know, the CISO. So are they the frog and boiling water right now? Or like, like what's going on in their world right now? How would you describe the state of, of the CISO in cyber security? >>Yeah, there's, there's, there's two kind of tactical themes. I think almost every CISO shares the, the, the, the, the first tactical theme is, is I as a CISO. I probably know there's a technology out there to solve a little bit of every problem possible. Like, that's you objectively true. But what I don't wanna do is I don't wanna buy 75 technologies when I could buy 20 platforms or 12 that could solve that problem set. So the first thing I wanna do is as I, I want to communicate what we do from the perspective of, of like a single platform that does multiple things from source code testing, to posture and configuration to runtime defense, because I, a CISO's sensibilities is, is, is, is challenged by having 15 technologies. I really just want a couple to manage because it's complexity that we're managing when we're managing all these technologies. >>Even if something works for a point problem set, I, I don't want another technology to implement and manage. That's, that's just throwing money. Oftentimes at, at suboptimal, you know, we're not getting the results when we just throw tools at a problem. So the, that that platform concept is I think really appealing cuz every CSO is looking to consider, how do I reduce the number of technologies that I have? The second thing is every organization faces the challenge of talent. So what are, what are my options for talent, for mitigating? What is sort of, I, I can't hire enough qualified people at a remotely reasonable price to staff, what I'd like to. So I have to pursue both the utilizing third parties who have expertise in professional services that I can deploy to, to, to, to solve my problems, but also then to employing automation. So, you know, the, a great example would be if I have a team that has a, you know, a five person application security team, and now next year, my applications security or my, my applications team is gonna develop three times the number of, of applications and APIs. >>I can't scale my team by a factor of three, just to meet that demand. I have to pursue automation opportunities. And so we really want to measure the, the, the successes that we can achieve with automation so that a CISO can look at us as, as an answer to complexity rather than as a source of new complexity, because it is true that we're overwhelmed with the options at our disposal. Most of those options create more complexity than they solve for. And, and, you know, I pursue that in, in my practice, which is to, is to figure out how to sort of limit the complexity of what is already very complicated, you know, role and protecting an organization. >>Got it. And when you, when, when the CSO says Carl, what's in it for me with no name, what's the answer, what's the bumper bumper sticker. >>It, it's reducing complexity. It's making a very sophisticated problem. Set, simple to solve for APIs are a, are a class of assets that there's an answer for that answer includes automation and includes professional services. And we can, we can achieve a high degree of sophistication relatively speaking with a low amount of effort. When we look across our security team, this is a, this is a solvable problem space and, and we can do so pretty efficiently. >>Awesome. Well call, thank you so much for showcasing no name. And the last minute we have here, give a quick plug for the company, give a little stats, some factoids that people might be interested in. How big is the company? What are you guys doing enthusiastic about the solution? Share some, yep. Give the plug. >>Sure. We're, we're, we're a company of just about 300 employees now all across the globe, Asia Pacific, north America, Europe, and the middle east, you know, tremendous success with the release of our, of our software testing module, which we call active testing. We have such a variety of ways also to, to sort of test and take Nona for a test drive from sandboxes to POVs and, and some really amazing opportunities to, to show and tell and have the organizations diagnose quickly where, where they are. And so we, we love to, we love to, to, to show off the platform and, and let people take it for a test drive. So, you know, no name, security.com and any, anywhere in the world, you are, we can, we can deploy a, a, a sales engineer who can help show you the platform and, and show you all the things that, that we can, we can offer for the organization. >>Carl, great insight. Thank you again for sharing the stats and talk about the industry and really showcasing some of the key things you guys are doing in the industry for customers. We really appreciate it. Thanks for coming on. >>Thanks John. Appreciate it. >>Okay. That's the, this is the ADBU startup showcase. John fur, your host season two, episode four of this ongoing series covering the exciting new growing startups from the AWS ecosystem in cybersecurity. Thanks for watching.

>>Hello. Welcome back to our super cloud 22 event. I'm John F host the cue with my co-host Dave ante. Extracting the signal from noise. We're proud to have two amazing cube alumnis here. We got Sanja Putin. Who's now the CEO of cohesive the emo Aaron who's the CTO. Co-founder also former CEO Cub alumni. The father of hyper-converged welcome back to the cube I endorsed the >>Cloud. Absolutely. Is the father. Great >>To see you guys. Thank thanks for coming on and perfect timing. The new job taking over that. The helm Mo it at cohesive big news, but part of super cloud, we wanna dig into it. Thanks for coming on. >>Thank you for having >>Us here. So first of all, we'll get into super before we get into the Supercloud. I want to just get the thoughts on the move Sanjay. We've been following your career since 2010. You've been a cube alumni from that point, we followed that your career. Why cohesive? Why now? >>Yeah, John David, thank you first and all for having us here, and it's great to be at your event. You know, when I left VMware last year, I took some time off just really primarily. I hadn't had a sabbatical in probably 18 years. I joined two boards, Phillips and sneak, and then, you know, started just invest and help entrepreneurs. Most of them were, you know, Indian Americans like me who were had great tech, were looking for the kind of go to market connections. And it was just a wonderful year to just de to unwind a bit. And along the, the way came CEO calls. And I'd asked myself, the question is the tech the best in the industry? Could you see value creation that was signi significant and you know, three, four months ago, Mohit and Carl Eschenbach and a few of the board members of cohesive called me and walk me through Mo's decision, which he'll talk about in a second. And we spent the last few months getting to know him, and he's everything you describe. He's not just the father of hyperconverge. And he wrote the Google file system, wicked smart, built a tech platform better than that second time. But we had to really kind of walk through the chemistry between us, which we did in long walks in, in, you know, discrete places so that people wouldn't find us in a Starbucks and start gossiping. So >>Why Sanjay? There you go. >>Actually, I should say it's a combination of two different decisions. The first one was to, for me to take a different role and I run the company as a CEO for, for nine years. And, you know, as a, as a technologist, I always like, you know, going deep into technology at the same time, the CEO duties require a lot of breadth, right? You're talking to customers, you're talking to partners, you're doing so much. And with the way we've been growing the with, you know, we've been fortunate, it was becoming hard to balance both. It's really also not fair to the company. Yeah. So I opted to do the depth job, you know, be the visionary, be the technologist. And that was the first decision to bring a CEO, a great CEO from outside. >>And I saw your video on the site. You said it was your decision. Yes. Go ahead. I have to ask you, cuz this is a real big transition for founders and you know, I have founder artists cuz everyone, you know, calls me that. But being the founder of a company, it's always hard to let go. I mean nine years as CEO, it's not like you had a, you had a great run. So this was it timing for you? Was it, was it a structural shift, like at super cloud, we're talking about a major shift that's happening right now in the industry. Was it a balance issue? Was it more if you wanted to get back in and in the tech >>Look, I, I also wanna answer, you know, why Sanja, but, but I'll address your question first. I always put the company first what's right for the company. Is it for me to start get stuck the co seat and try to juggle this depth and Brad simultaneously. I mean, I can stroke my ego a little bit there, but it's not good for the company. What's best for the company. You know, I'm a technologist. How about I oversee the technology part in partnership with so many great people I have in the company and I bring someone kick ass to be the CEO. And so then that was the second decision. Why Sanja when Sanjay, you know, is a very well known figure. He's managed billions of dollars of business in VMware. You know, been there, done that has, you know, some of the biggest, you know, people in the industry on his speed dial, you know, we were really fortunate to have someone like that, come in and accept the role of the CEO of cohesive. I think we can take the company to new Heights and I'm looking forward to my partnership with, with Sanja on this. >>It it's we, we called it the splash brothers and >>The, >>In the vernacular. It doesn't matter who gets the ball, whether it's step clay, we shoot. And I think if you look at some of the great partnerships, whether it was gates bomber, there, plenty of history of this, where a founder and a someone who was, it has to be complimentary skills. If I was a technologist myself and wanted to code we'd clash. Yeah. But I think this was really a match me in heaven because he, he can, I want him to keep innovating and building the best platform for today in the future. And our customers tell one customer told me, this is the best tech they've seen since VMware, 20 years ago, AWS, 10 years ago. And most recently this was a global 100 big customers. So I feel like this combination, now we have to show that it works. It's, you know, it's been three, four months. My getting to know him, you know, I'm day eight on the job, but I'm loving it. >>Well, it's a sluman model too. It's more modern example. You saw, he did it with Fred Ludy at service now. Yes. And, and of course at, at snowflake, yeah. And his book, you read his book. I dunno if you've read his book, amp it up, but app it up. And he says, I always you'll love this. Give great deference to the founder. Always show great respect. Right. And for good reason. So >>In fact, I mean you could talk to him, you actually met to >>Frank. I actually, you know, a month or so back, I actually had dinner with him in his ranch in Moana. And I posed the question. There was a number of CEOs that went there and I posed him the question. So Frank, you know, many of us, we grow being deaf guys, you know? And eventually when we take on the home of our CEO, we have to do breadth. How do you do it? And he's like, well, let me tell you, I was never a death guy. I'm a breath guy. >>I'm like, >>That's my answer. Yeah. >>So, so I >>Want the short story. So the day I got the job, I, I got a text from Frank and I said, what's your advice the first time CEO, three words, amp it up, >>Amp it up. Right? Yeah. >>And so you're always on brand, man. >>So you're an amazing operator. You've proven that time and time again at SAP, VMware, et cetera, you feel like now you, you, you wanna do both of those skills. You got the board and you got the operations cuz you look, you know, look at sloop when he's got Scarelli wherever he goes, he brings Scarelli with him as sort of the operator. How, how do you, how are you thinking >>About that? I mean it's early days, but yeah. Yeah. Small. I mean I've, you know, when I was, you know, it was 35,000 people at VMware, 80, 90,000 people at SAP, a really good run. The SAP run was 10 to 20 billion innovative products, especially in analytics and VMware six to 12 end user computing cloud. So I learned a lot. I think the company, you know, being about 2000 employees plus not to mayor tomorrow, but over the course next year I can meet everybody. Right? So first off the executive team, 10 of us, we're, we're building more and more cohesiveness if I could use that word between us, which is great, the next, you know, layers of VPs and every manager, I think that's possible. So I I'm a people person and a customer person. So I think when you take that sort of extroverted mindset, we'll bring energy to the workforce to, to retain the best and then recruit the best. >>And you know, even just the week we, we were announced that this announcement happened. Our website traffic went through the roof, the highest it's ever been, lots of resumes coming in. So, and then lots of customer engagement. So I think we'll take this, but I, I feel very good about the possibilities, because see, for me, I didn't wanna walk into the company to a company where the technology risk was high. Okay. I feel like that I can go to bed at night and the technology risk is low. This guy's gonna run a machine at the current and the future. And I'm hearing that from customers. Now, what I gotta do is get the, the amp it up part on the go to market. I know a little thing or too about >>That. You've got that down. I think the partnership is really key here. And again, nine use the CEO and then Sanja points to our super cloud trend that we've been looking at, which is there's another wave happening. There's a structural change in real time happening now, cloud one was done. We saw that transition, AWS cloud native now cloud native with an kind of operating system kind of vibe going on with on-premise hybrid edge. People say multi-cloud, but we're looking at this as an opportunity for companies like cohesive to go to the next level. So I gotta ask you guys, what do you see as structural change right now in the industry? That's disruptive. People are using cloud and scale and data to refactor their business models, change modern cases with cloud native. How are you guys looking at this next structural change that's happening right now? Yeah, >>I'll take that. So, so I'll start by saying that. Number one, data is the new oil and number two data is exploding, right? Every year data just grows like crazy managing data is becoming harder and harder. You mentioned some of those, right? There's so many cloud options available. Cloud one different vendors have different clouds. There is still on-prem there's edge infrastructure. And the number one problem that happens is our data is getting fragmented all over the place and managing so many fragments of data is getting harder and harder even within a cloud or within on-prem or within edge data is fragmented. Right? Number two, I think the hackers out there have realized that, you know, to make money, it's no longer necessary to Rob banks. They can actually see steal the data. So ransomware attacks on the rise it's become a boardroom level discussion. They say there's a ransomware attack happening every 11 seconds or so. Right? So protecting your data has become very important security data. Security has become very important. Compliance is important, right? So people are looking for data management solutions, the next gen data management platform that can really provide all this stuff. And that's what cohesive is about. >>What's the difference between data management and backup. Explain that >>Backup is just an entry point. That's one use case. I wanna draw an analogy. Let's draw an analogy to my former company, Google right? Google started by doing Google search, but is Google really just a search engine. They've built a platform that can do multiple things. You know, they might have started with search, but then they went down to roll out Google maps and Gmail and YouTube and so many other things on that platform. So similarly backups might be just the first use case, but it's really about that platform on which you can do more with the data that's next gen data management. >>But, but you am, I correct. You don't consider yourself a security company. One of your competitors is actually pivoting and in positioning themselves as a security company, I've always felt like data management, backup and recovery data protection is an adjacency to security, but those two worlds are coming together. How do you see >>It? Yeah. The way I see it is that security is part of data management. You start maybe by backing with data, but then you secure it and then you do more with that data. If you're only doing security, then you're just securing the data. You, you gotta do more with the data. So data management is much bigger. So >>It's a security is a subset of data. I mean, there you go. Big TA Sanjay. >>Well, I mean I've, and I, I, I I'd agree. And I actually, we don't get into that debate. You know, I've told the company, listen, we'll figure that out. Cuz who cares about the positioning at the bottom? My email, I say we are data management and data security company. Okay. Now what's the best word that describes three nouns, which I think we're gonna do management security and analytics. Okay. He showed me a beautiful diagram, went to his home in the course of one of these, you know, discrete conversations. And this was, I mean, he's done this before. Many, if you watch on YouTube, he showed me a picture of an ice big iceberg. And he said, listen, you know, if you look at companies like snowflake and data bricks, they're doing the management security and mostly analytics of data. That's the top of the iceberg, the stuff you see. >>But a lot of the stuff that's get backed archive is the bottom of the iceberg that you don't see. And you try to, if you try to ask a question on age data, the it guy will say, get a ticket. I'll come back with three days. I'll UNIV the data rehydrate and then you'll put it into a database. And you can think now imagine that you could do live searches analytics on, on age data that's analytics. So I think the management, the security, the analytics of, you know, if you wanna call it secondary data or backed up data or data, that's not hot and live warm, colder is a huge opportunity. Now, what do you wanna call one phrase that describes all of it. Do you call that superpower management security? Okay, whatever you wanna call it. I view it as saying, listen, let's build a platform. >>Some people call Google, a search company. People, some people call Google and information company and we just have to go and pursue every CIO and every CSO that has a management and a security and do course analytics problem. And that's what we're doing. And when I talk to the, you know, I didn't talk to all the 3000 customers, but the biggest customers and I was doing diligence. They're like this thing has got enormous potential. Okay. And we just have to now go focus, get every fortune 1000 company to pick us because this problem, even the first use case you talk back up is a little bit like, you know, razor blades and soap you've needed. You needed it 30 years ago and you'll need it for 30 years. It's just that the tools that were built in the last generation that were companies formed in 1990s, one of them I worked for years ago are aids are not built for the cloud. So I think this is a tremendous opportunity where many of those, those, those nos management security analytics will become part of what we do. And we'll come up with the right phrase for what the companies and do course >>Sanjay. So ma and Sanja. So given that given that's this Google transition, I like that example search was a data problem. They got