(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)

(intro music) >> Good afternoon guys and gals. Welcome back to The Strip, Las Vegas. It's "theCUBE" live day four of our coverage of "AWS re:Invent". Lisa Martin, Dave Vellante. Dave, we've had some awesome conversations the last four days. I can't believe how many people are still here. The AWS ecosystem seems stronger than ever. >> Yeah, last year we really noted the ecosystem, you know, coming out of the isolation economy 'cause everybody had this old pent up demand to get together and the ecosystem, even last year, we were like, "Wow." This year's like 10x wow. >> It really is 10x wow, it feels that way. We're going to have a 10x wow conversation next. We're bringing back DataStax to "theCUBE". Please welcome Thomas Bean, it's CMO. Thomas welcome to "theCUBE". >> Thanks, thanks a lot, thanks for having me. >> Great to have you, talk to us about what's going on at DataStax, it's been a little while since we talked to you guys. >> Indeed, so DataStax, we are the realtime data company and we've always been involved in technology such as "Apache Cassandra". We actually created to support and take this, this great technology to the market. And now we're taking it, combining it with other technologies such as "Apache Pulse" for streaming to provide a realtime data cloud. Which helps our users, our customers build applications faster and help them scale without limits. So it's all about mobilizing all of this information that is going to drive the application going to create the awesome experience, when you have a customer waiting behind their mobile phone, when you need a decision to take place immediately to, that's the kind of data that we, that we provide in the cloud on any cloud, but especially with, with AWS and providing the performance that technologies like "Apache Cassandra" are known for but also with market leading unit economics. So really empowering customers to operate at speed and scale. >> Speaking of customers, nobody wants less data slower. And one of the things I think we learned in the in the pan, during the pandemic was that access to realtime data isn't nice to have anymore for any business. It is table stakes, it's competitive advantage. There's somebody right behind in the rear view mirror ready to take over. How has the business model of DataStax maybe evolved in the last couple of years with the fact that realtime data is so critical? >> Realtime data has been around for some time but it used to be really niches. You needed a lot of, a lot of people a lot of funding actually to, to implement these, these applications. So we've adapted to really democratize it, made super easy to access. Not only to start developing but also scaling. So this is why we've taken these great technologies made them serverless cloud native on the cloud so that developers could really start easily and scale. So that be on project products could be taken to the, to the market. And in terms of customers, the patterns is we've seen enterprise customers, you were talking about the pandemic, the Home Depot as an example was able to deliver curbside pickup delivery in 30 days because they were already using DataStax and could adapt their business model with a real time application that combines you were just driving by and you would get the delivery of what exactly you ordered without having to go into the the store. So they shifted their whole business model. But we also see a real strong trend about customer experiences and increasingly a lot of tech companies coming because scale means success to them and building on, on our, on our stack to, to build our applications. >> So Lisa, it's interesting. DataStax and "theCUBE" were started the same year, 2010, and that's when it was the beginning of the ascendancy of the big data era. But of course back then there was, I mean very little cloud. I mean most of it was on-prem. And so data stacks had, you know, had obviously you mentioned a number of things that you had to do to become cloud friendly. >> Thomas: Yes. >> You know, a lot of companies didn't make it, make it through. You guys just raised a bunch of dough as well last summer. And so that's been quite a transformation both architecturally, you know, bringing the customers through. I presume part of that was because you had such a great open source community, but also you have a unique value problem. Maybe you could sort of describe that a little. >> Absolutely, so the, I'll start with the open source community where we see a lot of traction at the, at the moment. We were always very involved with, with the "Apache Cassandra". But what we're seeing right now with "Apache Cassandra" is, is a lot of traction, gaining momentum. We actually, we, the open source community just won an award, did an AMA, had a, a vote from their readers about the top open source projects and "Apache Cassandra" and "Apache Pulse" are part of the top three, which is, which is great. We also run a, in collaboration with the Apache Project, the, a series of events around the, around the globe called "Cassandra Days" where we had tremendous attendance. We, some of them, we had to change venue twice because there were more people coming. A lot of students, a lot of the big users of Cassandra like Apple, Netflix who spoke at these, at these events. So we see this momentum actually picking up and that's why we're also super excited that the Linux Foundation is running the Cassandra Summit in in March in San Jose. Super happy to bring that even back with the rest of the, of the community and we have big announcements to come. "Apache Cassandra" will, will see its next version with major advances such as the support of asset transactions, which is going to make it even more suitable to more use cases. So we're bringing that scale to more applications. So a lot of momentum in terms of, in terms of the, the open source projects. And to your point about the value proposition we take this great momentum to which we contribute a lot. It's not only about taking, it's about giving as well. >> Dave: Big committers, I mean... >> Exactly big contributors. And we also have a lot of expertise, we worked with all of the members of the community, many of them being our customers. So going to the cloud, indeed there was architectural work making Cassandra cloud native putting it on Kubernetes, having the right APIs for developers to, to easily develop on top of it. But also becoming a cloud company, building customer success, our own platform engineering. We, it's interesting because actually we became like our partners in a community. We now operate Cassandra in the cloud so that all of our customers can benefit from all the power of Cassandra but really efficiently, super rapidly, and also with a, the leading unit economies as I mentioned. >> How will the, the asset compliance affect your, you know, new markets, new use cases, you know, expand your TAM, can you explain that? >> I think it will, more applications will be able to tap into the power of, of "NoSQL". Today we see a lot on the customer experience as IOT, gaming platform, a lot of SaaS companies. But now with the ability to have transactions at the database level, we can, beyond providing information, we can go even deeper into the logic of the, of the application. So it makes Cassandra and therefore Astra which is our cloud service an even more suitable database we can address, address more even in terms of the transaction that the application itself will, will support. >> What are some of the business benefits that Cassandra delivers to customers in terms of business outcomes helping businesses really transform? >> So Cassandra brings skill when you have millions of customers, when you have million of data points to go through to serve each of the customers. One of my favorite example is Priceline, who runs entirely on our cloud service. You may see one offer, but it's actually everything they know about you and everything they have to offer matched while you are refreshing your page. This is the kind of power that Cassandra provide. But the thing to say about "Apache Cassandra", it used to be also a database that was a bit hard to manage and hard to develop with. This is why as part of the cloud, we wanted to change these aspects, provide developers the API they like and need and what the application need. Making it super simple to operate and, and, and super affordable, also cost effective to, to run. So the the value to your point, it's time to market. You go faster, you don't have to worry when you choose the right database you're not going to, going to have to change horse in the middle of the river, like sixth month down the line. And you know, you have the guarantee that you're going to get the performance and also the best, the best TCO which matters a lot. I think your previous person talking was addressing it. That's also important especially in the, in a current context. >> As a managed service, you're saying, that's the enabler there, right? >> Thomas: Exactly. >> Dave: That is the model today. I mean, you have to really provide that for customers. They don't want to mess with, you know, all the plumbing, right? I mean... >> Absolutely, I don't think people want to manage databases anymore, we do that very well. We take SLAs and such and even at the developer level what they want is an API so they get all the power. All of of this powered by Cassandra, but now they get it as a, and it's as simple as using as, as an API. >> How about the ecosystem? You mentioned the show in in San Jose in March and the Linux Foundation is, is hosting that, is that correct? >> Yes, absolutely. >> And what is it, Cassandra? >> Cassandra Summit. >> Dave: Cassandra Summit >> Yep. >> What's the ecosystem like today in Cassandra, can you just sort of describe that? >> Around Cassandra, you have actually the big hyperscalers. You have also a few other companies that are supporting Cassandra like technologies. And what's interesting, and that's been a, a something we've worked on but also the "Apache Project" has worked on. Working on a lot of the adjacent technologies, the data pipelines, all of the DevOps solutions to make sure that you can actually put Cassandra as part of your way to build these products and, and build these, these applications. So the, the ecosystem keeps on, keeps on growing and actually the, the Cassandra community keeps on opening the database so that it's, it's really easy to have it connect to the rest of the, the rest environment. And we benefit from all of this in our Astra cloud service. >> So things like machine learning, governance tools that's what you would expect in the ecosystem forming around it, right? So we'll see that in March. >> Machine learning is especially a very interesting use case. We see more and more of it. We recently did a, a nice video with one of our customers called Unifour who does exactly this using also our abstract cloud service. What they provide is they analyze videos of sales calls and they help actually the sellers telling them, "Okay here's what happened here was the customer sentiment". Because they have proof that the better the sentiment is, the shorter the sell cycle is going to be. So they teach the, the sellers on how to say the right things, how to control the thing. This is machine learning applied on video. Cassandra provides I think 200 data points per second that feeds this machine learning. And we see more and more of these use cases, realtime use cases. It happens on the fly when you are on your phone, when you have a, a fraud maybe to detect and to prevent. So it is going to be more and more and we see more and more of these integration at the open source level with technologies like even "Feast" project like "Apache Feast". But also in the, in, in the partners that we're working with integrating our Cassandra and our cloud service with. >> Where are customer conversations these days, given that every company has to be a data company. They have to be able to, to democratize data, allow access to it deep into the, into the organizations. Not just IT or the data organization anymore. But are you finding that the conversations are rising up the, up the stack? Is this, is this a a C-suite priority? Is this a board level conversation? >> So that's an excellent question. We actually ran a survey this summer called "The State of the Database" where we, we asked these tech leaders, okay what's top of mind for you? And real time actually was, was really one of the top priorities. And they explained for the one that who call themselves digital leaders that for 71% of them they could correlate directly the use of realtime data, the quality of their experience or their decision making with revenue. And that's really where the discussion is. And I think it's something we can relate to as users. We don't want the, I mean if the Starbucks apps take seconds to to respond there will be a riot over there. So that's, that's something we can feel. But it really, now it's tangible in, in business terms and now then they take a look at their data strategy, are we equipped? Very often they will see, yeah, we have pockets of realtime data, but we're not really able to leverage it. >> Lisa: Yeah. >> For ML use cases, et cetera. So that's a big trend that we're seeing on one end. On the other end, what we're seeing, and it's one of the things we discussed a lot at the event is that yeah cost is important. Growth at all, at all cost does not exist. So we see a lot of push on moving a lot of the workloads to the cloud to make them scale but at the best the best cost. And we also see some organizations where like, okay let's not let a good crisis go to waste and let's accelerate our innovation not at all costs. So that we see also a lot of new projects being being pushed but reasonable, starting small and, and growing and all of this fueled by, by realtime data, so interesting. >> The other big topic amongst the, the customer community is security. >> Yep. >> I presume it's coming up a lot. What's the conversation like with DataStax? >> That's a topic we've been working on intensely since the creation of Astra less than two years ago. And we keep on reinforcing as any, any cloud provider not only our own abilities in terms of making sure that customers can manage their own keys, et cetera. But also integrating to the rest of the, of the ecosystem when some, a lot of our customers are running on AWS, how do we integrate with PrivateLink and such? We fit exactly into their security environment on AWS and they use exactly the same management tool. Because this is also what used to cost a lot in the cloud services. How much do you have to do to wire them and, and manage. And there are indeed compliance and governance challenges. So that's why making sure that it's fully connected that they have full transparency on what's happening is, is a big part of the evolution. It's always, security is always something you're working on but it's, it's a major topic for us. >> Yep, we talk about that on pretty much every event. Security, which we could dive into, but we're out of time. Last question for you. >> Thomas: Yes. >> We're talking before we went live, we're both big Formula One fans. Say DataStax has the opportunity to sponsor a team and you get the whole side pod to, to put like a phrase about DataStax on the side pod of this F1 car. (laughter) Like a billboard, what does it say? >> Billboard, because an F1 car goes pretty fast, it will be hard to, be hard to read but, "Twice the performance at half the cost, try Astra a cloud service." >> Drop the mike. Awesome, Thomas, thanks so much for joining us. >> Thank for having me. >> Pleasure having you guys on the program. For our guest, Thomas Bean and Dave Vellante, I'm Lisa Martin and you're watching "theCUBE" live from day four of our coverage. "theCUBE", the leader in live tech coverage. (outro music)

(upbeat music) >> Okay, we're now going to try and stretch our minds a little bit and stretch Supercloud to the edge. Supercloud, as we've been discussing today and reporting through various breaking analyses, is a term we use to describe a continuous experience across clouds, or even on-prem, that adds new value on top of hyperscale infrastructure. Priya Rajagopal is the director of product management at Couchbase. She's a developer, a software architect, co-creator on a number of patents as well as being an expert on edge, IoT, and mobile computing technologies. And we're going to talk about edge requirements. Priya, you've been around software engineering and mobile and edge technologies your entire career, and now you're responsible for bringing enterprise class database technology to the edge and IoT environments, synchronizing. So, when you think about the edge, the near edge, the far edge, what are the fundamental assumptions that you have to make with regards to things like connectivity, bandwidth, security, and any other technical considerations when you think about software architecture for these environments? >> Sure, sure. First off, Dave, thanks for having me here. It's really exciting to be here again, my second time. And thank you for that kind introduction. So, quickly to get back to your question. When it comes to architecting for the edge our principle is prepare for the worst and hope for the best. Because, really, when it comes to edge computing, it's sort of the edge cases that come to bite you. You mentioned connectivity, bandwidth, security. I have a few more. Starting with connectivity, as you import on low network connectivity, think offshore oil rigs, cruise ships, or even retail settings, when you want to have business continuity, most of the time you've got an internet connection, but then when there is disruption, then you lose business continuity. Then when it comes to bandwidth, the notion or the approach we take is that bandwidth is always limited or it's at a premium. Data plans can go up through the roof, depending on the volume of data. Think medical clinics in rural areas. When it comes to security, edge poses unique challenges because you're moving away from this world garden, central cloud-based environment, and now everything is accessible over the internet. And the internet really is inherently untrustworthy. Every bit of data that is written or read by an application needs to be authenticated, needs to be authorized. The entire path needs to be secured end-to-end. It needs to be encrypted. That's confidentiality. Also the persistence of data itself. It needs to be encrypted on disk. Now, one of the advantages of edge computing or distributing data is that the impacted edge environment can be isolated away without impacting the other edge location. Looking at the classic retail architecture, if you've got retail use case, if you've got a a retail store where there's a security breach, you need to have a provision of isolating that store so that you don't bring down services for the other stores. When it comes to edge computing, you have to think about those aspects of security. Any of these locations could be breached. And if one of them is breached, how do you control that? So, that's to answer those three key topics that you brought up. But there are other considerations. One is data governance. That's a huge challenge. Because we are a database company at Couchbase, we think of database, data governance, compliance, privacy. All that is very paramount to our customers. It's not just about enforcing policies right now. We are talking about not enforcing policies in a central location, but you have to do it in a distributed fashion because one of the benefits of edge computing is, as you probably very well know, is the benefits it brings when it comes to data privacy, governance policies. You can enforce that at a granular scale because data doesn't have to ever leave the edge. But again, I talked about this in the context of security, there needs to be a way to control this data at the edge. You have to govern the data when it is at the edge remotely. Some of the other challenges when thinking about the edge is, of course, volume, scale, think IoT, mobile devices, classic far edge type scenarios. And I think the other criteria that we have to keep in mind when we are architecting a platform for this kind of computing paradigm is the heterogeneity of the edge itself. It's no longer a uniform set of compute and storage resources that are available at your disposal. You've got a variety of IoT devices. You've got mobile devices, different processing capabilities, different storage capabilities. When it comes to edge data centers, it's not uniform in terms of what services are available. Do they have a load balancer? Do they have a firewall? Can I deploy a firewall? These are all some key architectural considerations when it comes to actually architecting a solution for the edge. >> Great. Thank you for that awesome setup. Talking about stretching to the edge this idea of Supercloud that connote that single logical layer that spans across multiple clouds. It can include on on-prem, but a critical criterion is that the developer, and, of course, the user experience, is identical or substantially similar. Let's say identical. Let's say identical, irrespective of physical location. Priya, is that vision technically achievable today in the world of database. And if so, can you describe the architectural elements that make it possible to perform well and have low latency and the security and other criteria that you just mentioned? What's the technical enablers? Is it just good software. Is it architecture? Help us understand that. >> Sure. You brought up two aspects. You mentioned user experience, and then you mentioned from a developer standpoint, what does it take? And I'd like to address the two separately. They are very tightly related, but I'd like to address them separately. Just focusing on the easier of the two when it comes to user experience, what are the factors that impact user experience? You're talking about reliability of service. Always on, always available applications. It doesn't matter where the data is coming from. Whether the data is coming from my device, it's sourced from an on-prem data center, or if it is from the edge of the cloud, it's from a central cloud data center, from an end-user perspective, all they care about is that their application is available. The next is, of course, responsiveness. Users are getting increasingly impatient. Do you want to reduce wait times to service? You want something which is extremely fast. They're looking for immersive applications or immersive experiences, AR, VR, mixed reality use cases. Then something which is very critical, and what you just touched upon, is this sort of seamless experience. Like this omnichannel, as we talk about in the context of retail kind of experience, Or what I like to refer to as park and pick up reference. You park, you start your application, running your application, you start a transaction on one device, you park it, pick it up on another device. Or in case of retail, you walk into a store, you pick it up from there. So, there's a park and pick up. Seamless mobility of data is extremely critical. In the context of a database, when we talk about responsiveness, two key, the KPIs are latency, bandwidth. And latency is really the round trip time from the time it takes to make a request for data, and the response comes back. The factors that impact latency are, of course, the type of the network itself, but also the proximity of the data source to the point of consumption. And so the more number of hubs that the data packets have to take to reach from the source to its destination, then you're going to incur a lot of latency. And when it comes to bandwidth, we are talking about the capacity of the network. How much data can be shot through the pipe? And, of course, when edge computing, large number of clients. I talked about scale, the volume of devices. And when you're talking about all of them concurrently connected, then you're going to have network congestion which impacts bandwidth which, in turn, impacts performance. And so when it comes to how do you architect a solution for that, if you completely remove the reliance on network to the extent possible, then you get the highest guarantees when it comes to responsiveness, availability, reliability. Because your application is always going to be on. In order to do that, if you have the database and the data processing components co-located with the application that needs it, that would give you the best experience. But, of course, you want to bring it as close. A lot of times, it's not possible to end with that data within your application itself. And that's where you have options of your an on-prem data center, the edge of the cloud, max end and so on. So the closer you bring the data, you're going to get the better experience. Now, that's all great. But then when it comes to something to achieve a vision of Supercloud, when we talked about, "Hey, one way from a developer standpoint, I have one API to set up this connection to a server, but then behind the scenes, my data could be resident anywhere." How do you achieve something like that? And so, a critical aspect of the solution is data synchronization. I talked about data storage as a database, data storage database, that's a critical aspect of what database is really where the data is persisted, data processing, the APIs to access and query the data. But another really critical aspect of distributing a database is the data synchronization technology. And so once all the islands of data, whether it is on the device, whether it's an on-prem data center, whether it's the edge of the cloud, or whether it is a regional data center, once all those databases are kept in sync, then it's a question of when connectivity to one of those data centers goes down, then there needs to be a seamless switch to another data center. And today, at least when it comes to Couchbase, a lot of our customers do employ global load balancers which can automatically detect. So, from a perspective of an application, it's just one URL end point. But then when one of those services goes down or data centers goes down, we have active failover and standby. And so the load balance automatically redirects all the traffic to the backup data center. And of course, for that to happen, those two data centers need to be in sync. And that's critical. Did that answer your question? >> Yeah, let me jump in here. Thank you again for that. I want to unpack some of those, and I want use the example of Couchbase Light, which, as the name implies, a mobile version of Couchbase. I'm interested in a number of things that you said. You talked about, in some cases, you want to get data from the most proximate location. Is there a some kind of metadata intelligence that you have access to? I'm interested in how you do the synchronization. How do you deal with conflict resolution and recovery if something goes wrong? You're talking about distributed database challenges. How do you approach all that? >> Wow, great question. And probably one that I could occupy the entire session for, but I'll try and keep it brief and try and answer most of the points that you touched upon. So, we talked about distributed database and data sync. But here's the other challenge. A lot of these distributed locations can actually be disconnected. So, we've just exacerbated this whole notion of data sync. And that's what we call offline first, not just we call, what is typically referred to as offline first sync. But the ability for an application to run in a completely disconnected mode, but then when there is network connectivity, the data is synced back to the backend data servers. In order for this to happen, you need a sync protocol (indistinct). Since you asked in the context of Couchbase, our sync protocol, it's a web sockets, extremely lightweight data synchronization protocol that's resilient to network disruption. So, what this means is I could have hundreds of thousands of clients that are connected to a data center, and they could be at various stages of disconnect. And you have a field application, and then you are veering in and out of pockets of network connectivity, so network is disrupted, and then network