sequenced to a broader market opportunity. What super cloud we trying to tease out is what does that change over from a data standpoint, cuz now the operating environments change has become more complex and the enterprises are savvy. Developers are savvy. Now they want, they want SAS solutions. They want freemium and expanding. They're gonna drive the operations agenda with DevOps. So what is the complexity that needs to be abstracted away? How do you see that moment? Because this is what people are talking about. They're saying security's built in, driven by developers. Developers are driving operations behavior. So what is the shift? Where do you guys see this new? Yeah. Expansive for cohesive. How do you fit into super cloud? >>So let me build up from that entry point. Maybe back up to what you're saying is the super cloud, right? Let me draw that journey. So let's say the legacy players are just doing backups. How, how sad is it that you have one silo sitting there just for peace of mind as an insurance policy and you do nothing with the data. If you have to do something with the data, you have to build another silo, you have to build another copy. You have to manage it separately. Right. So clearly that's a little bit brain damaged. Right. So, okay. So now you take a little bit of, you know, newer vendors who may take that backup platform and do a little bit more with that. Maybe they provide security, but your problem still remains. How do you do more with the data? How do you do some analytics? >>Like he's saying, right. How do you test development on that? How do you migrate the data to the cloud? How do you manage it? The data at scale? How do you do you provide a unified experience across, across multiple cloud, which you're calling the super cloud. That's where cohesive goes. So what we do, we provide a platform, right? We have tentacles in on-prem in each of the clouds. And on top of that, it looks like one platform that you manage. We have a single control plane, a UI. If you may, a single pin of glass, if, if you may, that our customers can use to manage all of it. And now it looks, starts looking like one platform. You mentioned Google, do you, when you go to, you know, kind Google search or a URL, do you really care? What happens behind the scenes mean behind the scenes? Google's built a platform that spans the whole world. No, >>But it's interesting. What's behind the scenes. It's a beautiful now. And I would say, listen, one other thing to pull on Dave, on the security part, I saw a lot of vendors this day in this space, white washing a security message on top of backup. Okay. And CSO, see through that, they'll offer warranties and guarantees or whatever, have you of X million dollars with a lot of caveats, which will never paid because it's like escape clause here. We won't pay it. Yeah. And, and what people really want is a scalable solution that works. And you know, we can match every warranty that's easy. And what I heard was this was the most scalable solution at scale. And that's why you have to approach this with a Google type mindset. I love the fact that every time you listen to sun pitch, I would, what, what I like about him, the most common word to use is scale. >>We do things at scale. So I found that him and AUR and some of the early Google people who come into the company had thought about scale. And, and even me it's like day eight. I found even the non-tech pieces of it. The processes that, you know, these guys are built for simple things in some cases were better than some of the things I saw are bigger companies I'd been used to. So we just have to continue, you know, building a scale platform with the enterprise. And then our cloud product is gonna be the simple solution for the masses. And my view of the world is there's 5,000 big companies and 5 million small companies we'll push the 5 million small companies as the cloud. Okay. Amazon's an investor in the company. AWS is a big partner. We'll talk about I'm sure knowing John's interest in that area, but that's a cloud play and that's gonna go to the cloud really fast. You not build you're in the marketplace, you're in the marketplace. I mean, maybe talk about the history of the Amazon relationship investing and all that. >>Yeah, absolutely. So in two years back late 2020, we, you know, in collaboration with AWS who also by the way is an investor now. And in cohesive, we rolled out what we call data management as a service. It's our SaaS service where we run our software in the cloud. And literally all customers have to do is just go there and sign on, right? They don't have to manage any infrastructure and stuff. What's nice is they can then combine that with, you know, software that they might have bought from cohesive. And it still looks like one platform. So what I'm trying to say is that they get a choice of the, of the way they wanna consume our software. They can consume it as a SAS service in the cloud. They can buy our software, manage it themselves, offload it to a partner on premises or what have you. But it still looks like that one platform, what you're calling a Supercloud >>Yeah. And developers are saying, they want the bag of Legos to compose their solutions. That's the Nirvana they want to get there. So that's, it has to look the same. >>Well, what is it? What we're calling a Superlo can we, can we test that for a second? So data management and service could span AWS and on-prem with the identical experience. So I guess I would call that a Supercloud I presume it's not gonna through AWS span multiple clouds, but, but >>Why not? >>Well, well interesting cuz we had this, I mean, so, okay. So we could in the future, it doesn't today. Well, >>David enough kind of pause for a second. Everything that we do there, if we do it will be customer driven. So there might be some customers I'll give you one Walmart that may want to store the data in a non AWS cloud risk cuz they're competitors. Right. So, but the control plane could still be in, in, in the way we built it, but the data might be stored somewhere else. >>What about, what about a on-prem customer? Who says, Hey, I, I like cohesive. I've now got multiple clouds. I want the identical experience across clouds. Yeah. Okay. So, so can you do that today? How do you do that today? Can we talk >>About that? Yeah. So basically think roughly about the split between the data plane and the control plane, the data plane is, you know, our cohesive clusters that could be sitting on premises that could be sitting in multiple data centers or you can run an instance of that cluster in the cloud, whichever cloud you choose. Right. That's what he was referring to as the data plane. So collectively all these clusters from the data plane, right? They stored the data, but it can all be managed using the control plane. So you still get that single image, the single experience across all clouds. And by the way, the, the, the, the cloud vendor does actually benefit because here's a customer. He mentioned a customer that may not wanna go to AWS, but when they get the data plane on a different cloud, whether it's Azure, whether it's the Google cloud, they then get data management services. Maybe they're able to replicate the data over to AWS. So AWS also gains. >>And your deployment model is you instantiate the cohesive stack on each of the regions and clouds, is that correct? And you building essentially, >>It all happens behind the scenes. That's right. You know, just like Google probably has their tentacles all over the world. We will instantiate and then make it all look like one platform. >>I mean, you should really think it's like a human body, right? The control planes, the head. Okay. And that controls everything. The data plane is large because it's a lot of the data, right? It's the rest of the body, that data plane could be wherever you want it to be. Traditionally, the part the old days was tape. Then you got disk. Now you got multiple clouds. So that's the way we think about it. And there on that piece of it will be neutral, right? We should be multi-cloud to the data plane being every single place. Cause it's customer demand. Where do you want your store data? Air gapped. On-prem no problem. We'll work with Dell. Okay. You wanna be in a particular cloud, AWS we'll work then optimized with S3 and glacier. So this is where I think the, the path to a multi-cloud or Supercloud is to be customer driven, but the control plane sits in Amazon. So >>We're blessed to have a number of, you know, technical geniuses in here. So earlier we were speaking to Ben wa deja VI, and what they do is different. They don't instantiate an individual, you know, regions. What they do is of a single global. Is there a, is there an advantage of doing it the way the cohesive does it in terms of simplicity or how do you see that? Is that a future direction for you from a technology standpoint? What are the trade offs there? >>So you want to be where the data is when you said single global, I take it that they run somewhere and the data has to go there. And in this day age, correct >>Said that. He said, you gotta move that in this >>Day and >>Age query that's, you know, across regions, look >>In this day and age with the way the data is growing, the way it is, it's hard to move around the data. It's much easier to move around the competition. And in these instances, what have you, so let the data be where it is and you manage it right there. >>So that's the advantage of instantiating in multiple regions. As you don't have to move the >>Data cost, we have the philosophy we call it. Let's bring the, the computation to the data rather than the data to >>The competition and the same security model, same governance model, same. How do you, how do you federate that? >>So it's all based on policies. You know, this overarching platform controlled by, by the control plane, you just, our customers just put in the policies and then the underlying nuts and bolts just take care >>Of, you know, it's when I first heard and start, I started watching some of his old videos, ACE really like hyperconverged brought to secondary storage. In fact, he said, oh yeah, that's great. You got it. Because I first called this idea, hyperconverged secondary storage, because the idea of him inventing hyperconverge was bringing compute to storage. It had never been done. I mean, you had the kind of big VC stuff, but these guys were the first to bring that hyperconverge at, at Nutanix. So I think this is that same idea of bringing computer storage, but now applied not to the warm data, but to the rest of the data, including a >>Lot of, what about developers? What's, what's your relationship with developers? >>Maybe you talk about the marketplace and everything >>He's yeah. And I'm, I'm curious as to do you have a PAs layer, what we call super PAs layer to create an identical developer experience across your Supercloud. I'm gonna my >>Term. So we want our customers not just to benefit from the software that we write. We also want them to benefit from, you know, software that's written by developers by third party people and so on and so forth. So we also support a marketplace on the platform where you can download apps from third party developers and run them on this platform. There's a, a number of successful apps. There's one, you know, look like I said, our entry point might be backups, but even when backups, we don't do everything. Look, for instance, we don't backup mainframes. There is a, a company we partner with, you know, and their software can run in our marketplace. And it's actually used by many, many of our financial customers. So our customers don't get, just get the benefit of what we build, but they also get the benefit of what third parties build. Another analogy I like to draw. You can tell. And front of analogy is I drew an analogy to hyperscale is like Google. Yeah. The second analogy I like to draw is that to a simple smartphone, right? A smartphone starts off by being a great phone. But beyond that, it's also a GPS player. It's a, it's a, it's a music player. It's a camera, it's a flashlight. And it also has a marketplace from where you can download apps and extend the power of that platform. >>Is that a, can we think of that as a PAs layer or no? Is it really not? You can, okay. You can say, is it purpose built for what you're the problem that you're trying to solve? >>So we, we just built APIs. Yeah. Right. We have an SDK that developers can use. And through those APIs, they get to leverage the underlying services that exist on the platform. And now developers can use that to take advantage of all that stuff. >>And it was, that was a key factor for me too. Cause I, what I, you know, I've studied all the six, seven players that sort of so-called leaders. Nobody had a developer ecosystem, nobody. Right? The old folks were built for the hardware era, but anyones were built for the cloud to it didn't have any partners were building on their platform. So I felt for me listen, and that the example of, you know, model nine rights, the name of the company that does back up. So there's, there's companies that are built on and there's a number of others. So our goal is to have a big tent, David, to everybody in the ecosystem to partner with us, to build on this platform. And, and that may take over time, but that's the way we're build >>It. And you have a metadata layer too, that has the intelligence >>To correct. It's all abstract. That that's right. So it's a combination of data and metadata. We have lots of metadata that keeps track of where the data is. You know, it allows you to index the data you can do quick searches. You can actually, you, we talking about the control plan from that >>Tracing, >>You can inject a search that'll through search throughout your multi-cloud environment, right? The super cloud that you call it. We have all that, all that goodness sounds >>Like a Supercloud John. >>Yeah. I mean, data tracing involved can trace the data lineage. >>You, you can trace the data lineage. So we, you know, provide, you know, compliance and stuff. So you can, >>All right. So my final question to wrap up, we guys, first of all, thanks for coming on. I know you're super busy, San Jose. We, we know what you're gonna do. You're gonna amp it up and, you know, knock all your numbers out. Think you always do. But what I'm interested in, what you're gonna jump into, cuz now you're gonna have the creative license to jump in to the product, the platform there has to be the next level in your mind. Can you share your thoughts on where this goes next? Love the control plane, separate out from the data plane. I think that plays well for super. How >>Much time do you have John? This guy's got, he's got a wealth. Ditis keep >>Going. Mark. Give us the most important thing you're gonna focus on. That kind of brings the super cloud and vision together. >>Yeah. Right away. I'm gonna, perhaps I, I can ion into two things. The first one is I like to call it building the, the machine, the system, right. Just to draw an analogy. Look, I draw an analogy to the us traffic system. People from all walks of life, rich, poor Democrats, Republicans, you know, different states. They all work in the, the traffic system and we drive well, right. It's a system that just works. Whereas in some other countries, you know, the system doesn't work. >>We know, >>We know a few of those. >>It's not about works. It's not about the people. It's the same people who would go from here to those countries and, and not dry. Well, so it's all about the system. So the first thing I, I have my sights on is to really strengthen the system that we have in our research development to make it a machine. I mean, it functions quite well even today, but wanna take it to the next level. Right. So that I wanna get to a point where innovation just happens in the grassroots. And it just, just like >>We automations scale optic brings all, >>Just happens without anyone overseeing it. Anyone there's no single point of bottleneck. I don't have to go take any diving catches or have you, there are people just working, you know, in a decentralized fashion and innovation just happens. Yeah. The second thing I work on of course is, you know, my heart and soul is in, you know, driving the vision, you know, the next level. And that of course is part of it. So those are the two things >>We heard from all day in our super cloud event that there's a need for an, an operating system. Yeah. Whether that's defacto standard or open. Correct. Do you see a consortium around the corner potentially to bring people together so that things could work together? Cuz there really isn't no stand there. Isn't a standards bodies. Now we have great hyperscale growth. We have on-prem we got the super cloud thing happening >>And it's a, it's kind of like what is an operating system? Operating system exposes some APIs that the applications can then use. And if you think about what we've been trying to do with the marketplace, right, we've built a huge platform and that platform is exposed through APIs. That third party developers can use. Right? And even we, when we, you know, built more and more services on top, you know, we rolled our D as we rolled out, backup as a service and a ready for thing security as a service governance, as a service, they're using those APIs. So we are building a distributor, putting systems of sorts. >>Well, congratulations on a great journey. Sanja. Congratulations on taking the hem. Thank you've got ball control. Now you're gonna be calling the ball cohesive as they say, it's, >>It's a team. It's, you know, I think I like that African phrase. If you want to go fast, you go alone. If you wanna go far, you go together. So I've always operated with the best deal. I'm so fortunate. This is to me like a dream come true because I always thought I wanted to work with a technologist that frees me up to do what I like. I mean, I started as an engineer, but that's not what I am today. Right? Yeah. So I do understand the product and this category I think is right for disruption. So I feel excited, you know, it's changing growing. Yeah. No. And it's a, it requires innovation with a cloud scale mindset and you guys have been great friends through the years. >>We'll be, we'll be watching you. >>I think it's not only disruption. It's creation. Yeah. There's a lot of white space that just hasn't been created yet. >>You're gonna have to, and you know, the proof, isn't the pudding. Yeah. You already have five of the biggest 10 financial institutions in the us and our customers. 25% of the fortune 500 users, us two of the biggest five pharmaceutical companies in the world use us. Probably, you know, some of the biggest companies, you know, the cars you have, you know, out there probably are customers. So it's already happening. >>I know you got an IPO filed confidentially. I know you can't talk numbers, but I can tell by your confidence, you're feeling good right now we are >>Feeling >>Good. Yeah. One day, one week, one month at a time. I mean, you just, you know, I like the, you know, Jeff Bezos, Andy jazzy expression, which is, it's always day one, you know, just because you've had success, even, you know, if, if a and when an IPO O makes sense, you just have to stay humble and hungry because you realize, okay, we've had a lot of success in the fortune 1000, but there's a lot of white space that hasn't picked USS yet. So let's go, yeah, there's lots of midmarket account >>Product opportunities are still, >>You know, I just stay humble and hungry and if you've got the team and then, you know, I'm really gonna be working also in the ecosystem. I think there's a lot of very good partners. So lots of ideas brew through >>The head. Okay. Well, thank you so much for coming on our super cloud event and, and, and also doubling up on the news of the new appointment and congratulations on the success guys. Coverage super cloud 22, I'm sure. Dave ante, thanks for watching. Stay tuned for more segments after this break.

>>Hello, Ron. Welcome to AWS reinforce here. Live in Boston, Massachusetts. I'm John feer, host of the cube. We're here at Carl Matson. CSO at no name security. That's right, no name security, no name securities, also a featured partner at season two, episode four of our upcoming eightish startup showcase security themed event happening in the end of August. Look for that at this URL, AWS startups.com, but we're here at reinforc Carl. Thanks for joining me today. Good to see >>You. Thank you for having us, John. >>So this show security, it's not as packed as the eight of us summit was in New York. That just happened two weeks ago, 19,000 people here, more focused crowd. Lot at stake operations are under pressure. The security teams are under a lot of pressure as apps drive more and more cloud native goodness. As we say, the gen outta the bottle, people want more cloud native apps. Absolutely. That's put a lot of pressure on the ops teams and the security teams. That's the core theme here. How do you see it happening? How do you see this unfolding? Do you agree with that? And how would you describe today's event? >>Well, I think you're, you're spot on. I think the, the future of it is increasingly becoming the story of developers and APIs becoming the hero, the hero of digital transformation, the hero of public cloud adoption. And so this is really becoming much more of a developer-centric discussion about where we're moving our applications and, and where they're hosted, but also how they're designed. And so there's a lot of energy around that right now around focusing security capabilities that really appeal to the sensibilities and the needs of, of modern applications. >>I want to get to know name security a second, and let you explain what you guys do. Then I'll have a few good questions for you to kind of unpack that. But the thing about the structural change that's happened with cloud computing is kind of, and kind of in the past now, DevOps cloud scale, large scale data, the rise of the super cloud companies like snowflake capital, one there's examples of companies that don't even have CapEx investments building on the cloud. And in a way, our, the success of DevOps has created another sea of problems and opportunities that is more complexity as the benefits of DevOps and open source, continue to rise, agile applications that have value can be quantified. There's no doubt with the pandemic that's value there. Yeah. Now you have the collateral damage of success, a new opportunity to abstract away, more complexity to go to the next level. Yep. This is a big industry thing. What are the key opportunities and areas as this new environment, cuz that's the structural change happening now? Yep. What's the key dynamics right now. That's driving this new innovation and what are some of those problem areas that are gonna be abstracted away that you see? >>Well, the, the first thing I I'd suggest is is to, to lean into those structural changes and take advantage of them where they become an advantage for governance, security risk. A perfect example is automation. So what we have in microservices, applications and cloud infrastructures and new workloads like snowflake is we have workloads that want to talk, they want to be interoperated with. And because of that, we can develop new capabilities that take advantage of those of those capabilities. And, and so we want to have on our, on our security teams in particular is we wanna have the talent and the tools that are leaning into and capitalizing on exactly those strengths of, of the underlying capabilities that you're securing rather than to counter that trend, that the, the security professional needs to get ahead of it and, and be a part of that discussion with the developers and the infrastructure teams. >>And, and again, the tructure exchange could kill you too as well. I mean, some benefits, you know, data's the new oil, but end of the day it could be a problematic thing. Sure. All right. So let's get that. No names talk about the company. What you guys do, you have an interesting approach, heavily funded, good success, good buzz. What's going on with the company? Give the quick overview. >>Well, we're a company that's just under three years old and, and what APIs go back, of course, a, a decade or more. We've all been using APIs for a long time, but what's really shifted over the last couple of years is the, is the transition of, of applications and especially business critical processes to now writing on top of public facing APIs where API used to be the behind the scenes interconnection between systems. Now those APIs are exposed to their public facing. And so what we focus on as a company is looking at that API as a, as a software endpoint, just like any other endpoint in our environments that we're historically used to. That's an endpoint that needs full life cycle protection. It needs to be designed well secure coding standards for, for APIs and tested. Well, it also has to be deployed into production configured well and operated well. And when there's a misuse or an attack in progress, we have to be able to protect and identify the, the risks to that API in production. So when you add that up, we're looking at a full life cycle view of the API, and it's really it's about time because the API is not new yet. We're just starting to now to apply like actual discipline and, and practices that help keep that API secure. >>Yeah. It's interesting. It's like what I was saying earlier. They're not going anywhere. They're not going, they're the underpinning, the underlying benefit of cloud yes. Cloud native. So it's just more, more operational stability, scale growth. What are some of the challenges that, that are there and what do you guys do particularly to solve it? You're protecting it. Are you scaling it? What specifically are you guys addressing? >>But sure. So I think API security, even as a, as a discipline is not new, but I think the, the, the traditional look at API security looks only at, at the quality of the source code. Certainly quality of the source code of API is, is sort of step one. But what we see in, in practices is most of the publicly known API compromises, they weren't because of bad source code that they because of network misconfiguration or the misapplication of policy during runtime. So a great example of that would be developer designs, an API designs. It in such a way that Gar that, that enforces authentication to be well designed and strong. And then in production, those authentication policies are not applied at a gateway. So what we add to the, we add to the, to the conversation on API security is helping fill all those little gaps from design and testing through production. So we can see all of the moving parts in the, the context of the API to see how it can be exploited and, and how we can reduce risk in independent of. >>So this is really about hardening the infrastructure yep. Around cuz the developer did their job in that example. Yep. So academic API is well formed working, but something didn't happen on the network or gateway box or app, you know, some sort of network configuration or middleware configuration. >>Absolutely. So in our, in our platform, we, we essentially have sort of three functional areas. There's API code testing, and then we call next is posture management posture. Management's a real thing. If we're talking about a laptop we're talking about, is it up to date with patches? Is it configured? Well, is it secure network connectivity? The same is true with APIs. They have to be managed and cared for by somebody who's looking at their posture on the network. And then of course then there's threat defense and run time protection. So that posture management piece, that's really a new entrant into the discussion on API security. And that's really where we started as a company is focusing on that sort of acute gap of information, >>Posture, protection, >>Posture, and protection. Absolutely >>Define that. What does that, what does posture posture protection mean? How would you define that? >>Sure. I think it's a, it's identifying the inherent risk exposure of an API. Great example of that would be an API that is addressable by internal systems and external systems at the same time. Almost always. That is, that is an error. It's a mistake that's been made so well by, by identifying that misconfiguration of posture, then we can, we can protect that API by restricting the internet connectivity externally. That's just a great example of posture. We see almost every organization has that and it's never intended. >>Great, great, great call out. Thanks for sharing. All right, so I'm a customer. Yep. Okay. Look at, Hey, I already got an app firewall API gateway. Why do I need another tool? >>Well, first of all, web application firewalls are sort of essential parts of a security ecosystem. An API management gateway is usually the brain of an API economy. What we do is we, we augment those platforms with what they don't do well and also when they're not used. So for example, in, in any environment, we, we aspire to have all of our applications or APIs protected by web application firewall. First question is, are they even behind the web? Are they behind the w at all? We're gonna find that the WAFF doesn't know if it's not protecting something. And then secondary, there are attack types of business logic in particular of like authentication policy that a WAFF is not gonna be able to see. So the WAFF and the API management plan, those are the key control points and we can help make those better. >>You know what I think is cool, Carl, as you're bringing up a point that we're seeing here and we've seen before, but now it's kind of coming at the visibility. And it was mentioned in the keynote by one of the presenters, Kurt, I think it was who runs the platform. This idea of reasoning is coming into security. So the idea of knowing the topology know that there's dynamic stuff going on. I mean, topes aren't static anymore. Yep. And now you have more microservices. Yep. More APIs being turned on and off this runtime is interesting. So you starting to see this holistic view of, Hey, the secret sauce is you gotta be smarter. Yep. And that's either machine learning or AI. So, so how does that relate to what you guys do? Does it, cuz it sounds like you've got something of that going on with the product. Is that fair or yeah. >>Yeah, absolutely. So we, yeah, we talked about posture, so that's, that's really the inherent quality or secure posture of a, of an API. And now let's talk about sending traffic through that API, the request and response. When we're talking about organizations that have more APIs than they have people, employees, or, or tens of thousands, we're seeing in some customers, the only way to identify anomalous traffic is through machine learning. So we apply a machine learning model to each and every API in independently for itself because we wanna learn how that API is supposed to be behave. Where is it supposed to be talking? What kind of data is it supposed to be trafficking in, in, in all its facets. So we can model that activity and then identify the anomaly where there's a misuse, there's an attacker event. There's an, an insider employee is doing something with that API that's different. And that's really key with APIs is, is that no, a no two APIs are alike. Yeah. They really do have to be modeled individually rather than I can't share my, my threat signatures for my API, with your organization, cuz your APIs are different. And so we have to have that machine learning approach in order to really identify that >>Anomaly and watch the credentials, permissions. Absolutely all those things. All right. Take me through the life cycle of an API. There's pre-production postproduction what do I need to know about those two, those two areas with respect to what you guys do? >>Sure. So the pre-production activities are really putting in the hands of a developer or an APSEC team. The ability to test that API during its development and, and source code testing is one piece, but also in pre-production are we modeling production variables enough to know what's gonna happen when I move it into production? So it's one thing to have secure source code, of course, but then it's also, do we know how that API's gonna interact with the world once it's sort of set free? So the testing capabilities early life cycle is really how we de-risk in the long term, but we all have API ecosystems that are existing. And so in production we're applying the, all of those same testing of posture and configuration issues in runtime, but really what it, it may sound cliche to say, we wanna shift security left, but in APIs that's, that's a hundred percent true. We want to keep moving our, our issue detection to the earliest possible point in the development of an API. And that gives us the greatest return in the API, which is what we're all looking for is to capitalize on it as an agent of transformation. >>All right, let's take the customer perspective. I'm the customer, Carl, Carl, why do I need you? And how are you different from the competition? And if I like it, how do I get started? >>Sure. So the, the, the first thing that we differentiate selves from the customer is, or from our competitors is really looking at the API as an entire life cycle of activities. So whether it's from the documentation and the design and the secure source code testing that we can provide, you know, pre-development, or pre-deployment through production posture, through runtime, the differentiator really for us is being a one-stop shop for an entire API security program. And that's very important. And as that one stop shop, the, the great thing about that when having a conversation with a customer is not every customer's at the same point in their journey. And so if, if a customer discussion really focuses on their perhaps lack of confidence in their code testing, maybe somebody else has a lack of confidence in their runtime detection. We can say yes to those conversations, deliver value, and then consider other things that we can do with that customer along a whole continuum of life cycle. And so it allows us to have a customer conversation where we don't need to say, no, we don't do that. If it's an API, the answer is, yes, we do do that. And that's really where we, you know, we have an advantage, I think, in, in looking at this space and, and, and being able to talk with pretty much any customer in any vertical and having a, having a solution that, that gives them something value right away. >>And how do I get started? I like it. You sold me on, on operationalizing it. I like the one stop shop. I, my APIs are super important. I know that could be potential exposure, maybe access, and then lateral movement to a workload, all kinds of stuff could happen. Sure. How do I get started? What do I do to solve >>This? Well, no name, security.com. Of course we, we have, you know, most customers do sandboxing POVs as part of a trial period for us, especially with, you know, being here at AWS is wonderful because these are customers who's with whom we can integrate with. In a matter of minutes, we're talking about literally updating an IAM role. Permission is the complexity of implementation because cloud friendly workloads really allow us to, to do proofs of concept and value in a matter of minutes to, to achieve that value. So whether it's a, a dedicated sandbox for one customer, whether it's a full blown POC for a complicated organization, you know, whether it's here at AWS conference or, or, or Nona security.com, we would love to do a, do a, like a free demo test drive and assessment. >>Awesome. And now you guys are part of the elite alumni of our startup showcase yep. Where we feature the hot startups, obviously it's the security focuses episodes about security. You guys have been recognized by the industry and AWS as, you know, making it, making it happen. What specifically is your relationship with AWS? Are you guys doing stuff together? Cuz they're, they're clearly integrating with their partners. Yeah. I mean, they're going to companies and saying, Hey, you know what, the more we're integrated, the better security everyone gets, what are you doing with Amazon? Can you share any tidbits? You don't have to share any confidential information, but can you give us a little taste of the relationship? >>Well, so I think we have the best case scenario with our relationship with AWSs is, is as a, as a very, very small company. Most of our first customers were AWS customers. And so to develop the, the, the initial integrations with AWS, what we were able to do is have our customers, oftentimes, which are large public corporations, go to AWS and say, we need, we need that company to be through your marketplace. We need you to be a partner. And so that partnership with, with AWS has really grown from, you know, gone from zero to 60 to, you know, miles per hour in a very short period of time. And now being part of the startup program, we have a variety of ways that a customer can, can work with us from a direct purchase through the APS marketplace, through channel partners and, and VA, we really have that footprint now in AWS because our customers are there and, and they brought our customers to AWS with us. >>It's it nice. The customers pulls you to AWS. Yes. Its pulls you more customers. Yep. You get kind of intermingled there, provide the value. And certainly they got, they, they hyperscale so >>Well, that creates depth of the relationship. So for example, as AWS itself is evolving and changing new services become available. We are a part of that inner circle. So to speak, to know that we can make sure that our technology is sort of calibrated in advance of that service offering, going out to the rest of the world. And so it's a really great vantage point to be in as a startup. >>Well, Carl, the CISO for no name security, you're here on the ground. You partner with AWS. What do you think of the show this year? What's the theme. What's the top story one or two stories that you think of the most important stories that people should know about happening here in the security world? >>Well, I don't think it's any surprise that almost every booth in the, in the exhibit hall has the words cloud native associated with it. But I also think that's, that's, that's the best thing about it, which is we're seeing companies and, and I think no name is, is a part of that trend who have designed capabilities and technologies to take advantage and lean into what the cloud has to offer rather than compensating. For example, five years ago, when we were all maybe wondering, will the cloud ever be as secure as my own data center, those days are over. And we now have companies that have built highly sophisticated capabilities here in the exhibit hall that are remarkably better improvements in, in securing the cloud applications in, in our environments. So it's a, it's a real win for the cloud. It's something of a victory lap. If, if you hadn't already been there, you should be there at this point. >>Yeah. And the structural change is happening now that's clear and I'd love to get your reaction if you agree with it, is that the ops on security teams are now being pulled up to the level that the developers are succeeding at, meaning that they have to be in the boat together. Yes. >>Oh, lines of, of reporting responsibility are becoming less and less meaningful and that's a good thing. So we're having just in many conversations with developers or API management center of excellence teams to cloud infrastructure teams as we are security teams. And that's a good thing because we're finally starting to have some degree of conversions around where our interests lie in securing cloud assets. >>So developers ops security all in the boat together, sync absolutely together or win together. >>We, we, we win together, but we don't win on day one. We have to practice like we as organizations we have to, we have to rethink our, we have to rethink our tech stack. Yeah. But we also have to, you have to rethink our organizational models, our processes to get there, to get >>That in, keep the straining boat in low waters. Carl, thanks for coming on. No name security. Why the name just curious, no name. I love that name. Cause the restaurant here in Boston that used to be of all the people that know that. No name security, why no name? >>Well, it was sort of accidental at, in the, in the company's first few weeks, the there's an advisory board of CISOs who provides feedback on, on seed to seed companies on their, on their concept of, of where they're gonna build platforms. And, and so in absence of a name, the founders and the original investor filled out a form, putting no name as the name of this company that was about to develop an API security solution. Well, amongst this board of CSOs, basically there was unanimous feedback that the, what they needed to do was keep the name. If nothing else, keep the name, no name, it's a brilliant name. And that was very much accidental, really just a circumstance of not having picked one, but you know, a few weeks passed and all of a sudden they were locked in because sort of by popular vote, no name was, >>Was formed. Yeah. And now the legacy, the origination story is now known here on the cube call. Thanks for coming on. Really appreciate it. Thank you, John. Okay. We're here. Live on the floor show floor of AWS reinforced in Boston, Massachusetts. I'm John with Dave ALO. Who's out and about getting the stories in the trenches in the analyst meeting. He'll be right back with me shortly day tuned for more cube coverage. After this short break.

(light music) >> Hello, welcome back everybody to theCUBE's coverage in New York City of AWS Summit 2022. I'm John Furrier, host of theCUBE. We had Dave Vellante, Lisa Martin here earlier. I'm going to wrap it up here with James Forrester, last interview of the day here in New York. Wish we would have had another day. It's a packed house, 10,000 people. James Forrester's the VP Worldwide Technical Leader for VMware's Cloud on AWS. On AWS is a big distinction. James, welcome to theCube. Thanks for coming on. >> Thank you so much, John. It's great to be here. >> So I think it's been like six years since the announcement of VMware's Cloud on AWS, which is a separate instance, separate hardware, but it's changed the game for VMware. You guys have done a lot of work, successful traction with customers. Clarified, I remember at that time, it really clarified VMware's Cloud play. Which then gave VMware more time to work on what it's doing now, which is, you know, using all their assets and their operations with Tanzu, Monterey, Cloud Native, Cross Cloud. What they call you guys call Cross Cloud, I call Super Cloud, action, a lot of stuff happening. So thanks for coming on. Okay. So first question is, what's the future look like for VMware's Cloud on AWS? >> Super bright, super bright. And there's a couple of great reasons for that. I think firstly, what we're seeing is that customers have now made enough progress in their cloud journeys. Many of them have chosen AWS and they're going full force. We're going to help them go faster. We're going to help them get there and get native to those adjacent services much quicker with more confidence and more resiliency. So it's a super exciting time to be doing what we do. >> You know, VMware has had a steady install base, okay. I mean basically it's like almost ingrained in the operations. What do you guys see as that next level step up function? Because you know, obviously Broadcom is buying VMware. Obviously that utility will be in place, but there's more. There's more there that customers can tap into. This is the promise of the cross-cloud. How do you talk about that when you got the AWS action? How does that all integrate? >> Yeah, absolutely. And of course, because so many customers are going to AWS on their own cloud journeys right now, what we get to have the conversation about is how they can get there more confidently. And so for customers who are just starting out, who are looking at their application portfolios, who have a ton of skilled IT professionals who they want to bring into that cloud journey, they can use the skills they already have. For those folks who are a little bit further along but they may be finding that refactoring their applications is more complex, more difficult that they anticipated, we give them a way of moving with confidence and with much less risk so they can do those cloud journeys that they anticipated. >> You know, James, I want to get your thoughts on what the state of the current situation is, vis-à-vis, your customers and your customers' appetite for AWS services. 'Cause one of the promises of the original deal was clarifying messaging but more importantly, customers can get the VMware Cloud and take advantage of the higher level services on AWS. What's the update there? What's the current state of the art? What's some of the patterns that you're seeing on the uptake of services and how they're working together? >> Yeah, it's a great call out. And honestly, one of the misconceptions that I address right out of the gate is that somehow going VMware Cloud takes you away from those services. It doesn't, it gets you closer to them. Full, direct, native access to all of those hundreds of great AWS services. So what we often find is that customers have their enterprise data, inside data workloads in their data centers. But what they want to do is get that up next to the AWS services that can use it, like Redshift and Athena and Glue. They can move those workloads right adjacent to those services to start using them right away. So it's a great way to look at the platform. >> So one of the observations that's pretty well understood right now by most people, I'd say 90%, if not more, not a hundred percent 'cause I've heard people like not get it, but it's pretty clear that the operating model for the the enterprise will be hybrid as a steady state. I don't think there's any debate on that unless you think there is. >> Do you feel the same- >> No debate. No debate. >> Okay. Hybrid's a steady state. What does that mean as clients start to think about edge and their data centers. 