connectivity is restored. Our sync protocol has got a built-in checkpoint mechanism that allows the two replicating points to have a handshake of what is the previous sync point, and only data from that previous sync point is sent to that specific client. And in order to achieve that you mentioned Couchbase Light, which is, of course, our embedded database for mobile, desktop and any embedded platform. But the one that handles the data synchronization is our Sync Gateway. So, we got a component, Sync Gateway, that sits with our Couchbase server, and that's responsible for securely syncing the data and implementing this protocol with Couchbase Light. You talked about conflict resolution. And it's great that you mentioned that. Because when it comes to data sync, a lot of times folks think, "Oh well, how hard can that be?" I mean, you request for some data, and you pull down the a data, and that's great. And that's the happy path. When all of the clients are connected, when there is reliable network connectivity, that's great. But we are, of course, talking about unreliable network connectivity and resiliency to network disruptions. And also the fact that you have lots of concurrently connected clients, all of them potentially updating the same piece of data. That's when you have a conflict, When two or more clients are updating the same, clients or writers. You could have the writes coming in from the clients. You could have the writes coming in from the backend systems. Either way, multiple writers do the same piece of data. That's when you have conflicts. Now, when it comes to, so, a little bit to explain how conflict resolution is handled within our data sync protocol in Couchbase, it would help to understand a little bit about what kind of database we are, how is data itself stored within our database. So, Couchbase Light is a NoSql JSON document store, which means everything is stored as JSON documents. And so every time there is a write, an update to a document, let's say you start with an initial version of the document, the document is created. Every time there is a mutation to a document, you have a new revision to that document. So, as you build in more rights or more mutations to that document, you build out what's called a revision tree. And so when does a conflict happen? Conflict happens when there is a branch in the tree. So, you've got two writers, writing to the same revision, then you get a branch, and that's what is a conflict. We have a way of detecting those conflicts automatically. That's conflict detection. So, now we know there's a conflict, but we have to resolve it. And within Couchbase, you have two options. You don't have to do anything about it. The system has built-in automatic conflict resolution heuristics built in. So, it's going to check, pick a winning revision. And so we use a bunch of criteria, and we pick a winning revision. So, if two writers are updating the same revision of the document, version of the document, we pick a winner. But then that seemed to work from our experience, 80% of the use cases. But then for the remaining 20%, applications would like to have more control over how the winner of the conflict is picked. And for that, applications can implement a custom conflict resolver. So, we'll automatically detect the conflicting revisions and send these conflicting revisions over to the application via a callback, and the application has access to the entire document body of the two revisions and can use whatever criteria needs to merge >> So, that's policy based in that example? >> Yes. >> Yeah, yeah, okay. >> So you can have user policy based, or you can have the automatic heuristics. >> Okay, I got to wrap because we're out of time, but I want to run this scenario by you. One of the risks to the Supercloud Nirvana that we always talk about is this notion of a new architecture emerging at the edge, far edge really, 'cause they're highly-distributed environments. They're low power, tons of data. And this idea of AI inferencing at the edge, a lot of the AI today is done in modeling in the cloud. You think about ARM processors in these new low-cost devices and massive processing power eventually overwhelming the economics. And then that's seeping back into the enterprise and disrupting it. Now, you still get the problem of federated governance and security, and that's probably going to be more centralized slash federated. But, in one minute, do you see that AI inferencing real-time taking off at the edge? Where is that on the S-curve? >> Oh, absolutely right. When it comes to IoT applications, it's all about massive volumes of data generated at the edge. You talked about the economics doesn't add up. Now you need to actually, the data needs to be actioned at some point. And if you have to transfer all of that over the internet for analysis, the responsiveness, you're going to lose that. You're not going to get that real-time responsiveness and availability. The edge is the perfect location. And a lot of this data is temporal in nature. So, you don't want that to be sent back to the cloud for long-term persistence, but instead you want that to be actioned close as possible to the source itself. And when you talk about, there are, of course, the really small microcontrollers and so on. Even there, you can actually have some local processing done, like tiny ML models, but then mobile devices, when you talk about those, as you're very well aware, these are extremely capable. They're capable of running neural, they have neural network processors. And so they can do a lot of processing locally itself. But then when you want to have an aggregated view within the edge, you want to process that data in an IoT gateway and only send the aggregated data back to the cloud for long-term analytics and persistence. >> Yeah, this is something we're watching, and I think could be highly disruptive, and it's hard to predict. Priya, I got to go. Thanks so much for coming on the "theCube." Really appreciate your time. >> Yeah, thank you. >> All right, you're watching "Supercloud 22." We'll be right back right after this short break. (upbeat music)

(upbeat music) >> Once a staid and boring business dominated by IBM, Oracle, and at the time newcomer Microsoft, along with a handful of wannabes, the database business has exploded in the past decade and has become a staple of financial excellence, customer experience, analytic advantage, competitive strategy, growth initiatives, visualizations, not to mention compliance, security, privacy and dozens of other important use cases and initiatives. And on the vendor's side of the house, we've seen the rapid ascendancy of cloud databases. Most notably from Snowflake, whose massive raises leading up to its IPO in late 2020 sparked a spate of interest and VC investment in the separation of compute and storage and all that elastic resource stuff in the cloud. The company joined AWS, Azure and Google to popularize cloud databases, which have become a linchpin of competitive strategies for technology suppliers. And if I get you to put your data in my database and in my cloud, and I keep innovating, I'm going to build a moat and achieve a hugely attractive lifetime customer value in a really amazing marginal economics dynamic that is going to fund my future. And I'll be able to sell other adjacent services, not just compute and storage, but machine learning and inference and training and all kinds of stuff, dozens of lucrative cloud offerings. Meanwhile, the database leader, Oracle has invested massive amounts of money to maintain its lead. It's building on its position as the king of mission critical workloads and making typical Oracle like claims against the competition. Most were recently just yesterday with another announcement around MySQL HeatWave. An extension of MySQL that is compatible with on-premises MySQLs and is setting new standards in price performance. We're seeing a dramatic divergence in strategies across the database spectrum. On the far left, we see Amazon with more than a dozen database offerings each with its own API and primitives. AWS is taking a right tool for the right job approach, often building on open source platforms and creating services that it offers to customers to solve very specific problems for developers. And on the other side of the line, we see Oracle, which is taking the Swiss Army Knife approach, converging database functionality, enabling analytic and transactional workloads to run in the same data store, eliminating the need to ETL, at the same time adding capabilities into its platform like automation and machine learning. Welcome to this database Power Panel. My name is Dave Vellante, and I'm so excited to bring together some of the most respected industry analyst in the community. Today we're going to assess what's happening in the market. We're going to dig into the competitive landscape and explore the future of database and database platforms and decode what it means to customers. Let me take a moment to welcome our guest analyst today. Matt Kimball is a vice president and principal analysts at Moor Insights and Strategy, Matt. He knows products, he knows industry, he's got real world IT expertise, and he's got all the angles 25 plus years of experience in all kinds of great background. Matt, welcome. Thanks very much for coming on theCUBE. Holgar Mueller, friend of theCUBE, vice president and principal analyst at Constellation Research in depth knowledge on applications, application development, knows developers. He's worked at SAP and Oracle. And then Bob Evans is Chief Content Officer and co-founder of the Acceleration Economy, founder and principle of Cloud Wars. Covers all kinds of industry topics and great insights. He's got awesome videos, these three minute hits. If you haven't seen 'em, checking them out, knows cloud companies, his Cloud Wars minutes are fantastic. And then of course, Marc Staimer is the founder of Dragon Slayer Research. A frequent contributor and guest analyst at Wikibon. He's got a wide ranging knowledge across IT products, knows technology really well, can go deep. And then of course, Ron Westfall, Senior Analyst and Director Research Director at Futurum Research, great all around product trends knowledge. Can take, you know, technical dives and really understands competitive angles, knows Redshift, Snowflake, and many others. Gents, thanks so much for taking the time to join us in theCube today. It's great to have you on, good to see you. >> Good to be here, thanks for having us. >> Thanks, Dave. >> All right, let's start with an around the horn and briefly, if each of you would describe, you know, anything I missed in your areas of expertise and then you answer the following question, how would you describe the state of the database, state of platform market today? Matt Kimball, please start. >> Oh, I hate going first, but that it's okay. How would I describe the world today? I would just in one sentence, I would say, I'm glad I'm not in IT anymore, right? So, you know, it is a complex and dangerous world out there. And I don't envy IT folks I'd have to support, you know, these modernization and transformation efforts that are going on within the enterprise. It used to be, you mentioned it, Dave, you would argue about IBM versus Oracle versus this newcomer in the database space called Microsoft. And don't forget Sybase back in the day, but you know, now it's not just, which SQL vendor am I going to go with? It's all of these different, divergent data types that have to be taken, they have to be merged together, synthesized. And somehow I have to do that cleanly and use this to drive strategic decisions for my business. That is not easy. So, you know, you have to look at it from the perspective of the business user. It's great for them because as a DevOps person, or as an analyst, I have so much flexibility and I have this thing called the cloud now where I can go get services immediately. As an IT person or a DBA, I am calling up prevention hotlines 24 hours a day, because I don't know how I'm going to be able to support the business. And as an Oracle or as an Oracle or a Microsoft or some of the cloud providers and cloud databases out there, I'm licking my chops because, you know, my market is expanding and expanding every day. >> Great, thank you for that, Matt. Holgar, how do you see the world these days? You always have a good perspective on things, share with us. >> Well, I think it's the best time to be in IT, I'm not sure what Matt is talking about. (laughing) It's easier than ever, right? The direction is going to cloud. Kubernetes has won, Google has the best AI for now, right? So things are easier than ever before. You made commitments for five plus years on hardware, networking and so on premise, and I got gray hair about worrying it was the wrong decision. No, just kidding. But you kind of both sides, just to be controversial, make it interesting, right. So yeah, no, I think the interesting thing specifically with databases, right? We have this big suite versus best of breed, right? Obviously innovation, like you mentioned with Snowflake and others happening in the cloud, the cloud vendors server, where to save of their databases. And then we have one of the few survivors of the old guard as Evans likes to call them is Oracle who's doing well, both their traditional database. And now, which is really interesting, remarkable from that because Oracle it was always the power of one, have one database, add more to it, make it what I call the universal database. And now this new HeatWave offering is coming and MySQL open source side. So they're getting the second (indistinct) right? So it's interesting that older players, traditional players who still are in the market are diversifying their offerings. Something we don't see so much from the traditional tools from Oracle on the Microsoft side or the IBM side these days. >> Great, thank you Holgar. Bob Evans, you've covered this business for a while. You've worked at, you know, a number of different outlets and companies and you cover the competition, how do you see things? >> Dave, you know, the other angle to look at this