'Cause now the private cloud is back in the game. So I've heard people talk about private cloud, which we, I think we coined the term with Dave, Wikibon years ago, but it kind of went away because that was not the public cloud. So public cloud won, on premise didn't go away. We saw Amazon with Outpost. So now they're like, I can still have stuff on prem and run it in a cloud operations. So they're calling that private cloud, I think. So you're starting to hear the same things. What it means basically is that hybrid is winning. It's the standard. What does the hybrid environment look like from a VMware perspective as you guys look at that and have been building that out 'cause you have customers that are on premises. >> Yeah. >> Is it just to the cloud and back? Is it, is there any changes? Is there new connective tissue? Is there a glue layer? What's the operating model for VMware customers? >> Well, customers wanted those same benefits from public cloud agility, cost benefits, elasticity, innovation, sovereignty, sustainability, but they wanted to be able to do that everywhere. They wanted it in their data centers. They wanted it at the edge. And as you've pointed out public cloud delivered that for customers. AWS first out there delivering that for customers. Now with innovations like VMware Cloud and AWS outpost, we're able to bring that back into the data center. We're able to bring those same benefits of public cloud into the customers on-prem environment. And you're right. We see hybrid just rolling and rolling and being able to offer our solution across all of it. >> Yeah, we're big fans of VMware because theCube's 12 years old, we've been at every VMworld. Now they're calling it VMware Explorer, the events coming up. So the folks watching, plug for VMware Explorer, formerly VMworld, it's on the schedule. Content catalog just came out last week. It's looking pretty good. So put a plug out there. We'll be there with theCube, two sets. So you know, if you're going to VMworld, now Explorer go register, get up there. It's in San Francisco, always a great event. vSphere and vSAN, always great products. But you got Carbon Black, you got Security. So these things have all been working kind of pistons for VMware. Tanzu, I know Raghu and those guys are doing it. Craig McLuckie and team, they're working on that. You got Tanzu, you got Monterey. That's the new cloud native thing. How is that tracking vis-à-vis, the operating model of the the core engine, vSphere, vSAN and others. And then with the native services of Cloud. So you got AWS Cloud with VMware Cloud, vSphere, vSAN, Carbon Black, and Security. And then you got the Tanzu over here. How are those three things coming together? >> Well, the services that customers know and love first and foremost that they've been running the mission critical workloads on, vSphere, vSAN, NSX. What VMware cloud and AWS is, is a packaging together of those services. So customers don't have to configure it all themselves and do the heavy lifting. We manage and run it on their behalf. What we are adding to that most recently with Tanzu is now the ability to run containers within the same environment. 'Cause customers tell us they've got parts of their organization that are very much on vSphere VMs. Parts of their organization are moving to containers. We want be able to provide a single operating model, a single layer, a single way of managing all of that. No matter where it's deployed. >> You know, remember back in the day, when Raghu wasn't the CEO, Carl Eschenbach was there, Sanjay Poonen was there. Carl's now at Sequoia Capital, Raghu's a CEO. Sanjay's kind of looking for a next gig. I always said, why doesn't vSphere and NSX become that abstraction layer and commoditize the network so that white boxes and Dell and HP could all play in that layer? It just never happened yet. Is that something you guys talk about at all? Like, I mean in the, in the smokey room, in the execs, is that happening? What's the vision? >> Well, we always work backwards- from customers, right? (John laughing) And what customers are telling us is they want us to help them with that undifferentiated heavy lifting. So who knows where that could take us, but right now we're very focused on helping those customers move with confidence to the cloud. >> You didn't take the bait on that one. I appreciate that. (James laughing) Okay. So let's get some perspective. You're out with customers. What are the big things that you're seeing right now from your customers right now? 'Cause you look behind us here, 10,000 people at this event. This is not a no-show. This is not a throwaway event in, you know, somewhere in the corner of the world. This is New York City, only one summit. This is bigger than Snowflake Summit and that was packed. So from an event standpoint, this is pretty a big game statement here for AWS. These companies are not experiencing headwinds, they're changing. So what are your customers telling you around what they're looking at for the cloud native architecture? I mean obviously the digital transformation is continuing, obviously clouds here. And again, we were saying earlier, this is the first time in history that the cloud hyperscalers have been in market during a so-called downturn. So there's no other data. 2008, I wouldn't call 'em up and running. They were building, but AWS, Azure, others, these cloud players they're in market. And so you're starting to see kind of some data coming out saying, Hey, this thing's still working, the engine of innovation is cranking out and it's not slowing down the digital transformation. It might change the capital markets and valuations but it's not changing customers. That's what I'm hearing. Now, you probably would agree with that, right? >> James: I think that's exactly right. >> Okay. So let's stay with that. If you believe that, then it's like, okay, what are they doing? So what are customers doubling down on? What are some of the patterns you're seeing in the environment today that you could share with the audience? >> Yeah, so I think first and foremost is that steady transition to the cloud to deliver all of those benefits, agility, cost, elasticity, innovation, sovereignty, sustainability that hasn't gone away at all. In fact, it's only accelerated. With workloads like virtual desktops, which became so critical during COVID the need to be able to provide that kind of scalable elastic capacity has only increased. Now, coupled with that, most of these customers are already on a cloud journey. And while some folks may have had the luxury of letting that go a little bit more slowly, nowadays the urgency is pervasive across all of the industries that we get to talk to in New York. Everyone needs to go faster. Everybody's not seeing the progress that they expected that we think we can help them deliver. So the opportunity I think that's come out of COVID is more workloads, different use cases, disaster recovery, ransomware- >> Is that more of an awareness or reality or both? >> Both. Absolutely. >> Okay. So let me ask the next question. 'Cause this is a good conversation, I think. I agree a hundred percent. We're seeing the same exact thing. Now let's talk about how companies are thinking about the real opportunity that's emerged, which is refactoring the business model without actually changing the makeup of the organization per se, to take on new territories and potentially take over categories. >> James: Mm hmm. >> So I mean a data warehouse and a data cloud's kind of the same thing. Snowflake probably wouldn't like me saying that they're a data warehouse because they call themselves a data cloud, but it's kind of the same thing, just refactored on AWS. >> James: Yep. >> That's a super cloud. So that's an opportunity for everyone to do that in every vertical. How many customers are actually thinking that way and actually taking steps to pursue that, capture that opportunity? Or do you agree it's the opportunity? >> No, I think that that is an opportunity and I love that idea of super cloud in that what I think customers have started to realize, over the last couple of years in particular, is it's very difficult to take advantage of all of those great cloud services if your applications are still behind a whole lot of different layers of firewalls and so forth. So getting the application close to those services, in proximity to those services is that first step in modernization. Then it doesn't have to be a change the wings on the plane while it's flying conversation, which- >> John: Yeah. >> You know, is very risky for a lot of organizations. >> John: Exactly. >> It's a let's get the plane going a little bit faster. Let's get the plane going a little bit smoother, and let's get the plane to its destination with less risk. >> You know, James, that reminds me of the old school conversations of non disruptive operations. Remember those days? >> James: I do, yeah. >> Mostly around storage and, and servers. But that's what basically what you're saying. Transform while operating, right? >> James: Exactly. >> So this is, you can do both. You got to make time and it's a talent question too. I'd love to get your thoughts on how customers are thinking about who do you put on which task. 'Cause you want your A players on both areas. You don't want all your A players, what I hear, CSOs and CIOs telling me is that, I put all my A players on transformation, I got no one running the business. >> James: Mm hmm. >> So you got to kind of balance. That's a cultural team decision. >> It's a cultural team decision. It's also a skills marketplace decision. >> John: Yeah. >> And there's a practical reality to the skills that are available and how fast you can hire them. So a big part of the conversation that we have is when customers have existing skills sets, plug those into their transformation, plug those into their business outcomes. I like to use the phrase, "Let's make heroes out of IT" because they can be a much more critical player than they think they can be. Yeah, IT basically is not even around anymore. It's part of the organization. And then you have data science and data engineering coming in. So it's, you know, IT is not a department anymore, it's the company >> Exactly right. >> If you're kind of going down that road, yeah. >> Yeah. Alright, so final question. What's the biggest change you've seen and observed in your current year and a half? You know, we're coming out of COVID, knowing what was before, what sea change, what inflection point are we in now? How would you describe this current market? 'Cause again, we're kind of in a unique market. You know, you got crypto around the corner, people getting attracted to that, little bubbly obviously, reality of cloud and 2.0 or super cloud emerging. On premise is not going away. Edge exploding on the industrial side, especially with machine learning coming along. So this operating model is clearly in sight. What's the biggest observation you've noticed. >> I think it's the sense of urgency over the last couple of years in that most customers I talked to are no longer relaxed about the timing of delivering cloud capabilities to their organizations. Most customers are on sort of a transformation journey of their own and digital transformation and cloud transformation are absolutely fundamental to that. >> One more real quick follow up question if you don't mind, 'cause I appreciate your time. One of the things that's come up a lot in our conversations is the role of the ecosystem. Not only as a part of the business model but also validation of the enablement that cloud offers companies. You have an enabling platform, your ecosystem is well known. And so your customers are starting to develop ecosystems. So if the cloud model kind of trickles like downstream, ecosystem is kind of a proof of something. >> James: Mm hmm. >> What's your view of all this ecosystem discussion as we transform this next generation? >> Yeah, I think it touches on a couple of things. So obviously there is a technology ecosystem, which is evolving very rapidly in support of cloud and cloud transformation. But what's interesting, I think is the business ecosystem that's evolving around it. We're seeing our customers evolve their own businesses to assume that those cloud capabilities will be available to them. And if the cloud capabilities are not available to them in a timely fashion, then the ecosystem starts to have a domino effect. So the ecosystems are interdependent between business, and technology, and skills, and talent. And I think that's a great to be >> James Forrester, they're going to shut us down. The speakers are on, they're going to pull the plug. Thanks for being our last interview here in New York City and bringing us home. Really appreciate you taking the time to come on theCube. >> John, thanks so much. Great to be here, really enjoyed it. Okay. We are wrapping it up here in New York City. I'm John Ford with theCube, great day. For Lisa Martin, Dave Vellante, and the entire crew of theCube here on the ground. Live in person events are back. theCube hybrid, get online, check out our coverage there. The SiliconANGLE and thecube.net. I'm John Furrier signing off from New York City. See you next time. (light music)

>>the Cube presents H P E discovered 2022. Brought to you by H P E. >>Hi, buddy Dave Balon and Jon Ferrier Wrapping up the cubes. Coverage of day two, hp Discover 2022. We're live from Las Vegas. Vishal Lall is here. He's the senior vice president and general manager for HP ES Green Lake Cloud Services Solutions. Michelle, good to see you again. >>Likewise. David, good to see you. It was about a year ago that we met here. Or maybe nine months >>ago. That's right. Uh, September of last year. A new role >>for you. Is that right? I was starting that new role when I last met you. Yeah, but it's been nine months. Three quarters? What have you learned so far? I mean, it's been quite a right, right? I mean, when I was starting off, I had, you know, about three priorities we've executed on on all of them. So, I mean, if you remember back then they we talked about, you know, improving a cloud experience. We talked about data and analytics being a focus area and then building on the marketplace. I think you heard a lot of that over the last couple of days here. Right? So we've enhanced our cloud experience. We added a private cloud, which was the big announcement yesterday or day before yesterday that Antonio made so that's been I mean, we've been testing that with customers. Great feedback so far. Right? And we're super excited about that. And, uh, you know, uh, down there, the test drive section people are testing that. So we're getting really, really good feedback. Really good acceptance from customers on the data and Analytics side. We you know, we launched the S three connector. We also had the analytics platform. And then we launched data fabric as a service a couple of days ago, right, which is kind of like back into that hybrid world. And then on the marketplace side, we've added a tonne of partners going deep with them about 80 plus partners now different SVS. So again, I think, uh, great. I think we've accomplished a lot over the last three quarters or so lot more to be done. Though >>the marketplace is really interesting to us because it's a hallmark of cloud. You've got to have a market price. Talk about how that's evolving and what your vision is for market. Yes, >>you're exactly right. I mean, having a broad marketplace provides a full for the platform, right? It's a chicken and egg. You need both. You need a good platform on which a good marketplace can set, but the vice versa as well. And what we're doing two things there, Right? One Is we expanding coverage of the marketplace. So we're adding more SVS into the marketplace. But at the same time, we're adding more capabilities into the marketplace. So, for example, we just demoed earlier today quickly deploy capabilities, right? So we have an I S p in the marketplace, they're tested. They are, uh, the work with the solution. But now you can you can collect to deploy directly on our infrastructure over time, the lad, commerce capabilities, licencing capabilities, etcetera. But again, we are super excited about that capability because I think it's important from a customer perspective. >>I want to ask you about that, because that's again the marketplace will be the ultimate arbiter of value creation, ecosystem and marketplace. Go hand in hand. What's your vision for what a successful ecosystem looks like? What's your expectation now that Green Lake is up and running. I stay up and running, but like we've been following the announcement, it just gets better. It's up to the right. So we're anticipating an ecosystem surge. Yeah. What are you expecting? And what's your vision for? How the ecosystem is going to develop out? Yeah. I >>mean, I've been meeting with a lot of our partners over the last couple of days, and you're right, right? I mean, I think of them in three or four buckets right there. I s V s and the I S P is coming to two forms right there. Bigger solutions, right? I think of being Nutanix, right, Home wall, big, bigger solutions. And then they are smaller software packages. I think Mom would think about open source, right? So again, one of them is targeted to developers, the other to the I t. Tops. But that's kind of one bucket, right? I s P s, uh, the second is around the channel partners who take this to market and they're asking us, Hey, this is fantastic. Help us understand how we can help you take this to market. And I think the other bucket system indicators right. I met with a few today and they're all excited about. They're like, Hey, we have some tooling. We have the manage services capabilities. How can we take your cloud? Because they build great practise around extent around. Sorry. Aws around? Uh, sure. So they're like, how can we build a similar practise around Green Lake? So again, those are the big buckets. I would say. Yeah, >>that's a great answer. Great commentary. I want to just follow up on that real quick. You don't mind? So a couple things we're seeing observing I want to get your reaction to is with a i machine learning. And the promise of that vertical specialisation is creating unique opportunities on with these platforms. And the other one is the rise of the managed service provider because expertise are hard to come by. You want kubernetes? Good luck finding talent. So managed services seem to be exploding. How does that fit into the buckets? Or is it all three buckets or you guys enable that? How do you see that coming? And then the vertical piece? >>A really good question. What we're doing is through our software, we're trying to abstract a lot of the complexity of take communities, right? So we are actually off. We have actually automated a whole bunch of communities functionality in our software, and then we provide managed services around it with very little. I would say human labour associated with it is is software manage? But at the same time we are. What we are trying to do is make sure that we enable that same functionality to our partners. So a lot of it is software automation, but then they can wrap their services around it, and that way we can scale the business right. So again, our first principle is automated as much as we can to software right abstract complexity and then as needed, uh, at the Manus Services. >>So you get some functionality for HP to have it and then encourage the ecosystem to fill it in or replicated >>or replicated, right? I mean, I don't think it's either or it should be both right. We can provide many services or we should have our our partners provide manage services. That's how we scale the business. We are the end of the day. We are product and product company, right, and it can manifest itself and services. That discussion was consumed, but it's still I p based. So >>let's quantify, you know, some of that momentum. I think the last time you call your over $800 million now in a are are you gotta You're growing at triple digits. Uh, you got a big backlog. Forget the exact number. Uh, give us a I >>mean, the momentum is fantastic Day. Right. So we have about $7 billion in total contract value, Right? Significant. We have 1600 customers now. Unique customers are running Green Lake. We have, um, your triple dip growth year over year. So the last quarter, we had 100% growth year over year. So again, fantastic momentum. I mean, the other couple, like one other metric I would like to talk about is the, um the stickiness factor associated tension in our retention, right? As renewal's is running in, like, high nineties, right? So if you think about it, that's a reflection of the value proposition of, like, >>that's that's kind of on a unit basis, if you will. That's the number >>on the revenue basis on >>revenue basis. Okay? >>And the 1600 customers. He's talking about the size and actually big numbers. Must be large companies that are. They're >>both right. So I'll give you some examples, right? So I mean, there are large companies. They come from different industries. Different geography is we're seeing, like, the momentum across every single geo, every single industry. I mean, just to take some examples. BMW, for example. Uh, I mean, they're running the entire electrical electric car fleet data collection on data fabric on Green Lake, right? Texas Children's Health on the on the healthcare side. Right On the public sector side, I was with with Carl Hunt yesterday. He's the CEO of County of Essex, New Jersey. So they are running the entire operations on Green Lake. So just if you look at it, Barclays the financial sector, right? I mean, they're running 100,000 workloads of three legs. So if you just look at the scale large companies, small companies, public sector in India, we have Steel Authority of India, which is the largest steel producer there. So, you know, we're seeing it across multiple industries. Multiple geography is great. Great uptake. >>Yeah. We were talking yesterday on our wrap up kind of dissecting through the news. I want to ask you the question that we were riffing on and see if we can get some clarity on it. If I'm a customer, CI or C so or buyer HP have been working with you or your team for for years. What's the value proposition? Finish this sentence. I work with HPV because blank because green like, brings new value proposition. What is that? Fill in that blank for >>me. So I mean, as we, uh, talked with us speaking with customers, customers are looking at alternatives at all times, right? Sometimes there's other providers on premises, sometimes as public cloud. And, uh, as we look at it, uh, I mean, we have value propositions across both. Right. So from a public cloud perspective, some of the challenges that our customers cr around latency around, uh, post predictability, right? That variability cost is really kind of like a challenge. It's around compliance, right? Uh, things of that nature is not open systems, right? I mean, sometimes, you know, they feel locked into a cloud provider, especially when they're using proprietary services. So those are some of the things that we have solved for them as compared to kind of like, you know, the other on premises vendors. I would say the marketplace that we spoke about earlier is huge differentiator. We have this huge marketplace. Now that's developing. Uh, we have high levels of automation that we have built, right, which is, uh, you know, which tells you about the TCO that we can drive for the customers. What? The other thing that is really cool that be introduced in the public in the private cloud is fungible itty across infrastructure. Right? So basically on the same infrastructure you can run. Um, virtual machines, containers, bare metals, any application he wants, you can decommission and commission the infrastructure on the fly. So what it does, is it no matter where it is? Uh, on premises, right? Yeah, earlier. I mean, if you think about it, the infrastructure was dedicated for a certain application. Now we're basically we have basically made it compose herbal, right? And that way, what? Really? Uh, that doesnt increases utilisation so you can get increased utilisation. High automation. What drives lower tco. So you've got a >>horizontal basically platform now that handle a variety of work and >>and these were close. Can sit anywhere to your point, right? I mean, we could have a four node workload out in a manufacturing setting multiple racks in a data centre, and it's all run by the same cloud prints, same software train. So it's really extensive. >>And you can call on the resources that you need for that particular workload. >>Exactly what you need them exactly. Right. >>Excellent. Give you the last word kind of takeaways from Discover. And where when we talk, when we sit down and talk next year, it's about where do you want to be? >>I mean, you know, I think, as you probably saw from discovered, this is, like, very different. Antonio did a live demo of our product, right? Uh, visual school, right? I mean, we haven't done that in a while, so I mean, you started. It >>didn't die like Bill Gates and demos. No, >>no, no, no. I think, uh, so I think you'll see more of that from us. I mean, I'm focused on three things, right? I'm focused on the cloud experience we spoke about. So what we are doing now is making sure that we increase the time for that, uh, make it very, you know, um, attractive to different industries to certifications like HIPAA, etcetera. So that's kind of one focus. So I just drive harder at that adoption of that of the private out, right across different industries and different customer segments. The second is more on the data and analytics I spoke about. You will have more and more analytic capabilities that you'll see, um, building upon data fabric as a service. And this is a marketplace. So that's like it's very specific is the three focus areas were driving hard. All right, we'll be watching >>number two. Instrumentation is really keen >>in the marketplace to I mean, you mentioned Mongo. Some other data platforms that we're going to see here. That's going to be, I think. Critical for Monetisation on the on on Green Lake. Absolutely. Uh, Michelle, thanks so much for coming back in the Cube. >>Thank you. Thanks for coming. All >>right, keep it right. There will be John, and I'll be back up to wrap up the day with a couple of heavies from I d. C. You're watching the cube. Mhm. Mm mm. Mhm.