from is from the customer side, right? You got now CEOs who are any sort of business across all sorts of industries, and they understand that their future success is going to be dependent on their ability to become a digital company, to understand data, to use it the right way. So as you outline Dave, I think in your intro there, it is a fantastic time to be in the database business. And I think we've got a lot of new buyers and influencers coming in. They don't know all this history about IBM and Microsoft and Oracle and you know, whoever else. So I think they're going to take a long, hard look, Dave, at some of these results and who is able to help these companies not serve up the best technology, but who's going to be able to help their business move into the digital future. So it's a fascinating time now from every perspective. >> Great points, Bob. I mean, digital transformation has gone from buzzword to imperative. Mr. Staimer, how do you see things? >> I see things a little bit differently than my peers here in that I see the database market being segmented. There's all the different kinds of databases that people are looking at for different kinds of data, and then there is databases in the cloud. And so database as cloud service, I view very differently than databases because the traditional way of implementing a database is changing and it's changing rapidly. So one of the premises that you stated earlier on was that you viewed Oracle as a database company. I don't view Oracle as a database company anymore. I view Oracle as a cloud company that happens to have a significant expertise and specialty in databases, and they still sell database software in the traditional way, but ultimately they're a cloud company. So database cloud services from my point of view is a very distinct market from databases. >> Okay, well, you gave us some good meat on the bone to talk about that. Last but not least-- >> Dave did Marc, just say Oracle's a cloud company? >> Yeah. (laughing) Take away the database, it would be interesting to have that discussion, but let's let Ron jump in here. Ron, give us your take. >> That's a great segue. I think it's truly the era of the cloud database, that's something that's rising. And the key trends that come with it include for example, elastic scaling. That is the ability to scale on demand, to right size workloads according to customer requirements. And also I think it's going to increase the prioritization for high availability. That is the player who can provide the highest availability is going to have, I think, a great deal of success in this emerging market. And also I anticipate that there will be more consolidation across platforms in order to enable cost savings for customers, and that's something that's always going to be important. And I think we'll see more of that over the horizon. And then finally security, security will be more important than ever. We've seen a spike (indistinct), we certainly have seen geopolitical originated cybersecurity concerns. And as a result, I see database security becoming all the more important. >> Great, thank you. Okay, let me share some data with you guys. I'm going to throw this at you and see what you think. We have this awesome data partner called Enterprise Technology Research, ETR. They do these quarterly surveys and each period with dozens of industry segments, they track clients spending, customer spending. And this is the database, data warehouse sector okay so it's taxonomy, so it's not perfect, but it's a big kind of chunk. They essentially ask customers within a category and buy a specific vendor, you're spending more or less on the platform? And then they subtract the lesses from the mores and they derive a metric called net score. It's like NPS, it's a measure of spending velocity. It's more complicated and granular than that, but that's the basis and that's the vertical axis. The horizontal axis is what they call market share, it's not like IDC market share, it's just pervasiveness in the data set. And so there are a couple of things that stand out here and that we can use as reference point. The first is the momentum of Snowflake. They've been off the charts for many, many, for over two years now, anything above that dotted red line, that 40%, is considered by ETR to be highly elevated and Snowflake's even way above that. And I think it's probably not sustainable. We're going to see in the next April survey, next month from those guys, when it comes out. And then you see AWS and Microsoft, they're really pervasive on the horizontal axis and highly elevated, Google falls behind them. And then you got a number of well funded players. You got Cockroach Labs, Mongo, Redis, MariaDB, which of course is a fork on MySQL started almost as protest at Oracle when they acquired Sun and they got MySQL and you can see the number of others. Now Oracle who's the leading database player, despite what Marc Staimer says, we know, (laughs) and they're a cloud player (laughing) who happens to be a leading database player. They dominate in the mission critical space, we know that they're the king of that sector, but you can see here that they're kind of legacy, right? They've been around a long time, they get a big install base. So they don't have the spending momentum on the vertical axis. Now remember this is, just really this doesn't capture spending levels, so that understates Oracle but nonetheless. So it's not a complete picture like SAP for instance is not in here, no Hana. I think people are actually buying it, but it doesn't show up here, (laughs) but it does give an indication of momentum and presence. So Bob Evans, I'm going to start with you. You've commented on many of these companies, you know, what does this data tell you? >> Yeah, you know, Dave, I think all these compilations of things like that are interesting, and that folks at ETR do some good work, but I think as you said, it's a snapshot sort of a two-dimensional thing of a rapidly changing, three dimensional world. You know, the incidents at which some of these companies are mentioned versus the volume that happens. I think it's, you know, with Oracle and I'm not going to declare my religious affiliation, either as cloud company or database company, you know, they're all of those things and more, and I think some of our old language of how we classify companies is just not relevant anymore. But I want to ask too something in here, the autonomous database from Oracle, nobody else has done that. So either Oracle is crazy, they've tried out a technology that nobody other than them is interested in, or they're onto something that nobody else can match. So to me, Dave, within Oracle, trying to identify how they're doing there, I would watch autonomous database growth too, because right, it's either going to be a big plan and it breaks through, or it's going to be caught behind. And the Snowflake phenomenon as you mentioned, that is a rare, rare bird who comes up and can grow 100% at a billion dollar revenue level like that. So now they've had a chance to come in, scare the crap out of everybody, rock the market with something totally new, the data cloud. Will the bigger companies be able to catch up and offer a compelling alternative, or is Snowflake going to continue to be this outlier. It's a fascinating time. >> Really, interesting points there. Holgar, I want to ask you, I mean, I've talked to certainly I'm sure you guys have too, the founders of Snowflake that came out of Oracle and they actually, they don't apologize. They say, "Hey, we not going to do all that complicated stuff that Oracle does, we were trying to keep it real simple." But at the same time, you know, they don't do sophisticated workload management. They don't do complex joints. They're kind of relying on the ecosystems. So when you look at the data like this and the various momentums, and we talked about the diverging strategies, what does this say to you? >> Well, it is a great point. And I think Snowflake is an example how the cloud can turbo charge a well understood concept in this case, the data warehouse, right? You move that and you find steroids and you see like for some players who've been big in data warehouse, like Sentara Data, as an example, here in San Diego, what could have been for them right in that part. The interesting thing, the problem though is the cloud hides a lot of complexity too, which you can scale really well as you attract lots of customers to go there. And you don't have to build things like what Bob said, right? One of the fascinating things, right, nobody's answering Oracle on the autonomous database. I don't think is that they cannot, they just have different priorities or the database is not such a priority. I would dare to say that it's for IBM and Microsoft right now at the moment. And the cloud vendors, you just hide that right through scripts and through scale because you support thousands of customers and you can deal with a little more complexity, right? It's not against them. Whereas if you have to run it yourself, very different story, right? You want to have the autonomous parts, you want to have the powerful tools to do things. >> Thank you. And so Matt, I want to go to you, you've set up front, you know, it's just complicated if you're in IT, it's a complicated situation and you've been on the customer side. And if you're a buyer, it's obviously, it's like Holgar said, "Cloud's supposed to make this stuff easier, but the simpler it gets the more complicated gets." So where do you place your bets? Or I guess more importantly, how do you decide where to place your bets? >> Yeah, it's a good question. And to what Bob and Holgar said, you know, the around autonomous database, I think, you know, part of, as I, you know, play kind of armchair psychologist, if you will, corporate psychologists, I look at what Oracle is doing and, you know, databases where they've made their mark and it's kind of, that's their strong position, right? So it makes sense if you're making an entry into this cloud and you really want to kind of build momentum, you go with what you're good at, right? So that's kind of the strength of Oracle. Let's put a lot of focus on that. They do a lot more than database, don't get me wrong, but you know, I'm going to short my strength and then kind of pivot from there. With regards to, you know, what IT looks at and what I would look at you know as an IT director or somebody who is, you know, trying to consume services from these different cloud providers. First and foremost, I go with what I know, right? Let's not forget IT is a conservative group. And when we look at, you know, all the different permutations of database types out there, SQL, NoSQL, all the different types of NoSQL, those are largely being deployed by business users that are looking for agility or businesses that are looking for agility. You know, the reason why MongoDB is so popular is because of DevOps, right? It's a great platform to develop on and that's where it kind of gained its traction. But as an IT person, I want to go with what I know, where my muscle memory is, and that's my first position. And so as I evaluate different cloud service providers and cloud databases, I look for, you know, what I know and what I've invested in and where my muscle memory is. Is there enough there and do I have enough belief that that company or that service is going to be able to take me to, you know, where I see my organization in five years from a data management perspective, from a business perspective, are they going to be there? And if they are, then I'm a little bit more willing to make that investment, but it is, you know, if I'm kind of going in this blind or if I'm cloud native, you know, that's where the Snowflakes of the world become very attractive to me. >> Thank you. So Marc, I asked Andy Jackson in theCube one time, you have all these, you know, data stores and different APIs and primitives and you know, very granular, what's the strategy there? And he said, "Hey, that allows us as the market changes, it allows us to be more flexible. If we start building abstractions layers, it's harder for us." I think also it was not a good time to market advantage, but let me ask you, I described earlier on that spectrum from AWS to Oracle. We just saw yesterday, Oracle announced, I think the third major enhancement in like 15 months to MySQL HeatWave, what do you make of that announcement? How do you think it impacts the competitive landscape, particularly as it relates to, you know, converging transaction and analytics, eliminating ELT, I know you have some thoughts on this. >> So let me back up for a second and defend my cloud statement about Oracle for a moment. (laughing) AWS did a great job in developing the cloud market in general and everything in the cloud market. I mean, I give them lots of kudos on that. And a lot of what they did is they took open source software and they rent it to people who use their cloud. So I give 'em lots of credit, they dominate the market. Oracle was late to the cloud market. In fact, they actually poo-pooed it initially, if you look at some of Larry Ellison's statements, they said, "Oh, it's never going to take off." And then they did 180 turn, and they said, "Oh, we're going to embrace the cloud." And they really have, but when you're late to a market, you've got to be compelling. And this ties into the announcement yesterday, but let's deal with this compelling. To be compelling from a user point of view, you got to be twice as fast, offer twice as much functionality, at half the cost. That's generally what compelling is that you're going to capture market share from the leaders who established the market. It's very difficult to capture market share in a new market for yourself. And you're right. I mean, Bob was correct on this and Holgar and Matt in which you look at Oracle, and they did a great job of leveraging their database to move into this market, give 'em lots of kudos for that too. But yesterday they announced, as you said, the third innovation release and the pace is just amazing of what they're doing on these releases on HeatWave that ties together