(calm music) >> Welcome to theCUBE's live coverage of Snowflake Summit '22 from Las Vegas, Caesars Forum. Lisa Martin here with Dave Vellante, we're going to unpack some really cool stuff next, in the next 10 minutes with you, Carl Perry joins us, the Director of Product Management at Snowflake, he's here to talk about Snowflake's new Unistore workloads, how it's driving the next phase of innovation, welcome to the program. >> Oh, thank you so much for having me, this is awesome. >> There's a ton of momentum here, I saw the the numbers from fiscal 23Q1, product revenue 394 million, 85% growth, a lot of customers here, the customer growth is incredible as well, talk to us about Unistore, what is it? Unpack it and how have the customers been influential in it's development? >> Yeah, so Unistore is a way for customers to take their transactional workloads, for their enterprise applications and now have them run on or be built on top of Snowflake and now, you have your transactional data, along with all of your historical data, so now you have a single unified platform for doing anything you need to do with your data, whether it's transactional, single row look-ups, we can do that, whether it's the analytical data across again, transactional and historical data in a single query, our customers are super excited about this. >> So, what are Hybrid Tables? Is that just an extension of external tables? >> Yeah, that's a great question. So, Hybrid Tables are a new table-type that we've added to Snowflake and Hybrid Tables are really kind of just like another table with a couple of key differences, so number one is that Hybrid Tables provide fast, fine-grain read and write operations, so when you do something like a select star from customers where customer ID=832, that's going to return extremely fast, but on top of that same data, your transactional data, you can actually perform amazing analytical queries that return extremely fast and that's what Hybrid Tables at their core are. >> So, what does this mean for, so you're bringing that world of transaction and analytics together, what does it mean for customers? Walk us through Carl, an example of- >> Yeah, so it's great, so Adobe is a customer that is looking at using and leveraging Hybrid Tables today, and then more broadly Unistore, and frankly, Adobe has been an amazing customer since they started their journey, just really quickly, they're in phase three, the first phase was customers had data in Snowflake that they wanted to take advantage of with the Adobe Campaign Platform and so what they did is they built a connector basically into and being able to access customer data, and then they started to look at, "Well, this thing's working really well, let's try to leverage Snowflake for all our analytical needs." And so that was kind of phase two, and now phase three is like, look let's go and reimagine what we can do with the Adobe Campaign Platform by having both the transactional and analytical data in the same platform, so that they can really enable their customers to do personalization, ad campaign management, understanding the ethicacy of those things at a scale that they haven't been able to do before. >> Prior to this capability, they would what? Have to go outside of the Snowflake Data Cloud? And do something else? And then come back in? >> Exactly, right? So, they'd have a transactional system where all of the transactional state for what the customer was doing inside Adobe Campaign, setting up all their campaigns and everything, and that would be stored inside a database, right? And then they would need to ensure that, that data was moved over to Snowflake for further analytical purposes, right? You know you imagine the complexity that our customers have to manage every single day, a separate transactional system, an ETL pipeline to keep that data flowing and then Snowflake, right? And with Unistore, we really believe that customers will be able to remove that complexity from their lives and have that single platform that really makes their lives easier. >> I mean, they'll still have a transactional system, will they not? Or do you see a day where they sort of sunset that? >> I mean, there's a set of workloads that are not going to be the best choice today for Unistore and Hybrid Tables, right? And so we know that customers will continue to have their own transactional systems, right? And there's lots of transactional systems that customers rely and have entire applications, and systems built around, right? Right now with Hybrid Tables and Unistore, customers can take those enterprise applications, not consumer-facing applications and move them over to leverage Snowflake, and then really think about re-imagining how they can use their data that's both realtime transactional, as well as all the historical data without the need to move things between systems or use a ton of different services. >> The Adobe example that you just gave seems like, I loved how you described the phases they're in, they're discovering, it's like peeling the onion and just discovering more, and more, but what it sounds like is that Snowflake has enabled Adobe to transform part of it's business, how is Unistore positioned to be so transformational for your customers? >> Well, I mean I think there's a couple of things, so one, they have this like level of complexity today for a set of applications that they can completely stop worrying about, right? No need to maintain that separate transactional system for that again, enterprise application, no need to maintain that ETL pipeline, that's kind of like one step, the next step is, I mean all your data's in Snowflake, so you can start leveraging that data for insight and action immediately, there's no delay in being able to take advantage of that data, right? And then number three, which I think is the most compelling part is because it's part of Snowflake, you getting the benefit of Snowflake's entire ecosystem, whether it's first party capabilities like easy to manage and enforce really powerful governance, and security policies, right? Being able to take data from the market place and actually join it with my realtime transactional data, this is game-changing and then most importantly is the third-party ecosystem of partners who are building all these incredible solutions on top of Snowflake, I can't even begin to imagine what they're going to do with Hybrid Tables in Unistore. >> So, Carl I have to ask you, so I talked to a lot of customers and I talked to a lot of technology companies, explain, so Snowflake obviously was the first to separate compute from storage and you know the cloud, cloud database and then tons of investment came into that space, kind of follow you on, so that's cool, you reached escape velocity, awesome, but a lot of the companies that I talked to are saying, "We're converging transaction and analytics," I think (speaking softly) calls it HTAP or something, they came up with a name, explain the difference between what you're doing and what everybody else is doing, and why, what customer benefits you're delivering? >> Yeah, so I mean I think that's a really great question and to use the term you used HTAP, right? It's a industry understood term, really when people think about HTAP, what that is about is taking your transactional data that you have and enabling you to do fast analytical capabilities on that, and that's great, but there are a couple of problems that historical HTAP solutions have suffered from, so number one, that acceleration, that colander format of data is all in memory, so you're bound by the total amount of memory that you can use to accelerate the queries that you want to, so that's kind of problem one, this is not the approach that Snowflake is taking, most importantly, it's not just about accelerating queries on transactional data, whether it's a single-row lookup or a complex aggregate, it's about being able to leverage that data within the data cloud, right? I don't want to have a separate dataset on a transactional system or an HTAP system that can give me great analytics on transactional data and then I can't use it with all the other data that I have, it's truly about enabling the transformation with the data cloud and completely taking away silos, so that your data, whether it's realtime, whether it's historical, can be treated as a single dataset, this is the key thing that is different about Unistore, you can take the power of the data cloud, all of it, all of the partners, all the solutions and all the capabilities we continue to add, and leverage your data in ways that nobody's thought of possible before. >> Governance is a huge, huge component of that, right? So, in the press release, you have this statement, "As part of the Unistore Snowflake is introducing Hybrid Tables," you explained that, "Which offer fast, single-row operations and allow customers to build transactional business applications directly on Snowflake"- >> Yep. >> That's a little interesting tidbit, so you expect customers are going to build transactional applications inside the data cloud? And somewhat minimize the work that is going to be required by their existing transactional databases, correct? >> Exactly and I think, so let me say a couple things on this, right? So, first of all, there's a class of applications that will be able to just build on top of Hybrid Tables and run on Snowflake directly, for their transactional needs, I think what's super interesting here though is when you again start to talk about all your data, one example that we're going to walk through tomorrow in our talk is being able to do a transaction that updates data in a Hybrid Table and then updates data in a Standard Snowflake Table, and then either being able to atomically commit, or rollback that transaction, this is a transaction that's spanning multiple different table types inside Snowflake and you'll have consistency of either the rollback or the commit, this type of functionality doesn't exist elsewhere and being able to take, and build transactional applications with these capabilities, we think is transformative- >> And that's all going to happen inside the Snowflake Data Cloud, with all the capabilities and it's not like you know what you're doing with Dell and Pure, it's nice, but it's read-only, you can't you know add and delete, and do all that stuff, this is Native? First class citizen inside the database? >> Yep, just like other table types, you'll be able to take on and leverage the power of the data cloud as a normal table that you'd be able to use elsewhere. >> Got to ask you, your energy in the way that you're talking about this is fantastic, the transformation that it's going to be, how central it is to the product innovations that Snowflake is coming out with, what's been the feedback from customers? As there's so many thousands of folks here today, the keynote was standing in your room only, there was an overflow, what are you hearing on the floor here? >> Well, I mean, I think it was funny in the talk when I announced that primary keys are going to be required and enforced, and we got a standing ovation, I was like, "Wow, I didn't expect people to be so excited about primary key enforcement." I mean, what's been amazing both about the private preview and the feedback we're getting there, and then some of the early feedback we're getting from customers is that they want to understand and they're really thinking about like, "Wait, I can use Snowflake for all of this now?" And honestly I think that people are kind of like, "But wait, what would I do if I could have those applications running on Snowflake and not have to worry about multiple systems? Wait, I can combine it with all my historical data and anything that's in the data cloud, like what can I do?" Is the question they're asking and I think that this is the most fascinating thing, customers are going to build things they haven't been able to build before and I'm super excited to see what they do. >> But more specifically, my takeaway is that customers, actually application builders are going to be able to build applications that have data inherent to those apps, I mean John Furrier years ago said, "You know data is the new development kit." And it never happened the data, the data stack if you will separate from the application development stack, you're bringing those two worlds together, so what do you think the implications are of that? >> Well, I mean I think that we're going to dramatically simplify our customers lives, right? A thing that we focus on at Snowflake is relentless customer innovation, so we can make their lives better, so I mean frankly we talk to customers like, "Wait, I can do all this? Wait, are you sure that I'll be able to do this?" And we walk through what we can do, and what we can't do, and they really are like, "Wow, this could just dramatically simplify our lives and wait, what could we do with our data here?" And so, I think with the announcement of Unistore, and also all the Native app stuff that we're announcing today, I think we're really trying to enable customers and app developers there to think about, and being able to leverage Snowflake as their transactional system, the system of source, so I mean, I'm super excited about this, I came to Snowflake to work on this and I'm like, "Can't believe we get to talk about it." >> How do you, how, how? How does this work? What's the secret sauce behind it? Is it architecture or is it? >> Yeah, so I mean I think a big part of it is the architecture that we chose, so you know number one, a key product philosophy that we have at Snowflake is we have one product, we don't have many, we don't put the onus of complexity onto our customers and so building that into Snowflake is actually really hard, so underlying Hybrid Tables, which is the feature that powers Unistore is a row storage engine, a row-based storage engine, right? And then data is asynchronously copied over into a colander format and what this provides, because it's just another table that's deeply integrated with Snowflake is the compiler's completely aware of this, so you can write a query that spans multiple tables and take advantage of it, and we'll take over all the complexity, whether it needs to be a fast response to a single-row lookup, or it needs to aggregate and scan a ton of data, we'll make sure that we choose the right thing and provide you with the best performance that we have- >> You built that intelligence inside of that? >> Completely built in and amazing, but provided in a very simple fashion. >> You said you came to Snowflake to do this? How long ago was that? >> I came here a little over a year and a half. >> Okay, and had they started working on this obviously beforehand, or at least envisioning it, right? >> Yeah, this I mean, this is absolutely incredible, I have been working on this now for a year and a half, some of the team members have been working on it for more and it's incredible to finally be able to talk to customers and everybody about it, and for them to tell us what they're trying to do. I've already talked to a bunch of customers like, "Well wait, I could do this, or this, what about this scenario?" And it's awesome to hear their requirements, right? The thing that's been most amazing and you'll hear it in the talk tomorrow with Adobe who's been a great customer is like, "Customers give us insanely hard requirements." And what I love about this company is not, "Well, you know it's easier to do it this way." It's like, "No, how can we actually make their life easier?" And so, we really focus on doing that with Snowflake. >> And that's one of the things Frank talked about this morning with that mission alignment being critical there. So, it's in private preview now, when can folks expect to get their hands on it? >> Well, we don't have a date right now we're talking about, but you can go signup to be notified of the public preview when we get there, I think it's like snowflake.com/try-unistore, but we'll publish that later and you know if you're interested in the private preview, talk to your account team and we'll see if we can get you in. >> Carl, thank you so much for joining Dave and me in an action-packed 15 minutes, talking about the power of Unistore, what it's going to enable organizations to do and it sounds like you're tapping the surface, there's just so much more innovation that's to come, you're going to have to come back. >> Yes, that sounds awesome, thank you so much. >> Our pleasure. For Carl and Dave Vellante, I'm Lisa Martin, you're watching theCUBE's live coverage of Snowflake Summit '22 from the show floor in Las Vegas, we're going to be right back with our next guest. (calm music)

>>The cube presents, Dell technologies world brought to you by Dell. >>Hello. Welcome to the cube here at Dell tech world. I'm John furry host of the cube with Dave Alon here with Michael Dell, the CEO of Dell technologies cube alumni comes on every year. We have the cube here. It's been two years. Michael, welcome to the cube. Get to see you. >>Hey, John, Dave, great to be with you guys. Thanks for being here. Wonderful to be back here in Vegas with >>You. Well, great to be in person two years ago, we had the cue with the pandemic a lot's happened. We were talking end to end solutions here at Dell tech world in person two years ago, pandemic hits. Thank God you had all that supply for the, for the people having the remote remote end to work now back in person. What's it look like now with, with Dell tech end to end, the edge is important. What's the story, >>You know, edge is, is the physical world. And if you, if you step back from clouds and, you know, multi-cloud, you sort of think about what is the purpose of a cloud or a data center? Well, it's to take data out of the physical world and move it to this place, to somehow enhance it or do something with it and create business value and hopefully create better outcomes. Well, it turns out that, you know, increasingly a lot of that data is gonna stay in the physical world and all of those nodes are gonna be connected. They're gonna be intelligent and we're seeing it in manufacturing and retail and healthcare, transportation, logistics. We're seeing this rapidly intelligent edge being formed. And then of course, with the new networks, the 5g we're seeing, you know, all, all this develop. And so here on the show floor, we're showing a lot of those solutions, but our customers are, are highly engaged. And certainly we think that's a, a big, a big growth factor for the next decade. >>And it's been ING to watch the transformation of the it world and cloudification and the as service, uh, consumption model, which you guys are putting out there has been very successful, but cloud operations is more prominent now on premises and edge and cloud. So the combination of cloud on-premise and edge hardware matters more now than ever before Silicon advances, um, abstraction layers from modern cloud native applications are what people are focused on. What's the story that you cite to the CIOs saying, we're here to help you with that new architecture cloud multi-cloud on premise and edge. What's the main story for you guys with the customers? >>Well, you know, customers want to go faster, right? And they want to accelerate their transformation. And so they wanna shift more resources over to developers, to applications, to access their data, to create competitive advantage. And so we talk a lot about the value line and what are those things below the value line, where we can provide that as a service on a consumption based model and accelerate their transformation, kind of, you know, do for them what we've done inside our own business. And, you know, it's absolutely resonating. We're seeing great growth there. People continue to, to need the solutions, but as we can automate the management and deployment of infrastructure and make it super easy, it gives them a lot of cycles back. >>You know, Michael, my, the favorite part, my favorite part of your book was you were in, I think you were in his, in his home court, in his dining room at Carl Icahn's house. And you said, well, why don't you just buy the company? And then you'll do what you're doing. I I'll buy it back for cheaper. Now, thankfully, you didn't have to do that. Cuz you had an environment of low interest rates and you obviously took it into the other direction, added tremendous value, 101 billion in revenue last year, 17% revenue growth, which was out astounding. When you think about that, um, now we're entering a new chapter with VMware untethered of course you're the chairman of both companies. So how should we think about the new Dell what's next? >>Well, so look, we, we have some unbelievable core businesses, right? We have our client system business and we've all learned during these last two years, how incredibly important it is to enable and empower your workforce with the right tools in the remote and high hybrid work. And we're showing off all kinds of new innovations here. That's a huge business force continues to grow, continues to be super important. Then we have our ISG, the cloud data center, the network of the future, the edge, you know, the, the sort of epicenter of where we're embracing, consumption based business models. That's absolutely huge. Then we have these new, new businesses that we're building with telco with edge, put it all together. It's a 1.3 trillion Tam that we operate in, as you said, more than a hundred billion dollars last year. So there's plenty of room for us to continue to grow and, and expand. And you know, as we make this shift to outcomes, it's obviously more valuable for customers and that, you know, increases our opportunity, increases the, the value we can create for all our stakeholders. >>And number one, number one, share in PCs, by the way, congratulations, again, hit that milestone. All of our gamer, uh, fans in our discord want to know what's the hottest chips coming. What's the fastest machines. What, how's the monitors coming? They want faster, cheaper. What's the coolest, uh, monitors out there right now and, and machines. >>Well, uh, you know, what what's, what's amazing is the, the pace of innovation continues to improve. So whether it's in the GPU, the CPU, the, the resolution, I I'm pretty partial to our 41, uh, display 11 million pixels of fun. And look, I mean, we, we it's, it's, it's clear that people are more productive when they have large screens and all the performance is enabling photo realistic, uh, you know, uh, gaming and photo realistic, everything. And these are immersive experiences. And, you know, again, uh, what companies have figured out to bring it back to, to, to a little bit of business here, John, is that when you, uh, give people the right tools, they're more productive, they're more engaged and look, people are smart. They know what tools are available. And, you know, uh, the thing that actually is most representative of how a person thinks about the tools they have at their organization is actually the thing that's right in front of 'em. And