initially MySQL with an integrated builtin analytics engine, so a data warehouse built in. And then they added automation with autopilot, and now they've added machine learning to it, and it's all in the same service. It's not something you can buy and put on your premise unless you buy their cloud customers stuff. But generally it's a cloud offering, so it's compellingly better as far as the integration. You don't buy multiple services, you buy one and it's lower cost than any of the other services, but more importantly, it's faster, which again, give 'em credit for, they have more integration of a product. They can tie things together in a way that nobody else does. There's no additional services, ETL services like Glue and AWS. So from that perspective, they're getting better performance, fewer services, lower cost. Hmm, they're aiming at the compelling side again. So from a customer point of view it's compelling. Matt, you wanted to say something there. >> Yeah, I want to kind of, on what you just said there Marc, and this is something I've found really interesting, you know. The traditional way that you look at software and, you know, purchasing software and IT is, you look at either best of breed solutions and you have to work on the backend to integrate them all and make them all work well. And generally, you know, the big hit against the, you know, we have one integrated offering is that, you lose capability or you lose depth of features, right. And to what you were saying, you know, that's the thing I found interesting about what Oracle is doing is they're building in depth as they kind of, you know, build that service. It's not like you're losing a lot of capabilities, because you're going to one integrated service versus having to use A versus B versus C, and I love that idea. >> You're right. Yeah, not only you're not losing, but you're gaining functionality that you can't get by integrating a lot of these. I mean, I can take Snowflake and integrate it in with machine learning, but I also have to integrate in with a transactional database. So I've got to have connectors between all of this, which means I'm adding time. And what it comes down to at the end of the day is expertise, effort, time, and cost. And so what I see the difference from the Oracle announcements is they're aiming at reducing all of that by increasing performance as well. Correct me if I'm wrong on that but that's what I saw at the announcement yesterday. >> You know, Marc, one thing though Marc, it's funny you say that because I started out saying, you know, I'm glad I'm not 19 anymore. And the reason is because of exactly what you said, it's almost like there's a pseudo level of witchcraft that's required to support the modern data environment right in the enterprise. And I need simpler faster, better. That's what I need, you know, I am no longer wearing pocket protectors. I have turned from, you know, break, fix kind of person, to you know, business consultant. And I need that point and click simplicity, but I can't sacrifice, you know, a depth of features of functionality on the backend as I play that consultancy role. >> So, Ron, I want to bring in Ron, you know, it's funny. So Matt, you mentioned Mongo, I often and say, if Oracle mentions you, you're on the map. We saw them yesterday Ron, (laughing) they hammered RedShifts auto ML, they took swipes at Snowflake, a little bit of BigQuery. What were your thoughts on that? Do you agree with what these guys are saying in terms of HeatWaves capabilities? >> Yes, Dave, I think that's an excellent question. And fundamentally I do agree. And the question is why, and I think it's important to know that all of the Oracle data is backed by the fact that they're using benchmarks. For example, all of the ML and all of the TPC benchmarks, including all the scripts, all the configs and all the detail are posted on GitHub. So anybody can look at these results and they're fully transparent and replicate themselves. If you don't agree with this data, then by all means challenge it. And we have not really seen that in all of the new updates in HeatWave over the last 15 months. And as a result, when it comes to these, you know, fundamentals in looking at the competitive landscape, which I think gives validity to outcomes such as Oracle being able to deliver 4.8 times better price performance than Redshift. As well as for example, 14.4 better price performance than Snowflake, and also 12.9 better price performance than BigQuery. And so that is, you know, looking at the quantitative side of things. But again, I think, you know, to Marc's point and to Matt's point, there are also qualitative aspects that clearly differentiate the Oracle proposition, from my perspective. For example now the MySQL HeatWave ML capabilities are native, they're built in, and they also support things such as completion criteria. And as a result, that enables them to show that hey, when you're using Redshift ML for example, you're having to also use their SageMaker tool and it's running on a meter. And so, you know, nobody really wants to be running on a meter when, you know, executing these incredibly complex tasks. And likewise, when it comes to Snowflake, they have to use a third party capability. They don't have the built in, it's not native. So the user, to the point that he's having to spend more time and it increases complexity to use auto ML capabilities across the Snowflake platform. And also, I think it also applies to other important features such as data sampling, for example, with the HeatWave ML, it's intelligent sampling that's being implemented. Whereas in contrast, we're seeing Redshift using random sampling. And again, Snowflake, you're having to use a third party library in order to achieve the same capabilities. So I think the differentiation is crystal clear. I think it definitely is refreshing. It's showing that this is where true value can be assigned. And if you don't agree with it, by all means challenge the data. >> Yeah, I want to come to the benchmarks in a minute. By the way, you know, the gentleman who's the Oracle's architect, he did a great job on the call yesterday explaining what you have to do. I thought that was quite impressive. But Bob, I know you follow the financials pretty closely and on the earnings call earlier this month, Ellison said that, "We're going to see HeatWave on AWS." And the skeptic in me said, oh, they must not be getting people to come to OCI. And then they, you remember this chart they showed yesterday that showed the growth of HeatWave on OCI. But of course there was no data on there, it was just sort of, you know, lines up and to the right. So what do you guys think of that? (Marc laughs) Does it signal Bob, desperation by Oracle that they can't get traction on OCI, or is it just really a smart tame expansion move? What do you think? >> Yeah, Dave, that's a great question. You know, along the way there, and you know, just inside of that was something that said Ellison said on earnings call that spoke to a different sort of philosophy or mindset, almost Marc, where he said, "We're going to make this multicloud," right? With a lot of their other cloud stuff, if you wanted to use any of Oracle's cloud software, you had to use Oracle's infrastructure, OCI, there was no other way out of it. But this one, but I thought it was a classic Ellison line. He said, "Well, we're making this available on AWS. We're making this available, you know, on Snowflake because we're going after those users. And once they see what can be done here." So he's looking at it, I guess you could say, it's a concession to customers because they want multi-cloud. The other way to look at it, it's a hunting expedition and it's one of those uniquely I think Oracle ways. He said up front, right, he doesn't say, "Well, there's a big market, there's a lot for everybody, we just want on our slice." Said, "No, we are going after Amazon, we're going after Redshift, we're going after Aurora. We're going after these users of Snowflake and so on." And I think it's really fairly refreshing these days to hear somebody say that, because now if I'm a buyer, I can look at that and say, you know, to Marc's point, "Do they measure up, do they crack that threshold ceiling? Or is this just going to be more pain than a few dollars savings is worth?" But you look at those numbers that Ron pointed out and that we all saw in that chart. I've never seen Dave, anything like that. In a substantive market, a new player coming in here, and being able to establish differences that are four, seven, eight, 10, 12 times better than competition. And as new buyers look at that, they're going to say, "What the hell are we doing paying, you know, five times more to get a poor result? What's going on here?" So I think this is going to rattle people and force a harder, closer look at what these alternatives are. >> I wonder if the guy, thank you. Let's just skip ahead of the benchmarks guys, bring up the next slide, let's skip ahead a little bit here, which talks to the benchmarks and the benchmarking if we can. You know, David Floyer, the sort of semiretired, you know, Wikibon analyst said, "Dave, this is going to force Amazon and others, Snowflake," he said, "To rethink actually how they architect databases." And this is kind of a compilation of some of the data that they shared. They went after Redshift mostly, (laughs) but also, you know, as I say, Snowflake, BigQuery. And, like I said, you can always tell which companies are doing well, 'cause Oracle will come after you, but they're on the radar here. (laughing) Holgar should we take this stuff seriously? I mean, or is it, you know, a grain salt? What are your thoughts here? >> I think you have to take it seriously. I mean, that's a great question, great point on that. Because like Ron said, "If there's a flaw in a benchmark, we know this database traditionally, right?" If anybody came up that, everybody will be, "Oh, you put the wrong benchmark, it wasn't audited right, let us do it again," and so on. We don't see this happening, right? So kudos to Oracle to be aggressive, differentiated, and seem to having impeccable benchmarks. But what we really see, I think in my view is that the classic and we can talk about this in 100 years, right? Is the suite versus best of breed, right? And the key question of the suite, because the suite's always slower, right? No matter at which level of the stack, you have the suite, then the best of breed that will come up with something new, use a cloud, put the data warehouse on steroids and so on. The important thing is that you have to assess as a buyer what is the speed of my suite vendor. And that's what you guys mentioned before as well, right? Marc said that and so on, "Like, this is a third release in one year of the HeatWave team, right?" So everybody in the database open source Marc, and there's so many MySQL spinoffs to certain point is put on shine on the speed of (indistinct) team, putting out fundamental changes. And the beauty of that is right, is so inherent to the Oracle value proposition. Larry's vision of building the IBM of the 21st century, right from the Silicon, from the chip all the way across the seven stacks to the click of the user. And that what makes the database what Rob was saying, "Tied to the OCI infrastructure," because designed for that, it runs uniquely better for that, that's why we see the cross connect to Microsoft. HeatWave so it's different, right? Because HeatWave runs on cheap hardware, right? Which is the breadth and butter 886 scale of any cloud provider, right? So Oracle probably needs it to scale OCI in a different category, not the expensive side, but also allow us to do what we said before, the multicloud capability, which ultimately CIOs really want, because data gravity is real, you want to operate where that is. If you have a fast, innovative offering, which gives you more functionality and the R and D speed is really impressive for the space, puts away bad results, then it's a good bet to look at. >> Yeah, so you're saying, that we versus best of breed. I just want to sort of play back then Marc a comment. That suite versus best of breed, there's always been that trade off. If I understand you Holgar you're saying that somehow Oracle has magically cut through that trade off and they're giving you the best of both. >> It's the developing velocity, right? The provision of important features, which matter to buyers of the suite vendor, eclipses the best of breed vendor, then the best of breed vendor is in the hell of a potential job. >> Yeah, go ahead Marc. >> Yeah and I want to add on what Holgar just said there. I mean the worst job in the data center is data movement, moving the data sucks. I don't care who you are, nobody likes it. You never get any kudos for doing it well, and you always get the ah craps, when things go wrong. So it's in- >> In the data center Marc all the time across data centers, across cloud. That's where the bleeding comes. >> It's right, you get beat up all the time. So nobody likes to move data, ever. So what you're looking at with what they announce with HeatWave and what I love about HeatWave is it doesn't matter when you started with it, you get all the additional features they announce it's part of the service, all the time. But they don't have to move any of the data. You want to analyze the data that's in your transactional, MySQL database, it's there. You want to do machine learning models, it's there, there's no data movement. The data movement is the key thing, and they just eliminate that, in so many ways. And the other thing I wanted to talk about is on the benchmarks. As great as those benchmarks are, they're really conservative 'cause they're underestimating the cost of that data movement. The ETLs, the other services, everything's left out. It's just comparing HeatWave, MySQL cloud service with HeatWave versus Redshift, not Redshift and Aurora and Glue, Redshift and Redshift ML and SageMaker, it's just Redshift. >> Yeah, so what you're saying is what Oracle's doing is saying, "Okay, we're going to run MySQL HeatWave benchmarks on analytics against Redshift, and