so, you know, this ability for us to provide a pool set of solutions for organizations to keep their workforce productive, to run their applications and infrastructure securely anywhere they want. That's, that's a winning proposition. >>Michael trust was a big theme of your keynote yesterday. And when you acquired EMC and got VMware, it really changed the dynamic with regard to your ability to, into new parts of organizations. You became a much more strategic supplier. I, I would argue. And now with VMware as a separate company, do you feel like you have built up over the, you know, five or whatever years that muscle memory you kinda earn that trust. So how do you see the customer relationship with that regard to that integration that they, they loved the eco. So system competitors might not have loved it so much, but the customers really did love. In fact, the, the U S a, a gentleman yesterday kind of mentioned that, how do you see it? >>You know, customers, uh, are not as interested in the balance sheet and what you know, where different holdings are, what they, they want things to work together, right? And they want partnerships in ecosystems. And certainly, you know, with VMware, even before the combination, we had a powerful partnership. It obviously solidified in a super special way. And now we have this first and best relationship and I've remained the chairman of VMware and super excited about their future. But our ecosystem is incredibly broad. And you see that here in this show floor, and again, making things work together better and more effectively building these engineered solutions that allow people to very quickly deploy the kind of capabilities they want, whether it's, you know, snowflake now working with the on premise and the edge data and more of these, you know, multi-cloud, uh, eco of systems that are being built. It's not gonna be just one company >>You called the edge a couple years ago. You're really prominent in your, in your speeches. And your keynotes data also is a big theme. You mentioned data now, data engineering seems to be the hottest track of, of, of students graduating with data engineering skills, not data science, data engineering, large scale data as code concepts. So what's your vision now with data, how's that fitting into the solutions and the role of data, obviously data protection with cybersecurity data as code is becoming really part of that next big thing. >>Yeah. I mean, if, if you look at anything that is interesting in the world today, uh, at the center of it is data, right? Whether it's the blockchain or the defi or the AI drug discovery, or the autonomous vehicles or whatever you wanna do, there's data in, in, in the middle of that. And of course with that data, well, you've gotta manage it. You, you need compute engines, right? You need to be able to protect it, secure it. And, you know, that's kind of what we do, and we're not going to create all those solutions, but we are gonna be an enabling layer to allow that data to be accessed no matter, you know, where, where it is. And, and, and of course, you know, leading in storage continues to be a super important part of our business. Number one, larger than number two than number three, number four, combined, and, and most of number five as well, and, and growing share. And, and you saw today, the software defined innovations, allowing that, you know, data layer to exist across the edge, the colos, the OnPrem, and the public clouds >>Throughout a stat yesterday. I can't remember if it was a keynote of the analyst round table, but it was 9 million cell towers. And if I heard, right, you kinda look at those as potential data centers talk about that's >>Right. It it's actually 7 million, but, but probably will be 9 million and not, not too long, I don't have the update, but so yeah, the public clouds all together is about 600 data centers. They're about 7 million cellular base stations in the world. Every single one of those is becoming a, you know, multi access, edge compute node. And what are they putting in there? They're putting many data centers of compute and GPS and storage. And, you know, 5g is not about, uh, connecting people that was 4g and before 5g is about connecting things. And there are way more things than there are people, right? And, uh, you know, this, this, this edge is, is rapidly developing. You'll also have private 5g and you'll have, you know, again, embedded intelligence I believe is gonna be in everything this next decade is going to be about that intelligent, connected future, taking that data, turning it into useful outsides in insights and outcomes. And, you know, lots of new businesses will be existing. Businesses will be transformed and also disrupted. >>Yeah. I mean, I think that's so right on and not to pat ourselves on the back day, but we called that edge distributed computing a couple years ago on the cube. And that's, what's turning into the home with COVID you saw that become a workplace, basically compute center, these compute nodes, tying it together as we, what everyone's talking about right now. So as customers say, okay, I want to keep my operations steady, steady, and secure. How do I glue it together? How do I bring these compute node together? That seems to be the top question on, on top of people's minds. And they want it to be cloud native, which means they want it to run cloud-like and they want to connect these compute node together. That's a big discussion point. What's your view on, >>Well, you know, if you, if you sort of have a, a cloud here, a cloud there cloud everywhere, and you, you know, have lots of different Kubernetes frameworks, uh, and you've got, you know, everything is, is spread out, it's a disaster, right? And, and, and it's, it's a, it's a, it's a real challenge to manage all that. So what people are trying to do is create ruthless standardization. It's like, how do you drive cost out and get speed? It's ruthless standardization create consistent environments where you can operate the across all the different domains that, that you want. And so, uh, you know, this is what we're bringing together in, in, in the capabilities that we're delivering. >>And that chaos is great opportunity for you. Um, how are you feeling about VMware these days, new team, uh, give us the update there. >>Yeah. The team is doing well. You know, I think the tons message is resonating. You know, people want Kubernetes and, and, and container based apps, for sure. That's the main, you know, growth in, in, in, in, in new, in new workloads. Uh, but they also want it to work with what they have. Yeah. And they don't want it to be locked into one particular infrastructure. So software finding everything, making it run in all the public clouds, you know, we've had a great success with VxRail, you know, that, that absolutely continues. We have, uh, 200,000 plus nodes, 15,000 customers and growing, we have edge satellite nodes and we continue to work together in SD wan in software defined networking in VMware cloud foundation, uh, you know, expressed, uh, in, in, in all locations. >>You know, one of the things that we've been seeing with the trend towards, um, future of work, which is a big theme, here is a lot of managed services are popping up where the complexity is so ha high that customers want to manage services. Uh, and also the workforce of it's kind of changing. You got a younger generation coming in, how do you see that future of the workforce? The next level? It's not gonna be like, yesterday's it, it's gonna be distributed computing dashboard based. And then you've got these managed services, you know, need to have the training and expertise maybe to run something at scale. How do, how do you see that connecting? Cuz that seems to be another big trend people are talking about, Hey, it's complex someone manage it for me. And I want ease of views. I want the easy button in it. >>Yeah. Well we we've all been at this a while. So we can remember, you know, the beginnings of converged infrastructure and then hyperconverged, which wasn't that long go. And now we have consumption based business models. These are all along the trajectory of the easy button that you're talking about and customers really thinking about the value line, where are the things that really differentiate and add value for their business. And it's not below the value line in those infrastructure areas are creating that easy button with appliances, with consumption based models and allowing them to deploy the scarce resources. They have to the things that really drive their unique differe. And you know, if you look at our managed services flex on demand, all the sort of ancestors and predecessors of apex, those have been great businesses for us. And now with apex, we're kind of industrializing this and, and making it, you know, at scale for all >>Customers, you know, the three of us, we go back, we, we, our first interactions with you separately, we're in the nine. And then we reconnected in the 2012. I think it was Tarkin Mayer had a little breakout session with CIOs. You brought us to early on a Dell tech world in Austin. And of course it was, >>It was just Dell world. Then Dell >>Four, we had Dell tech, you and then EMC world in 2010 was our first cube. And now that's all come together here in Las Vegas. So, you know, it's been great. Uh, the three of us come together and so really appreciate that. Yeah. >>Awesome. Absolutely awesome. >>Well, you know, really appreciate you guys being here, the wonderful work you do in thank you in, you know, bringing out the, the, the stories and, and showing off and helping us show off the innovations that, you know, our team has been working on. You know, during the past year >>It's been great in conversations and, and on a personal note, it's been great to have, uh, chat with all the top people and your company. Appreciate it. Um, someone told me to ask you this question, I want to ask you, you, we've all seen waves of innovation cycles up and down. We're kind of on one. Now you're seeing an inflection point, this next gen, uh, computing and, and web three cultural shit F with workforces and distributed computing decentralization. You mentioned that DFI earlier, how do you see this wave coming? Cause we've seen cycles come and go.com. Bubble kind of looks the same as the web three NFTs and stuff. Now it seems to be Look different, but how do you see this next wave? Cuz looking back on all the other ones that you you have lived through and you rode >>Well. So, you know, the, the way I see it is is, uh, to some extent, these are like foundational layers that have to be built for the next phase to occur. And if you look at the sort of new companies that are being founded today, and we see a lot of those, you, you, you, you see'em, we invest in a bunch of 'em, you know, they're, they're not going and, and kind of redoing the old foundational layers, they're going deeply into vertical businesses and, and disrupting and adding value on top of those. And I think that's, that's really the, the point of, of technology, right? It's enabling human progress us in, in all fields, it's making us healthier. It's making us safer. It's making us more successful in everything that, that we as humans do. And so all these layers of technology are enabling further progress and I think it's absolutely gonna continue. It's all been super exciting. Yeah. You know, so far for the first several decades, but as I, as I believe it, it's, it's just a pre-game show. >>And it's clear your strategy is, is, is really building that foundation of a layer, hardening it, but making it flexible enough, anybody read your book, you're a technology, visionary. A lot of people put you in a, you know, finance bucket, but you can, you can see that you can connect the dots. And that's what you're doing with your foundation of layers. You that's where you're making the bets, isn't it? Uh, you don't can't predict the future. You've said that many times, but you can sort of see where it's going and be prepared for >>It. Well, you, you, you know, you think about any company in, in the industry or any public sector organization, right? Uh, they're, they're, they're wanting to evolve more quickly and transform more quick, more quickly. Right. And we can give them an infrastructure or set of tools, a set of capabilities to help them go faster. >>Yeah. And the other one thing in the eighties, when you started Dell and we were in college, there was no open source really then if look at the growth of open source, talk about those layers, open source, better Silicon GPS, faster, cheap >>More now and now we even have, uh, open source instruction sets for processors. So I mean the whole world's changing. It's exciting. You have people around the world working together. I mean, when you see our development teams, uh, whether they're in Israel or Ireland or Bangalore or Singapore, Hopton Austin, Silicon valley, you know, Taiwan, they're, they're all, they're all collaborating together and, you know, driving, driving innovation and, and, and our business is not that dissimilar from our customers >>Like great to have you in the queue. Great. To have a physical event. People are excited. I'm talking to people, Hey, haven't been back in Vegas in two years. Thanks for having this event. Great to see you. Thanks for coming on the cube. >>Absolutely. Thank you guys. >>Michael Dell here in the cube CEO of Dell technologies. I'm John far, Dave Volante. We'll be right back, more live coverage here at Dell tech world.

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

(bright music) >> In the 2010s, organizations became keenly aware that data would become the key ingredient to driving competitive advantage, differentiation, and growth. But to this day, putting data to work remains a difficult challenge for many, if not most organizations. Now, as the cloud matures, it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible. We've also seen better tooling in the form of data workflows, streaming, machine intelligence, AI, developer tools, security, observability, automation, new databases and the like. These innovations they accelerate data proficiency, but at the same time, they add complexity for practitioners. Data lakes, data hubs, data warehouses, data marts, data fabrics, data meshes, data catalogs, data oceans are forming, they're evolving and exploding onto the scene. So in an effort to bring perspective to the sea of optionality, we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond. Hello everyone, my name is Dave Velannte with theCUBE, and I'd like to welcome you to a special Cube presentation, analysts predictions 2022: the future of data management. We've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade. Let me introduce our six power panelists. Sanjeev Mohan is former Gartner Analyst and Principal at SanjMo. Tony Baer, principal at dbInsight, Carl Olofson is well-known Research Vice President with IDC, Dave Menninger is Senior Vice President and Research Director at Ventana Research, Brad Shimmin, Chief Analyst, AI Platforms, Analytics and Data Management at Omdia and Doug Henschen, Vice President and Principal Analyst at Constellation Research. Gentlemen, welcome to the program and thanks for coming on theCUBE today. >> Great to be here. >> Thank you. >> All right, here's the format we're going to use. I as moderator, I'm going to call on each analyst separately who then will deliver their prediction or mega trend, and then in the interest of time management and pace, two analysts will have the opportunity to comment. If we have more time, we'll elongate it, but let's get started right away. Sanjeev Mohan, please kick it off. You want to talk about governance, go ahead sir. >> Thank you Dave. I believe that data governance which we've been talking about for many years is now not only going to be mainstream, it's going to be table stakes. And all the things that you mentioned, you know, the data, ocean data lake, lake houses, data fabric, meshes, the common glue is metadata. If we don't understand what data we have and we are governing it, there is no way we can manage it. So we saw Informatica went public last year after a hiatus of six. I'm predicting that this year we see some more companies go public. My bet is on Culebra, most likely and maybe Alation we'll see go public this year. I'm also predicting that the scope of data governance is going to expand beyond just data. It's not just data and reports. We are going to see more transformations like spark jawsxxxxx, Python even Air Flow. We're going to see more of a streaming data. So from Kafka Schema Registry, for example. We will see AI models become part of this whole governance suite. So the governance suite is going to be very comprehensive, very detailed lineage, impact analysis, and then even expand into data quality. We already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management, data catalogs, also data access governance. So what we are going to see is that once the data governance platforms become the key entry point into these modern architectures, I'm predicting that the usage, the number of users of a data catalog is going to exceed that of a BI tool. That will take time and we already seen that trajectory. Right now if you look at BI tools, I would say there a hundred users to BI tool to one data catalog. And I see that evening out over a period of time and at some point data catalogs will really become the main way for us to access data. Data catalog will help us visualize data, but if we want to do more in-depth analysis, it'll be the jumping off point into the BI tool, the data science tool and that is the journey I see for the data governance products. >> Excellent, thank you. Some comments. Maybe Doug, a lot of things to weigh in on there, maybe you can comment. >> Yeah, Sanjeev I think you're spot on, a lot of the trends the one disagreement, I think it's really still far from mainstream. As you say, we've been talking about this for years, it's like God, motherhood, apple pie, everyone agrees it's important, but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking. I think one thing that deserves mention in this context is ESG mandates and guidelines, these are environmental, social and governance, regs and guidelines. We've seen the environmental regs and guidelines and posts in industries, particularly the carbon-intensive industries. We've seen the social mandates, particularly diversity imposed on suppliers by companies that are leading on this topic. We've seen governance guidelines now being imposed by banks on investors. So these ESGs are presenting new carrots and sticks, and it's going to demand more solid data. It's going to demand more detailed reporting and solid reporting, tighter governance. But we're still far from mainstream adoption. We have a lot of, you know, best of breed niche players in the space. I think the signs that it's going to be more mainstream are starting with things like Azure Purview, Google Dataplex, the big cloud platform players seem to be upping the ante and starting to address governance. >> Excellent, thank you Doug. Brad, I wonder if you could chime in as well. >> Yeah, I would love to be a believer in data catalogs. But to Doug's point, I think that it's going to take some more pressure for that to happen. I recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the nineties and that didn't happen quite the way we anticipated. And so to Sanjeev's point it's because it is really complex and really difficult to do. My hope is that, you know, we won't sort of, how do I put this? Fade out into this nebula of domain catalogs that are specific to individual use cases like Purview for getting data quality right or like data governance and cybersecurity. And instead we have some tooling that can actually be adaptive to gather metadata to create something. And I know its important to you, Sanjeev and that is this idea of observability. If you can get enough metadata without moving your data around, but understanding the entirety of a system that's running on this data, you can do a lot. So to help with the governance that Doug is talking about. >> So I just want to add that, data governance, like any other initiatives did not succeed even AI went into an AI window, but that's a different topic. But a lot of these things did not succeed because to your point, the incentives were not there. I remember when Sarbanes Oxley had come into the scene, if a bank did not do Sarbanes Oxley, they were very happy to a million dollar fine. That was like, you know, pocket change for them instead of doing the right thing. But I think the stakes are much higher now. With GDPR, the flood gates opened. Now, you know, California, you know, has CCPA but even CCPA is being outdated with CPRA, which is much more GDPR like. So we are very rapidly entering a space where pretty much every major country in the world is coming up with its own compliance regulatory requirements, data residents is becoming really important. And I think we are going to reach a stage where it won't be optional anymore. So whether we like it or not, and I think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption. We were focused on features and these features were disconnected, very hard for business to adopt. These are built by IT people for IT departments to take a look at technical metadata, not business metadata. Today the tables have turned. CDOs are driving this initiative, regulatory compliances are beating down hard, so I think the time might be right. >> Yeah so guys, we have to move on here. But there's some real meat on the bone here, Sanjeev. I like the fact that you called out Culebra and Alation, so we can look back a year from now and say, okay, he made the call, he stuck it. And then the ratio of BI tools to data catalogs that's another sort of measurement that we can take even though with some skepticism there, that's something that we can watch. And I wonder if someday, if we'll have more metadata than data. But I want to move to Tony Baer, you want to talk about data mesh and speaking, you know, coming off of governance. I mean, wow, you know the whole concept of data mesh is, decentralized data, and then governance becomes, you know, a nightmare there, but take it away, Tony. >> We'll put this way, data mesh, you know, the idea at least as proposed by ThoughtWorks. You know, basically it was at least a couple of years ago and the press has been almost uniformly almost uncritical. A good reason for that is for all the problems that basically Sanjeev and Doug and Brad we're just speaking about, which is that we have all this data out there and we don't know what to do about it. Now, that's not a new problem. That was a problem we had in enterprise data warehouses, it was a problem when we had over DoOP data clusters, it's even more of a problem now that data is out in the cloud where the data is not only your data lake, is not only us three, it's all over the place. And it's also including streaming, which I know we'll be talking about later. So