then we're going to run 'em in transaction against Aurora." >> Right. >> But if you really had to look at what you would have to do with the ETL, you'd have to buy two different data stores and all the infrastructure around that, and that goes away so. >> Due to the nature of the competition, they're running narrow best of breed benchmarks. There is no suite level benchmark (Dave laughs) because they created something new. >> Well that's you're the earlier point they're beating best of breed with a suite. So that's, I guess to Floyer's earlier point, "That's going to shake things up." But I want to come back to Bob Evans, 'cause I want to tap your Cloud Wars mojo before we wrap. And line up the horses, you got AWS, you got Microsoft, Google and Oracle. Now they all own their own cloud. Snowflake, Mongo, Couchbase, Redis, Cockroach by the way they're all doing very well. They run in the cloud as do many others. I think you guys all saw the Andreessen, you know, commentary from Sarah Wang and company, to talk about the cost of goods sold impact of cloud. So owning your own cloud has to be an advantage because other guys like Snowflake have to pay cloud vendors and negotiate down versus having the whole enchilada, Safra Catz's dream. Bob, how do you think this is going to impact the market long term? >> Well, Dave, that's a great question about, you know, how this is all going to play out. If I could mention three things, one, Frank Slootman has done a fantastic job with Snowflake. Really good company before he got there, but since he's been there, the growth mindset, the discipline, the rigor and the phenomenon of what Snowflake has done has forced all these bigger companies to really accelerate what they're doing. And again, it's an example of how this intense competition makes all the different cloud vendors better and it provides enormous value to customers. Second thing I wanted to mention here was look at the Adam Selipsky effect at AWS, took over in the middle of May, and in Q2, Q3, Q4, AWS's growth rate accelerated. And in each of those three quotas, they grew faster than Microsoft's cloud, which has not happened in two or three years, so they're closing the gap on Microsoft. The third thing, Dave, in this, you know, incredibly intense competitive nature here, look at Larry Ellison, right? He's got his, you know, the product that for the last two or three years, he said, "It's going to help determine the future of the company, autonomous database." You would think he's the last person in the world who's going to bring in, you know, in some ways another database to think about there, but he has put, you know, his whole effort and energy behind this. The investments Oracle's made, he's riding this horse really hard. So it's not just a technology achievement, but it's also an investment priority for Oracle going forward. And I think it's going to form a lot of how they position themselves to this new breed of buyer with a new type of need and expectations from IT. So I just think the next two or three years are going to be fantastic for people who are lucky enough to get to do the sorts of things that we do. >> You know, it's a great point you made about AWS. Back in 2018 Q3, they were doing about 7.4 billion a quarter and they were growing in the mid forties. They dropped down to like 29% Q4, 2020, I'm looking at the data now. They popped back up last quarter, last reported quarter to 40%, that is 17.8 billion, so they more doubled and they accelerated their growth rate. (laughs) So maybe that pretends, people are concerned about Snowflake right now decelerating growth. You know, maybe that's going to be different. By the way, I think Snowflake has a different strategy, the whole data cloud thing, data sharing. They're not trying to necessarily take Oracle head on, which is going to make this next 10 years, really interesting. All right, we got to go, last question. 30 seconds or less, what can we expect from the future of data platforms? Matt, please start. >> I have to go first again? You're killing me, Dave. (laughing) In the next few years, I think you're going to see the major players continue to meet customers where they are, right. Every organization, every environment is, you know, kind of, we use these words bespoke in Snowflake, pardon the pun, but Snowflakes, right. But you know, they're all opinionated and unique and what's great as an IT person is, you know, there is a service for me regardless of where I am on my journey, in my data management journey. I think you're going to continue to see with regards specifically to Oracle, I think you're going to see the company continue along this path of being all things to all people, if you will, or all organizations without sacrificing, you know, kind of richness of features and sacrificing who they are, right. Look, they are the data kings, right? I mean, they've been a database leader for an awful long time. I don't see that going away any time soon and I love the innovative spirit they've brought in with HeatWave. >> All right, great thank you. Okay, 30 seconds, Holgar go. >> Yeah, I mean, the interesting thing that we see is really that trend to autonomous as Oracle calls or self-driving software, right? So the database will have to do more things than just store the data and support the DVA. It will have to show it can wide insights, the whole upside, it will be able to show to one machine learning. We haven't really talked about that. How in just exciting what kind of use case we can get of machine learning running real time on data as it changes, right? So, which is part of the E5 announcement, right? So we'll see more of that self-driving nature in the database space. And because you said we can promote it, right. Check out my report about HeatWave latest release where I post in oracle.com. >> Great, thank you for that. And Bob Evans, please. You're great at quick hits, hit us. >> Dave, thanks. I really enjoyed getting to hear everybody's opinion here today and I think what's going to happen too. I think there's a new generation of buyers, a new set of CXO influencers in here. And I think what Oracle's done with this, MySQL HeatWave, those benchmarks that Ron talked about so eloquently here that is going to become something that forces other companies, not just try to get incrementally better. I think we're going to see a massive new wave of innovation to try to play catch up. So I really take my hat off to Oracle's achievement from going to, push everybody to be better. >> Excellent. Marc Staimer, what do you say? >> Sure, I'm going to leverage off of something Matt said earlier, "Those companies that are going to develop faster, cheaper, simpler products that are going to solve customer problems, IT problems are the ones that are going to succeed, or the ones who are going to grow. The one who are just focused on the technology are going to fall by the wayside." So those who can solve more problems, do it more elegantly and do it for less money are going to do great. So Oracle's going down that path today, Snowflake's going down that path. They're trying to do more integration with third party, but as a result, aiming at that simpler, faster, cheaper mentality is where you're going to continue to see this market go. >> Amen brother Marc. >> Thank you, Ron Westfall, we'll give you the last word, bring us home. >> Well, thank you. And I'm loving it. I see a wave of innovation across the entire cloud database ecosystem and Oracle is fueling it. We are seeing it, with the native integration of auto ML capabilities, elastic scaling, lower entry price points, et cetera. And this is just going to be great news for buyers, but also developers and increased use of open APIs. And so I think that is really the key takeaways. Just we're going to see a lot of great innovation on the horizon here. >> Guys, fantastic insights, one of the best power panel as I've ever done. Love to have you back. Thanks so much for coming on today. >> Great job, Dave, thank you. >> All right, and thank you for watching. This is Dave Vellante for theCube and we'll see you next time. (soft music)

(upbeat music) >> Hey everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase. This is Data as Code, The Future of Enterprise Data and Analytics. This is also season two, episode two of our ongoing series with exciting partners from the AWS ecosystem who are here to talk with us about data and analytics. I'm your host, Lisa Martin. Two guests join me, one, a cube alumni. Venkat Venkataramani is here CEO & Co-Founder of Rockset. Good to see you again. And Doug Moore, VP of cloud platforms at Command Alkon. You're here to talk to me about how Command Alkon implemented real time analytics in just days with Rockset. Guys, welcome to the program. >> Thanks for having us. >> Yeah, great to be here. >> Doug, give us a little bit of a overview of Command Alkon, what type of business you are? what your mission is? That good stuff. >> Yeah, great. I'll pref it by saying I've been in this industry for only three years. The 30 years prior I was in financial services. So this was really exciting and eye opening. It actually plays into the story of how we met Rockset. So that's why I wanted to preface that. But Command Alkon is in the business, is in the what's called The Heavy Building Materials Industry. And I had never heard of it until I got here. But if you think about large projects like building buildings, cities, roads anything that requires concrete asphalt or just really big trucks, full of bulky materials that's the heavy building materials industry. So for over 40 years Command Alkon has been the north American leader in providing software to quarries and production facilities to help mine and load these materials and to produce them and then get them to the job site. So that's what our supply chain is, is from the quarry through the development of these materials, then out to the to a heavy building material job site. >> Got it, and now how historically in the past has the movement of construction materials been coordinated? What was that like before you guys came on the scene? >> You'll love this answer. So 'cause, again, it's like a step back in time. When I got here the people told me that we're trying to come up with the platform that there are 27 industries studied globally. And our industry is second to last in terms of automation which meant that literally everything is still being done with paper and a lot of paper. So when one of those, let's say material is developed, concrete asphalt is produced and then needs to get to the job site. They start by creating a five part printed ticket or delivery description that then goes to multiple parties. It ends up getting touched physically over 50 times for every delivery. And to give you some idea what kind of scale it is there are over 330 million of these type deliveries in north America every year. So it's really a lot of favor and a lot of manual work. So that was the state of really where we were. And obviously there are compelling reasons certainly today but even 3, 4, 5 years ago to automate that and digitize it. >> Wow, tremendous potential to go nowhere but up with the amount of paper, the lack of, of automation. So, you guys Command Alkon built a platform, a cloud software construction software platform. Talk to me of about that. Why you built it, what was the compelling event? I mean, I think you've kind of already explained the compelling event of all the paper but give us a little bit more context. >> Yeah. That was the original. And then we'll get into what happened two years ago which has made it even more compelling but essentially with everything on premises there's really in a huge amount of inefficiency. So, people have heard the enormous numbers that it takes to build up a highway or a really large construction project. And a lot of that is tied up in these inefficiencies. So we felt like with our significant presence in this market, that if we could figure out how to automate getting this data into the cloud so that at least the partners in the supply chain could begin sharing information. That's not on paper a little bit closer to real time that we could make has an impact on everything from the timing it takes to do a project to even the amount of carbon dioxide that's admitted, for example from trucks running around and being delayed and not being coordinated well. >> So you built the connect platform you started on Amazon DynamoDB and ran into some performance challenges. Talk to us about the, some of those performance bottlenecks and how you found Venkat and Rockset. >> So from the beginning, we were fortunate, if you start building a cloud three years ago you're you have a lot of opportunity to use some of the what we call more fully managed or serverless offerings from Amazon and all the cloud vendors have them but Amazon is the one we're most familiar with throughout the past 10 years. So we went head first into saying, we're going to do everything we can to not manage infrastructure ourselves. So we can really focus on solving this problem efficiently. And it paid off great. And so we chose dynamo as our primary database and it still was a great decision. We have obviously hundreds of millions of billions of these data points in dynamo. And it's great from a transactional perspective, but at some point you need to get the data back out. And what plays into the story of the beginning when I came here with no background basically in this industry, is that, and as did most of the other people on my team, we weren't really sure what questions were going to be asked of the data. And that's super, super important with a NoSQL database like dynamo. You sort of have to know in advance what those usage patterns are going to be and what people are going to want to get back out of it. And that's what really began to strain us on both performance and just availability of information. >> Got it. Venkat, let's bring you into the conversation. Talk to me about some of the challenges that Doug articulated the, is industry with such little automation so much paper. Are you finding that still out there for in quite a few industries that really have nowhere to go but up? >> I think that's a very good point. We talk about digital transformation 2.0 as like this abstract thing. And then you meet like disruptors and innovators like Doug, and you realize how much impact, it has on the real world. But now it's not just about disrupting, and digitizing all of these records but doing it at a faster pace than ever before, right. I think this is really what digital transformation in the cloud really enable tools you do that, a small team in a, with a very very big mission and responsibility like what Doug team have been, shepherding here. They're able to move very, very, very fast, to be able to kind of accelerate this. And, they're not only on the forefront of digitizing and transforming a very big, paper-heavy kind of process, but real-time analytics and real time reporting is a requirement, right? Nobody's wondering where is my supply chain three days ago? Are my, one of the most important thing in heavy construction is to keep running on a schedule. If you fall behind, there's no way to catch up because there's so many things that falls apart. Now, how do you make sure you don't fall behind, realtime analytics and realtime reporting on how many trucks are supposed to be delivered today? Halfway through the day, are they on track? Are they getting behind? And all of those things is not just able to manage the data but also be able to get reporting and analytics on that is a extremely important aspect of this. So this is like a combination of digital transformation happening in the cloud in realtime and realtime analytics being in the forefront of it. And so we are very, very happy to partner with digital disruptors like Doug and his team to be part of this movement. >> Doug, as Venkat mentioned, access to real time data is a requirement that is just simple truth these days. I'm just curious, compelling event wise was COVID and accelerator? 'Cause we all know of the supply chain challenges that we're all facing in one way or the other, was that part of the compelling event that had you guys go and say, we want to do DynamoDB plus Rockset? >> Yeah, that is a fantastic question. In fact, more so than you can imagine. So anytime you come into an industry and you're going to try to completely change or revolutionize the way it operates it takes a long time to get the message out. Sometimes years, I remember in insurance it took almost 10 years really to get that message out and get great adoption and then COVID came along. And when COVID came along, we all of a sudden had a situation where drivers and the foreman on the job site didn't want to exchange the paperwork. I heard one story of a driver taping the ticket for signature to the foreman on a broomstick and putting it out his windows so that he didn't get too close. It really was that dramatic. And again, this is the early days and no one really has any idea what's happening and we're all working from home. So we launched, we saw that as an opportunity to really help people solve that problem and understand more what this transformation would mean in the long term. So we launched internally what we called Project Lemonade obviously from, make lemonade out of lemons, that's the situation that we were in and we immediately made some enhancements to a mobile app and then launched that to the field. So that basically there's now a digital acceptance capability where the driver can just stay in the vehicle and the foreman can be anywhere, look at the material say it's acceptable for delivery and go from there. So yeah, it made a, it actually immediately caused many of our customers hundreds to begin, to want to push their data to the cloud for that reason just to take advantage of that one capability >> Project lemonade, sounds like it's made a lot of lemonade out of a lot of lemons. Can you comment Doug on kind of the larger trend of real time analytics and logistics? >> Yeah, obviously, and this is something I didn't think about much either not knowing anything about concrete other than it was in my driveway before I got here. And that it's a perishable product and you've got that basically no more than about an hour and a half from the time you mix it, put it in the drum and get it to the job site and pour it. And then the next one has to come behind it. And I remember I, the trend is that we can't really do that on paper anymore and stay on top of what has to be done we'll get into the field. So a foreman, I recall saying that when you're in the field waiting on delivery, that you have people standing around and preparing the site ready to make a pour that two minutes is an eternity. And so, working a real time is all always a controversial word because it means something different to anyone, but that gave it real, a real clarity to mean, what it really meant to have real time analytics and how we are doing and where are my vehicles and how is this job performing today? And I think that a lot of people are still trying to figure out how to do that. And fortunately, we found a great tool set that's allowing us to do that at scale. Thankfully, for Rockset primarily. >> Venkat talk about it from your perspective the larger trend of real time analytics not just in logistics, but in other key industries. >> Yeah. I think we're seeing this across the board. I think, whether, even we see a huge trend even within an enterprise different teams from the marketing team to the support teams to more and more business operations team to the security team, really moving more and more of their use cases from real time. So we see this, the industries that are the innovators and the pioneers here are the ones for whom real times that requirement like Doug and his team here or where, if it is all news, it's no news, it's useless, right? But I think even within, across all industries, whether it is, gaming whether it is, FinTech, Bino related companies, e-learning platforms, so across, ed tech and so many different platforms, there is always this need for business operations. Some, certain aspects certain teams within large organizations to, have to tell me how to win the game and not like, play Monday morning quarterback after the game is over. >> Right, Doug, let's go back at you, I'm curious with connects, have you been able to scale the platform since you integrated with Rockset? Talk to us about some of the outcomes that you've achieved so far? >> Yeah, we have, and of course we knew and we made our database selection with dynamo that it really doesn't have a top end in terms of how much information that we can throw at it. But that's very, very challenging when it comes to using that information from reporting. But we've found the same thing as we've scaled the analytics side with Rockset indexing and searching of that database. So the scale in terms of the number of customers and the amount of data we've been able to take on has been, not been a problem. And honestly, for the first time in my career, I can say that we've always had to add people every time we add a certain number of customers. And that has absolutely not been the case with this platform. >> Well, and I imagine the team that you do have is far more, sorry Venkat, far more strategic and able to focus on bigger projects. >> It, is, and, you've amazed at, I mean Venkat hit on a couple of points that it's in terms of the adoption of analytics. What we found is that we are as big a customer of this analytic engine as our customers are because our marketing team and our sales team are always coming to us. Well how many customers are doing this? How many partners are connected in this way? Which feature flags are turned on the platform? And the way this works is all data that we push into the platform is automatically just indexed and ready for reporting analytics. So we really it's no additional ad of work, to answer these questions, which is really been phenomenal. >> I think the thing I want to add here is the speed at which they were able to build a scalable solution and also how little, operational and administrative overhead that it has cost of their teams, right. I think, this is again, realtime analytics. If you go and ask hundred people, do you want fast analytics on realtime data or slow analytics on scale data, people, no one would say give me slow and scale. So, I think it goes back to again our fundamental pieces that you have to remove all the cost and complexity barriers for realtime analytics to be the new default, right? Today companies try to get away with batch and the pioneers and the innovators are forced to solve, I know, kind of like address some of these realtime analytics challenges. I think with the platforms like the realtime analytics platform, like Rockset, we want to completely flip it on its head. You can do everything in real time. And there may be some extreme situations where you're dealing with like, hundreds of petabytes of data and you just need an analyst to generate like, quarterly reports out of that, go ahead and use some really, really good batch base system but you should be able to get anything, and everything you want without additional cost or complexity, in real time. That is really the vision. That is what we are really enabling here. >> Venkat, I want to also get your perspective and Doug I'd like your perspective on this as well but that is the role of cloud native and serverless technologies in digital disruption. And what do you see there? >> Yeah, I think it's huge. I think, again and again, every customer, and we meet, Command Alkon and Doug and his team is a great example of this where they really want to spend as much time and energies and calories that they have to, help their business, right? Like what, are we accomplishing trying to accomplish as a business? How do we enable, how do we build better products? How do we grow revenue? How do we eliminate risk that is inherent in the business? And that is really where they want to spend all of their energy not trying to like, install some backend software, administer build IDL pipelines and so on and so forth. And so, doing serverless on the compute side of that things like AWS lambda does and what have you. And, it's a very important innovation but that isn't, complete the story or your data stack also have to become serverless. And, that is really the vision with Rockset that your entire realtime analytics stack can be operating and managing. It could be as simple as managing a serverless stack for your compute environments like your APS servers and what have you. And so I think that is going to be a that is for here to stay. This is a path towards simplicity and simplicity scales really, really well, right? Complexity will always be the killer that'll limit, how far you can use this solution and how many problems can you solve with that solution? So, simplicity is a very, very important aspect here. And serverless helps you, deliver that. >> And Doug your thoughts on cloud native and serverless in terms of digital disruption >> Great point, and there are two parts to the scalability part. The second one is the one that's more subtle unless you're in charge of the budget. And that is, with enough effort and enough money that you can make almost any technology scale whether it's multiple copies of it, it may take a long time to get there but you can get there with most technologies but what is least scalable, at least that I as I see that this industry is the people, everybody knows we have a talent shortage and these other ways of getting the real time analytics and scaling infrastructure for compute and database storage, it really takes a highly skilled set of resources. And the more your company grows, the more of those you need. And that is what we really can't find. And that's actually what drove our team in our last industry to even go this way we reached a point where our growth was limited by the people we could find. And so we really wanted to break out of that. So now we had the best of both scalable people because we don't have to scale them and scalable technology. >> Excellent. The best of both worlds. Isn't it great when those two things come together? Gentlemen, thank you so much for joining me on "theCUBE" today. Talking about what Rockset and Command Alkon are doing together better together what you're enabling from a supply chain digitization perspective. We appreciate your insights. >> Great. Thank you. >> Thanks, Lisa. Thanks for having us. >> My pleasure. For Doug Moore and Venkat Venkatramani, I'm Lisa Martin. Keep it right here for more coverage of "theCUBE", your leader in high tech event coverage. (upbeat music)

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