the data mesh was a response to that, the idea of that we need to bait, you know, who are the folks that really know best about governance? It's the domain experts. So it was basically data mesh was an architectural pattern and a process. My prediction for this year is that data mesh is going to hit cold heart reality. Because if you do a Google search, basically the published work, the articles on data mesh have been largely, you know, pretty uncritical so far. Basically loading and is basically being a very revolutionary new idea. I don't think it's that revolutionary because we've talked about ideas like this. Brad now you and I met years ago when we were talking about so and decentralizing all of us, but it was at the application level. Now we're talking about it at the data level. And now we have microservices. So there's this thought of have we managed if we're deconstructing apps in cloud native to microservices, why don't we think of data in the same way? My sense this year is that, you know, this has been a very active search if you look at Google search trends, is that now companies, like enterprise are going to look at this seriously. And as they look at it seriously, it's going to attract its first real hard scrutiny, it's going to attract its first backlash. That's not necessarily a bad thing. It means that it's being taken seriously. The reason why I think that you'll start to see basically the cold hearted light of day shine on data mesh is that it's still a work in progress. You know, this idea is basically a couple of years old and there's still some pretty major gaps. The biggest gap is in the area of federated governance. Now federated governance itself is not a new issue. Federated governance decision, we started figuring out like, how can we basically strike the balance between getting let's say between basically consistent enterprise policy, consistent enterprise governance, but yet the groups that understand the data and know how to basically, you know, that, you know, how do we basically sort of balance the two? There's a huge gap there in practice and knowledge. Also to a lesser extent, there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data. You know, basically through the full life cycle, from develop, from selecting the data from, you know, building the pipelines from, you know, determining your access control, looking at quality, looking at basically whether the data is fresh or whether it's trending off course. So my prediction is that it will receive the first harsh scrutiny this year. You are going to see some organization and enterprises declare premature victory when they build some federated query implementations. You going to see vendors start with data mesh wash their products anybody in the data management space that they are going to say that where this basically a pipelining tool, whether it's basically ELT, whether it's a catalog or federated query tool, they will all going to get like, you know, basically promoting the fact of how they support this. Hopefully nobody's going to call themselves a data mesh tool because data mesh is not a technology. We're going to see one other thing come out of this. And this harks back to the metadata that Sanjeev was talking about and of the catalog just as he was talking about. Which is that there's going to be a new focus, every renewed focus on metadata. And I think that's going to spur interest in data fabrics. Now data fabrics are pretty vaguely defined, but if we just take the most elemental definition, which is a common metadata back plane, I think that if anybody is going to get serious about data mesh, they need to look at the data fabric because we all at the end of the day, need to speak, you know, need to read from the same sheet of music. >> So thank you Tony. Dave Menninger, I mean, one of the things that people like about data mesh is it pretty crisply articulate some of the flaws in today's organizational approaches to data. What are your thoughts on this? >> Well, I think we have to start by defining data mesh, right? The term is already getting corrupted, right? Tony said it's going to see the cold hard light of day. And there's a problem right now that there are a number of overlapping terms that are similar but not identical. So we've got data virtualization, data fabric, excuse me for a second. (clears throat) Sorry about that. Data virtualization, data fabric, data federation, right? So I think that it's not really clear what each vendor means by these terms. I see data mesh and data fabric becoming quite popular. I've interpreted data mesh as referring primarily to the governance aspects as originally intended and specified. But that's not the way I see vendors using it. I see vendors using it much more to mean data fabric and data virtualization. So I'm going to comment on the group of those things. I think the group of those things is going to happen. They're going to happen, they're going to become more robust. Our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half, so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access. Again, whether you define it as mesh or fabric or virtualization isn't really the point here. But this notion that there are different elements of data, metadata and governance within an organization that all need to be managed collectively. The interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not, it's almost double, 68% of organizations, I'm sorry, 79% of organizations that were using virtualized access express satisfaction with their access to the data lake. Only 39% express satisfaction if they weren't using virtualized access. >> Oh thank you Dave. Sanjeev we just got about a couple of minutes on this topic, but I know you're speaking or maybe you've always spoken already on a panel with (indistinct) who sort of invented the concept. Governance obviously is a big sticking point, but what are your thoughts on this? You're on mute. (panelist chuckling) >> So my message to (indistinct) and to the community is as opposed to what they said, let's not define it. We spent a whole year defining it, there are four principles, domain, product, data infrastructure, and governance. Let's take it to the next level. I get a lot of questions on what is the difference between data fabric and data mesh? And I'm like I can't compare the two because data mesh is a business concept, data fabric is a data integration pattern. How do you compare the two? You have to bring data mesh a level down. So to Tony's point, I'm on a warpath in 2022 to take it down to what does a data product look like? How do we handle shared data across domains and governance? And I think we are going to see more of that in 2022, or is "operationalization" of data mesh. >> I think we could have a whole hour on this topic, couldn't we? Maybe we should do that. But let's corner. Let's move to Carl. So Carl, you're a database guy, you've been around that block for a while now, you want to talk about graph databases, bring it on. >> Oh yeah. Okay thanks. So I regard graph database as basically the next truly revolutionary database management technology. I'm looking forward for the graph database market, which of course we haven't defined yet. So obviously I have a little wiggle room in what I'm about to say. But this market will grow by about 600% over the next 10 years. Now, 10 years is a long time. But over the next five years, we expect to see gradual growth as people start to learn how to use it. The problem is not that it's not useful, its that people don't know how to use it. So let me explain before I go any further what a graph database is because some of the folks on the call may not know what it is. A graph database organizes data according to a mathematical structure called a graph. The graph has elements called nodes and edges. So a data element drops into a node, the nodes are connected by edges, the edges connect one node to another node. Combinations of edges create structures that you can analyze to determine how things are related. In some cases, the nodes and edges can have properties attached to them which add additional informative material that makes it richer, that's called a property graph. There are two principle use cases for graph databases. There's semantic property graphs, which are use to break down human language texts into the semantic structures. Then you can search it, organize it and answer complicated questions. A lot of AI is aimed at semantic graphs. Another kind is the property graph that I just mentioned, which has a dazzling number of use cases. I want to just point out as I talk about this, people are probably wondering, well, we have relation databases, isn't that good enough? So a relational database defines... It supports what I call definitional relationships. That means you define the relationships in a fixed structure. The database drops into that structure, there's a value, foreign key value, that relates one table to another and that value is fixed. You don't change it. If you change it, the database becomes unstable, it's not clear what you're looking at. In a graph database, the system is designed to handle change so that it can reflect the true state of the things that it's being used to track. So let me just give you some examples of use cases for this. They include entity resolution, data lineage, social media analysis, Customer 360, fraud prevention. There's cybersecurity, there's strong supply chain is a big one actually. There is explainable AI and this is going to become important too because a lot of people are adopting AI. But they want a system after the fact to say, how do the AI system come to that conclusion? How did it make that recommendation? Right now we don't have really good ways of tracking that. Machine learning in general, social network, I already mentioned that. And then we've got, oh gosh, we've got data governance, data compliance, risk management. We've got recommendation, we've got personalization, anti money laundering, that's another big one, identity and access management, network and IT operations is already becoming a key one where you actually have mapped out your operation, you know, whatever it is, your data center and you can track what's going on as things happen there, root cause analysis, fraud detection is a huge one. A number of major credit card companies use graph databases for fraud detection, risk analysis, tracking and tracing turn analysis, next best action, what if analysis, impact analysis, entity resolution and I would add one other thing or just a few other things to this list, metadata management. So Sanjeev, here you go, this is your engine. Because I was in metadata management for quite a while in my past life. And one of the things I found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it, but graphs can, okay? Graphs can do things like say, this term in this context means this, but in that context, it means that, okay? Things like that. And in fact, logistics management, supply chain. And also because it handles recursive relationships, by recursive relationships I mean objects that own other objects that are of the same type. You can do things like build materials, you know, so like parts explosion. Or you can do an HR analysis, who reports to whom, how many levels up the chain and that kind of thing. You can do that with relational databases, but yet it takes a lot of programming. In fact, you can do almost any of these things with relational databases, but the problem is, you have to program it. It's not supported in the database. And whenever you have to program something, that means you can't trace it, you can't define it. You can't publish it in terms of its functionality and it's really, really hard to maintain over time. >> Carl, thank you. I wonder if we could bring Brad in, I mean. Brad, I'm sitting here wondering, okay, is this incremental to the market? Is it disruptive and replacement? What are your thoughts on this phase? >> It's already disrupted the market. I mean, like Carl said, go to any bank and ask them are you using graph databases to get fraud detection under control? And they'll say, absolutely, that's the only way to solve this problem. And it is frankly. And it's the only way to solve a lot of the problems that Carl mentioned. And that is, I think it's Achilles heel in some ways. Because, you know, it's like finding the best way to cross the seven bridges of Koenigsberg. You know, it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique, it's still unfortunately kind of stands apart from the rest of the community that's building, let's say AI outcomes, as a great example here. Graph databases and AI, as Carl mentioned, are like chocolate and peanut butter. But technologically, you think don't know how to talk to one another, they're completely different. And you know, you can't just stand up SQL and query them. You've got to learn, know what is the Carl? Specter special. Yeah, thank you to, to actually get to the data in there. And if you're going to scale that data, that graph database, especially a property graph, if you're going to do something really complex, like try to understand you know, all of the metadata in your organization, you might just end up with, you know, a graph database winter like we had the AI winter simply because you run out of performance to make the thing happen. So, I think it's already disrupted, but we need to like treat it like a first-class citizen in the data analytics and AI community. We need to bring it into the fold. We need to equip it with the tools it needs to do the magic it does and to do it not just for specialized use cases, but for everything. 'Cause I'm with Carl. I think it's absolutely revolutionary. >> Brad identified the principal, Achilles' heel of the technology which is scaling. When these things get large and complex enough that they spill over what a single server can handle, you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down. So that's still a problem to be solved. >> Sanjeev, any quick thoughts on this? I mean, I think metadata on the word cloud is going to be the largest font, but what are your thoughts here? >> I want to (indistinct) So people don't associate me with only metadata, so I want to talk about something slightly different. dbengines.com has done an amazing job. I think almost everyone knows that they chronicle all the major databases that are in use today. In January of 2022, there are 381 databases on a ranked list of databases. The largest category is RDBMS. The second largest category is actually divided into two property graphs and IDF graphs. These two together make up the second largest number databases. So talking about Achilles heel, this is a problem. The problem is that there's so many graph databases to choose from. They come in different shapes and forms. To Brad's point, there's so many query languages in RDBMS, in SQL. I know the story, but here We've got cipher, we've got gremlin, we've got GQL and then we're proprietary languages. So I think there's a lot of disparity in this space. >> Well, excellent. All excellent points, Sanjeev, if I must say. And that is a problem that the languages need to be sorted and standardized. People need to have a roadmap as to what they can do with it. Because as you say, you can do so many things. And so many of those things are unrelated that you sort of say, well, what do we use this for? And I'm reminded of the saying I learned a bunch of years ago. And somebody said that the digital computer is the only tool man has ever device that has no particular purpose. (panelists chuckle) >> All right guys, we got to move on to Dave Menninger. We've heard about streaming. Your prediction is in that realm, so please take it away. >> Sure. So I like to say that historical databases are going to become a thing of the past. By that I don't mean that they're going to go away, that's not my point. I mean, we need historical databases, but streaming data is going to become the default way in which we operate with data. So in the next say three to five years, I would expect that data platforms and we're using the term data platforms to represent the evolution of databases and data lakes, that the data platforms will incorporate these streaming capabilities. We're going to process data as it streams into an organization and then it's going to roll off into historical database. So historical databases don't go away, but they become a thing of the past. They store the data that occurred previously. And as data is occurring, we're going to be processing it, we're going to be analyzing it, we're going to be acting on it. I mean we only ever ended up with historical databases because we were limited by the technology that was available to us. Data doesn't occur in patches. But we processed it in patches because that was the best we could do. And it wasn't bad and we've continued to improve and we've improved and we've improved. But streaming data today is still the exception. It's not the rule, right? There are projects within organizations that deal with streaming data. But it's not the default way in which we deal with data yet. And so that's my prediction is that this is going to change, we're going to have streaming data be the default way in which we deal with data and how you label it and what you call it. You know, maybe these databases and data platforms just evolved to be able to handle it. But we're going to deal with data in a different way. And our research shows that already, about half of the participants in our analytics and data benchmark research, are using streaming data. You know, another third are planning to use streaming technologies. So that gets us to about eight out of 10 organizations need to use this technology. And that doesn't mean they have to use it throughout the whole organization, but it's pretty widespread in its use today and has continued to grow. If you think about the consumerization of IT, we've all been conditioned to expect immediate access to information, immediate responsiveness. You know, we want to know if an item is on the shelf at our local retail store and we can go in and pick it up right now. You know, that's the world we live in and that's spilling over into the enterprise IT world We have to provide those same types of capabilities. So that's my prediction, historical databases become a thing of the past, streaming data becomes the default way in which we operate with data. >> All right thank you David. Well, so what say you, Carl, the guy who has followed historical databases for a long time? >> Well, one thing actually, every database is historical because as soon as you put data in it, it's now history. They'll no longer reflect the present state of things. But even if that history is only a millisecond old, it's still history. But I would say, I mean, I know you're trying to be a little bit provocative in saying this Dave 'cause you know, as well as I do that people still need to do their taxes, they still need to do accounting, they still need to run general ledger programs and things like that. That all involves historical data. That's not going to go away unless you want to go to jail. So you're going to have to deal with that. But as far as the leading edge functionality, I'm totally with you on that. And I'm just, you know, I'm just kind of wondering if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way applications work. Saying that an application should respond instantly, as soon as the state of things changes. What do you say about that? >> I think that's true. I think we do have to think about things differently. It's not the way we designed systems in the past. We're seeing more and more systems designed that way. But again, it's not the default. And I agree 100% with you that we do need historical databases you know, that's clear. And even some of those historical databases will be used in conjunction with the streaming data, right? >> Absolutely. I mean, you know, let's take the data warehouse example where you're using the data warehouse as its context and the streaming data as the present and you're saying, here's the sequence of things that's happening right now. Have we seen that sequence before? And where? What does that pattern look like in past situations? And can we learn from that? >> So Tony Baer, I wonder if you could comment? I mean, when you think about, you know, real time inferencing at the edge, for instance, which is something that a lot of people talk about, a lot of what we're discussing here in this segment, it looks like it's got a great potential. What are your thoughts? >> Yeah, I mean, I think you nailed it right. You know, you hit it right on the head there. Which is that, what I'm seeing is that essentially. Then based on I'm going to split this one down the middle is that I don't see that basically streaming is the default. What I see is streaming and basically and transaction databases and analytics data, you know, data warehouses, data lakes whatever are converging. And what allows us technically to converge is cloud native architecture, where you can basically distribute things. So you can have a node here that's doing the real-time processing, that's also doing... And this is where it leads in or maybe doing some of that real time predictive analytics to take a look at, well look, we're looking at this customer journey what's happening with what the customer is doing right now and this is correlated with what other customers are doing. So the thing is that in the cloud, you can basically partition this and because of basically the speed of the infrastructure then you can basically bring these together and kind of orchestrate them sort of a loosely coupled manner. The other parts that the use cases are demanding, and this is part of it goes back to what Dave is saying. Is that, you know, when you look at Customer 360, when you look at let's say Smart Utility products, when you look at any type of operational problem, it has a real time component and it has an historical component. And having predictive and so like, you know, my sense here is that technically we can bring this together through the cloud. And I think the use case is that we can apply some real time sort of predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction, we have this real-time input. >> Sanjeev, did you have a comment? >> Yeah, I was just going to say that to Dave's point, you know, we have to think of streaming very different because in the historical databases, we used to bring the data and store the data and then we used to run rules on top, aggregations and all. But in case of streaming, the mindset changes because the rules are normally the inference, all of that is fixed, but the data is constantly changing. So it's a completely reversed way of thinking and building applications on top of that. >> So Dave Menninger, there seem to be some disagreement about the default. What kind of timeframe are you thinking about? Is this end of decade it becomes the default? What would you pin? >> I think around, you know, between five to 10 years, I think this becomes the reality. >> I think its... >> It'll be more and more common between now and then, but it becomes the default. And I also want Sanjeev at some point, maybe in one of our subsequent conversations, we need to talk about governing streaming data. 'Cause that's a whole nother set of challenges. >> We've also talked about it rather in two dimensions, historical and streaming, and there's lots of low latency, micro batch, sub-second, that's not quite streaming, but in many cases its fast enough and we're seeing a lot of adoption of near real time, not quite real-time as good enough for many applications. (indistinct cross talk from panelists) >> Because nobody's really taking the hardware dimension (mumbles). >> That'll just happened, Carl. (panelists laughing) >> So near real time. But maybe before you lose the customer, however we define that, right? Okay, let's move on to Brad. Brad, you want to talk about automation, AI, the pipeline people feel like, hey, we can just automate everything. What's your prediction? >> Yeah I'm an AI aficionados so apologies in advance for that. But, you know, I think that we've been seeing automation play within AI for some time now. And it's helped us do a lot of things especially for practitioners that are building AI outcomes in the enterprise. It's helped them to fill skills gaps, it's helped them to speed development and it's helped them to actually make AI better. 'Cause it, you know, in some ways provide some swim lanes and for example, with technologies like AutoML can auto document and create that sort of transparency that we talked about a little bit earlier. But I think there's an interesting kind of conversion happening with this idea of automation. And that is that we've had the automation that started happening for practitioners, it's trying to move out side of the traditional bounds of things like I'm just trying to get my features, I'm just trying to pick the right algorithm, I'm just trying to build the right model and it's expanding across that full life cycle, building an AI outcome, to start at the very beginning of data and to then continue on to the end, which is this continuous delivery and continuous automation of that outcome to make sure it's right and it hasn't drifted and stuff like that. And because of that, because it's become kind of powerful, we're starting to actually see this weird thing happen where the practitioners are starting to converge with the users. And that is to say that, okay, if I'm in Tableau right now, I can stand up Salesforce Einstein Discovery, and it will automatically create a nice predictive algorithm for me given the data that I pull in. But what's starting to happen and we're seeing this from the companies that create business software, so Salesforce, Oracle, SAP, and others is that they're starting to actually use these same ideals and a lot of deep learning (chuckles) to basically stand up these out of the box flip-a-switch, and you've got an AI outcome at the ready for business users. And I am very much, you know, I think that's the way that it's going to go and what it means is that AI is slowly disappearing. And I don't think that's a bad thing. I think if anything, what we're going to see in 2022 and maybe into 2023 is this sort of rush to put this idea of disappearing AI into practice and have as many of these solutions in the enterprise as possible. You can see, like for example, SAP is going to roll out this quarter, this thing called adaptive recommendation services, which basically is a cold start AI outcome that can work across a whole bunch of different vertical markets and use cases. It's just a recommendation engine for whatever you needed to do in the line of business. So basically, you're an SAP user, you look up to turn on your software one day, you're a sales professional let's say, and suddenly you have a recommendation for customer churn. Boom! It's going, that's great. Well, I don't know, I think that's terrifying. In some ways I think it is the future that AI is going to disappear like that, but I'm absolutely terrified of it because I think that what it really does is it calls attention to a lot of the issues that we already see around AI, specific to this idea of what we like to call at Omdia, responsible AI. Which is, you know, how do you build an AI outcome that is free of bias, that is inclusive, that is fair, that is safe, that is secure, that its audible, et cetera, et cetera, et cetera, et cetera. I'd take a lot of work to do. And so if you imagine a customer that's just a Salesforce customer let's say, and they're turning on Einstein Discovery within their sales software, you need some guidance to make sure that when you flip that switch, that the outcome you're going to get is correct. And that's going to take some work. And so, I think we're going to see this move, let's roll this out and suddenly there's going to be a lot of problems, a lot of pushback that we're going to see. And some of that's going to come from GDPR and others that Sanjeev was mentioning earlier. A lot of it is going to come from internal CSR requirements within companies that are saying, "Hey, hey, whoa, hold up, we can't do this all at once. "Let's take the slow route, "let's make AI automated in a smart way." And that's going to take time. >> Yeah, so a couple of predictions there that I heard. AI simply disappear, it becomes invisible. Maybe if I can restate that. And then if I understand it correctly, Brad you're saying there's a backlash in the near term. You'd be able to say, oh, slow down. Let's automate what we can. Those attributes that you talked about are non trivial to achieve, is that why you're a bit of a skeptic? >> Yeah. I think that we don't have any sort of standards that companies can look to and understand. And we certainly, within these companies, especially those that haven't already stood up an internal data science team, they don't have the knowledge to understand when they flip that switch for an automated AI outcome that it's going to do what they think it's going to do. And so we need some sort of standard methodology and practice, best practices that every company that's going to consume this invisible AI can make use of them. And one of the things that you know, is sort of started that Google kicked off a few years back that's picking up some momentum and the companies I just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing. You know, so like for the SAP example, we know, for example, if it's convolutional neural network with a long, short term memory model that it's using, we know that it only works on Roman English and therefore me as a consumer can say, "Oh, well I know that I need to do this internationally. "So I should not just turn this on today." >> Thank you. Carl could you add anything, any context here? >> Yeah, we've talked about some of the things Brad mentioned here at IDC and our future of intelligence group regarding in particular, the moral and legal implications of having a fully automated, you know, AI driven system. Because we already know, and we've seen that AI systems are biased by the data that they get, right? So if they get data that pushes them in a certain direction, I think there was a story last week about an HR system that was recommending promotions for White people over Black people, because in the past, you know, White people were promoted and more productive than Black people, but it had no context as to why which is, you know, because they were being historically discriminated, Black people were being historically discriminated against, but the system doesn't know that. So, you know, you have to be aware of that. And I think that at the very least, there should be controls when a decision has either a moral or legal implication. When you really need a human judgment, it could lay out the options for you. But a person actually needs to authorize that action. And I also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases. In some extent, they always will. So we'll always be chasing after them. But that's (indistinct). >> Absolutely Carl, yeah. I think that what you have to bear in mind as a consumer of AI is that it is a reflection of us and we are a very flawed species. And so if you look at all of the really fantastic, magical looking supermodels we see like GPT-3 and four, that's coming out, they're xenophobic and hateful because the people that the data that's built upon them and the algorithms and the people that build them are us. So AI is a reflection of us. We need to keep that in mind. >> Yeah, where the AI is biased 'cause humans are biased. All right, great. All right let's move on. Doug you mentioned mentioned, you know, lot of people that said that data lake, that term is not going to live on but here's to be, have some lakes here. You want to talk about lake house, bring it on. >> Yes, I do. My prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering. I say offering that doesn't mean it's going to be the dominant thing that organizations have out there, but it's going to be the pro dominant vendor offering in 2022. Now heading into 2021, we already had Cloudera, Databricks, Microsoft, Snowflake as proponents, in 2021, SAP, Oracle, and several of all of these fabric virtualization/mesh vendors joined the bandwagon. The promise is that you have one platform that manages your structured, unstructured and semi-structured information. And it addresses both the BI analytics needs and the data science needs. The real promise there is simplicity and lower cost. But I think end users have to answer a few questions. The first is, does your organization really have a center of data gravity or is the data highly distributed? Multiple data warehouses, multiple data lakes, on premises, cloud. If it's very distributed and you'd have difficulty consolidating and that's not really a goal for you, then maybe that single platform is unrealistic and not likely to add value to you. You know, also the fabric and virtualization vendors, the mesh idea, that's where if you have this highly distributed situation, that might be a better path forward. The second question, if you are looking at one of these lake house offerings, you are looking at consolidating, simplifying, bringing together to a single platform. You have to make sure that it meets both the warehouse need and the data lake need. So you have vendors like Databricks, Microsoft with Azure Synapse. New really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements, can meet the user and query concurrency requirements. Meet those tight SLS. And then on the other hand, you have the Oracle, SAP, Snowflake, the data warehouse folks coming into the data science world, and they have to prove that they can manage the unstructured information and meet the needs of the data scientists. I'm seeing a lot of the lake house offerings from the warehouse crowd, managing that unstructured information in columns and rows. And some of these vendors, Snowflake a particular is really relying on partners for the data science needs. So you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement. >> Thank you Doug. Well Tony, if those two worlds are going to come together, as Doug was saying, the analytics and the data science world, does it need to be some kind of semantic layer in between? I don't know. Where are you in on this topic? >> (chuckles) Oh, didn't we talk about data fabrics before? Common metadata layer (chuckles). Actually, I'm almost tempted to say let's declare victory and go home. And that this has actually been going on for a while. I actually agree with, you know, much of what Doug is saying there. Which is that, I mean I remember as far back as I think it was like 2014, I was doing a study. I was still at Ovum, (indistinct) Omdia, looking at all these specialized databases that were coming up and seeing that, you know, there's overlap at the edges. But yet, there was still going to be a reason at the time that you would have, let's say a document database for JSON, you'd have a relational database for transactions and for data warehouse and you had basically something at that time that resembles a dupe for what we consider your data life. Fast forward and the thing is what I was seeing at the time is that you were saying they sort of blending at the edges. That was saying like about five to six years ago. And the lake house is essentially on the current manifestation of that idea. There is a dichotomy in terms of, you know, it's the old argument, do we centralize this all you know in a single place or do we virtualize? And I think it's always going to be a union yeah and there's never going to be a single silver bullet. I do see that there are also going to be questions and these are points that Doug raised. That you know, what do you need for your performance there, or for your free performance characteristics? Do you need for instance high concurrency? You need the ability to do some very sophisticated joins, or is your requirement more to be able to distribute and distribute our processing is, you know, as far as possible to get, you know, to essentially do a kind of a brute force approach. All these approaches are valid based on the use case. I just see that essentially that the lake house is the culmination of it's nothing. It's a relatively new term introduced by Databricks a couple of years ago. This is the culmination of basically what's been a long time trend. And what we see in the cloud is that as we start seeing data warehouses as a check box items say, "Hey, we can basically source data in cloud storage, in S3, "Azure Blob Store, you know, whatever, "as long as it's in certain formats, "like, you know parquet or CSP or something like that." I see that as becoming kind of a checkbox item. So to that extent, I think that the lake house, depending on how you define is already reality. And in some cases, maybe new terminology, but not a whole heck of a lot new under the sun. >> Yeah. And Dave Menninger, I mean a lot of these, thank you Tony, but a lot of this is going to come down to, you know, vendor marketing, right? Some people just kind of co-op the term, we talked about you know, data mesh washing, what are your thoughts on this? (laughing) >> Yeah, so I used the term data platform earlier. And part of the reason I use that term is that it's more vendor neutral. We've tried to sort of stay out of the vendor terminology patenting world, right? Whether the term lake houses, what sticks or not, the concept is certainly going to stick. And we have some data to back it up. About a quarter of organizations that are using data lakes today, already incorporate data warehouse functionality into it. So they consider their data lake house and data warehouse one in the same, about a quarter of organizations, a little less, but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake. So it's pretty obvious that three quarters of organizations need to bring this stuff together, right? The need is there, the need is apparent. The technology is going to continue to converge. I like to talk about it, you know, you've got data lakes over here at one end, and I'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a server and you ignore it, right? That's not what a data lake is. So you've got data lake people over here and you've got database people over here, data warehouse people over here, database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities. So it's obvious that they're going to meet in the middle. I mean, I think it's like Tony says, I think we should declare victory and go home. >> As hell. So just a follow-up on that, so are you saying the specialized lake and the specialized warehouse, do they go away? I mean, Tony data mesh practitioners would say or advocates would say, well, they could all live. It's just a node on the mesh. But based on what Dave just said, are we gona see those all morphed together? >> Well, number one, as I was saying before, there's always going to be this sort of, you know, centrifugal force or this tug of war between do we centralize the data, do we virtualize? And the fact is I don't think that there's ever going to be any single answer. I think in terms of data mesh, data mesh has nothing to do with how you're physically implement the data. You could have a data mesh basically on a data warehouse. It's just that, you know, the difference being is that if we use the same physical data store, but everybody's logically you know, basically governing it differently, you know? Data mesh in space, it's not a technology, it's processes, it's governance process. So essentially, you know, I basically see that, you know, as I was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring, but there are going to be cases where, for instance, if I need, let's say like, Upserve, I need like high concurrency or something like that. There are certain things that I'm not going to be able to get efficiently get out of a data lake. And, you know, I'm doing a system where I'm just doing really brute forcing very fast file scanning and that type of thing. So I think there always will be some delineations, but I would agree with Dave and with Doug, that we are seeing basically a confluence of requirements that we need to essentially have basically either the element, you know, the ability of a data lake and the data warehouse, these need to come together, so I think. >> I think what we're likely to see is organizations look for a converge platform that can handle both sides for their center of data gravity, the mesh and the fabric virtualization vendors, they're all on board with the idea of this converged platform and they're saying, "Hey, we'll handle all the edge cases "of the stuff that isn't in that center of data gravity "but that is off distributed in a cloud "or at a remote location." So you can have that single platform for the center of your data and then bring in virtualization, mesh, what have you, for reaching out to the distributed data. >> As Dave basically said, people are happy when they virtualized data. >> I think we have at this point, but to Dave Menninger's point, they are converging, Snowflake has introduced support for unstructured data. So obviously literally splitting here. Now what Databricks is saying is that "aha, but it's easy to go from data lake to data warehouse "than it is from databases to data lake." So I think we're getting into semantics, but we're already seeing these two converge. >> So take somebody like AWS has got what? 15 data stores. Are they're going to 15 converge data stores? This is going to be interesting to watch. All right, guys, I'm going to go down and list do like a one, I'm going to one word each and you guys, each of the analyst, if you would just add a very brief sort of course correction for me. So Sanjeev, I mean, governance is going to to be... Maybe it's the dog that wags the tail now. I mean, it's coming to the fore, all this ransomware stuff, which you really didn't talk much about security, but what's the one word in your prediction that you would leave us with on governance? >> It's going to be mainstream. >> Mainstream. Okay. Tony Baer, mesh washing is what I wrote down. That's what we're going to see in 2022, a little reality check, you want to add to that? >> Reality check, 'cause I hope that no vendor jumps the shark and close they're offering a data niche product. >> Yeah, let's hope that doesn't happen. If they do, we're going to call them out. Carl, I mean, graph databases, thank you for sharing some high growth metrics. I know it's early days, but magic is what I took away from that, so magic database. >> Yeah, I would actually, I've said this to people too. I kind of look at it as a Swiss Army knife of data because you can pretty much do anything you want with it. That doesn't mean you should. I mean, there's definitely the case that if you're managing things that are in fixed schematic relationship, probably a relation database is a better choice. There are times when the document database is a better choice. It can handle those things, but maybe not. It may not be the best choice for that use case. But for a great many, especially with the new emerging use cases I listed, it's the best choice. >> Thank you. And Dave Menninger, thank you by the way, for bringing the data in, I like how you supported all your comments with some data points. But streaming data becomes the sort of default paradigm, if you will, what would you add? >> Yeah, I would say think fast, right? That's the world we live in, you got to think fast. >> Think fast, love it. And Brad Shimmin, love it. I mean, on the one hand I was saying, okay, great. I'm afraid I might get disrupted by one of these internet giants who are AI experts. I'm going to be able to buy instead of build AI. But then again, you know, I've got some real issues. There's a potential backlash there. So give us your bumper sticker. >> I'm would say, going with Dave, think fast and also think slow to talk about the book that everyone talks about. I would say really that this is all about trust, trust in the idea of automation and a transparent and visible AI across the enterprise. And verify, verify before you do anything. >> And then Doug Henschen, I mean, I think the trend is your friend here on this prediction with lake house is really becoming dominant. I liked the way you set up that notion of, you know, the data warehouse folks coming at it from the analytics perspective and then you get the data science worlds coming together. I still feel as though there's this piece in the middle that we're missing, but your, your final thoughts will give you the (indistinct). >> I think the idea of consolidation and simplification always prevails. That's why the appeal of a single platform is going to be there. We've already seen that with, you know, DoOP platforms and moving toward cloud, moving toward object storage and object storage, becoming really the common storage point for whether it's a lake or a warehouse. And that second point, I think ESG mandates are going to come in alongside GDPR and things like that to up the ante for good governance. >> Yeah, thank you for calling that out. Okay folks, hey that's all the time that we have here, your experience and depth of understanding on these key issues on data and data management really on point and they were on display today. I want to thank you for your contributions. Really appreciate your time. >> Enjoyed it. >> Thank you. >> Thanks for having me. >> In addition to this video, we're going to be making available transcripts of the discussion. We're going to do clips of this as well we're going to put them out on social media. I'll write this up and publish the discussion on wikibon.com and siliconangle.com. No doubt, several of the analysts on the panel will take the opportunity to publish written content, social commentary or both. I want to thank the power panelists and thanks for watching this special CUBE presentation. This is Dave Vellante, be well and we'll see you next time. (bright music)