>> Welcome to the Cubes coverage of Couchbase connect online 2021. I'm Lisa Martin. I have a first timer here on the cube Priya Rajgopal, the director of product management from Couchbase joins me next. Priya, welcome to the program. >> Thank you, Lisa. Thanks for having me here and glad to be here. First timer. So really excited. >> Yeah. Well, we'll make sure that you're going to have fun. We're going to talk about edge computing and what I'd love to get is your perspectives on what's going on and the evolution in the last 18 months. I'm sure so much has changed, but talk to me about edge computing what's going on >> Sure. >> Sure. there's 6 lot of literature on there and different there's a lot of literature on there and different interpretations and the way we see it at Couchbase, it's a distributed computing paradigm, that brings compute and storage to the edge. And what is the edge? The edge is the location where data is generated or consumed. And so the edge, again, the taxonomy is varied, but it's really a continuum. So it's not a thing, right? So it's a location. So it could be a single device or it could actually be a data center. And so it's getting a lot of traction with the proliferation of a lot of applications around AR, VR, IOT, and mobile devices and mobile applications. Because it delivers on the promise of ultra low latency access to data because you know, the edges where the data is generated and consumed, data privacy, governance, residency to network disruptions, low bandwidth usage. So to your question on how does mobile fit into the space of edge computing? In my view, mobile application, mobile devices are a classic example of edge computing because think about mobile devices, right, they're generating data, they're processing data, applications are processing data right there on the devices. You can store data in offline mode on those devices. So it is a classic edge device. And of course, the data doesn't have to be generated on the device itself. There's mobile applications could sort of be gateways to other external like variables for instance, and other IOT devices, which can connect to these mobile applications. And these mobile applications could process that data. >> Got it. So thank you for sharing Couchbase's definition. And it's a good point to do that as so many times, there's so many different terms and solutions and technologies that can be interpreted and explained many different ways. Let's now go through Couchbase's role in edge computing. Help the audience understand where you fit into that. >> Sure. So if you recap the definition, right? edge computing is all about storage and compute to the edge. So clearly a database has a key role to play in this model, Right? Or in this paradigm, because when you think about it, a classic application architecture, you've got three tiers, you've got an application tier, it includes your business logic, some of the UI elements, that's optional. You've got your database tier, which drives the application, Does the obligation needs data? It's driven by the database tier. And then you've got the infrastructure tier, that includes your network storage compute. Now, when you're talking about an edge computing architecture, you're talking about distributing all these three tiers. Your application tier, a database tier, as well as your infrastructure tier and a Couchbase is a fully distributed, no sequel database solution. So it fits in right into this paradigm of edge computing. Now, when we are talking about distributing our storage, that's just one aspect of it, right? You have to distribute it to these edge devices. You may have to distribute it to edge data centers, You need to be able to sync or move data between these You need to be able to sync or move data between these distributed cloud environments, right? So data synchronization is a key component of the tier of of edge computing architectures. And then finally, there's data management. That's all about enforcement of policies, when it comes to data privacy, you know what, data needs to be resident at the edge, what data needs to be filtered, what needs to be aggregated? what data needs to be filtered, what needs to be aggregated? So you need a solution that can provide those hooks that allows you to enforce those policies. So, a database like Couchbase has a critical role to play solution that can be deployed in the cloud, or it can be deployed at the edge. And again, or it can be deployed at the edge. And again, the edge could be a data center or it could be a device. So what about device? We have an embedded database solution for mobile desktop and embedded platforms. And then of course, data movement, comprehensive data synchronization technology. comprehensive data synchronization technology. >> Let's go through specifically some of the database capabilities that are required for businesses in any industry to be successful in edge computing. >> Sure, absolutely. Right. to do sort of reiterate or reinforce the three concepts, right? Data storage, data movement, data management, right? And Couchbase technology because that the stack consists of couchbase server, our flagship fully distributed, no sequel data platform. It can be containerized. It can be deployed in any public or private cloud. It could be deployed at the edge cloud. And then you've a Couchbase lite. Again, no sequel embedded database full featured, right? Anything that you can do with a standalone database, you can do it with the embedded database. Now you can embed that within your mobile applications, within your other embedded applications or desktop applications. And that's great, right? That's the data storage part of it. And, and that's one part of it, but what about the data movement? And that's where you got a data synchronization technology where we facilitate a high throughput, high performance, highly scalable data synchronization, between the edge and the cloud. And of course, as I mentioned, data management is a critical aspect of all this, right? And so the synchronization technology has got components that allow you to set filters, access control policies. And there's a lot of hooks when it comes to data governance. So for instance, if an edge goes out of commission, or if there's a security breach, for instance, you want to isolate the edge, you can do that. The data that was previously synchronized to that edge, you want to be able to poach that data. So we have options the automatically poach the data, if the device is no longer in the hands of the right recipient for instance. those are the critical aspects. Of course, the overarching theme is security, right? And, that goes hand-in-hand with encryption of the data at rest, encryption of the data in motion, then authentication, authorization, access control. >> Security is even more important in given the events of the last 18 months where we've seen a massive rise in ransom, where we've seen a huge rise in DDoS attacks. Let's, double-click more on the security aspect of what Couchbase is delivering. >> Sure, absolutely. So when it comes to security of data at rest, right, even when the Couchbase lite, which is our embedded device, your entire database is encrypted AES-256 data encryption, and then data, when it leaves the device through our data synchronization protocol, everything is encrypted. And of course, when it goes to a sync gateway, the sync gateway is sort of, as I mentioned, the middle tier component, that is responsible for data synchronization between the embedded devices and Couchbase server. That entity is responsible for enforcement of access control policies. So you are guaranteed that only users who should have access to those documents or data are granted access to that. And in fact, we are NoSQL Json database. So which means, everything is modeled in the form of documents, Json documents. And so when we're talking about read, write access control, read access is at the granularity of a document, and write access can be enforced at the granularity of a property within the document. So you may have access to an entire document, but you may only be allowed to update a certain property within the document. So, as I mentioned, when it comes to distributed computing architectures like edge computing, security is even more paramount, right? You have devices going offline, coming back online and, you might have a breach point at one edge environment, whether it is a data center or an edge device, you need to be able to ensure that you have isolated all the other edge components from that breach. And as I mentioned, when it comes to data governance and so on, data retention, for instance, even if it is not a security breach, let's say you do have, for some reason, the owner of a device should no longer have access to that content. You know, their role has changed, they have transitioned to a different company for instance. Then you will have a way of automatically purging all that data that was previously synchronized to the user's device. >> Got it. Okay. Let's continue talking about the events of the last year and a half. Because we saw this massive scatter, 18 months ago of an explosion at the edge when a lot of people went from the office to this work from home, work from any anywhere environment in which we're still in. So how has the pandemic and the events that related to it changed mobile apps and edge computing and what are some of the new requirements that customers have? >> Sure. Well, as you rightly said, right? In fact, if anything, the relevance of mobile devices and applications has just grown in significance through the pandemic. And it's kind of interesting, there are some surveys that have suggested that through the pandemic people have been using their mobile devices as their primary communication device for accessing the internet. And it's kind of interesting because you think, well, everyone is cooped up in their homes. They probably will have access to other forms of data consumption, but no, it's mobile devices. That's what they have primarily been using. So with that, there is also a new range of use cases and applications, which are driven in large part by the events of the pandemic. But I think that's just made things much more efficient. Customer satisfaction, user experience is paramount, is number one. And I think a lot of that is here to stay even following or post pandemic because it's just made things a lot more efficient. And we've seen that through different industries, right? Healthcare, there was always telemedicine medicine, but now for non-essential care, it's always telemedicine, Of course, specific to the pandemic. there was the, tracking, the contact tracing application, right? That's enabled through technologies like Bluetooth and GPS, so they track the whereabouts of infected persons. But then even if you arrive at your doctor's office, right, you wait in your car and you get notified when the doctor's ready to check you in. And then retail sector, E-commerce right? Of course everything was going online, but everything is overwhelming People are shopping online through their mobile devices, than the traditional web based applications. And you order on your phone, you pick up at the store, right? So curbside pickup, you pull into the store, the store clerk is notified of your arrival. They come out to the curb with your order. And here's the interesting bit, you know, it's kind of intuitive that it's going to be e-commerce applications. They got a huge boost through the pandemic. But interestingly, even the experience when it comes to retail in store, that's undergone a transformation because it was all about how do we make the process very efficient. So customers are in and out of the store really quick, right? there was the reason for that. But now we can translate to making the whole shopping experience much more easy. So you walk into the store, you meet a sales associate who can bring up information about the catalog inventory right there on the iPad. And so if you have any questions, whether it's something is available in the store or an access for you're looking for, they can give answers to you immediately. Right? And of course there are companies like Walmart, they have been rolling out applications. Mobile scan and go sort of applications, which is all about, you know, you scan items as you walk through the aisle, do a self checkout, totally contact-less experience. And, the list goes on, right? We talked about healthcare retail, same thing in, in a restaurant, right? A curbside that delivery and pickup, you can now track your delivery order because now it's just a huge surge in order deliveries. And then the same pickup concept, curbside pickup concept, you arrive in your car, the kitchen is notified of your arrival and they come out with your order, very streamlined drive-through. You've got people now coming to your car, taking the order, right there from the car on their tablets, that synced in real time to the backend kitchen, your order. And you get notified when your order is ready. So I think all this is about making things a lot more efficient. It's about customer experience, and edge computing has a big role to play in that. And so I think, if anything is just propelled the growth of mobile applications and use cases. >> Yeah. That that propulsion is something that we've been hearing a lot about the acceleration in the last year and a half. You did a great job there of painting a picture of some of the positives that come out of this accelerating the efficiencies that we all as consumers and in our business life expect to have. And this explosion at the edge that's really become even more of a lifeline for millions and millions and billions of people globally. I got to ask you that from a connectivity perspective, that's another area where we had this expectation as again, consumers or in our business lives we have connectivity. Where does all that talk about 5G; What does 5G fit into edge computing? >> Sure. That's a good question. Because 5G and edge computing sort of go hand in hand so much so that they are being used synonymously in some cases and that's inaccurate. Okay? So because every time people talk about edge computing, there are folks talking about 5G in the same breath, right? But really 5G, as we all know, is a cellular next generation cellular technology, promises, ultra low latency, very high bandwidth. Now we talked about this huge surge, right in mobile applications and new sort of use cases where a lot of the data is generated at the edge. IOT applications are just data intensive applications, right gaming apps and so on. And all of these applications, they demand ultra low latency, right? And they're generating a lot of this data and all that data needs to be processed in real time. So if you have to send all of that data back to the cloud, and then you get the responses, that's a really bad experience. So that's what 5G is here to solve, right? I mean, it's like low latency, high bandwidth, high concurrency. Now that's all great. But then the coverage of 5G, it terminates at the edge of the mobile operator network. So you have all these massive influx of devices generating all that data. And all that stuff is transmitted under a very low latency conditions over a 5G network. But then if all that data from the mobile operator network has to be back hauled to the internet, to the backend servers, then you kind of defeat the whole purpose of ultra low latency applications. So that's where edge computing comes into play because edge computing is really an architecture, right? It's a distributed architecture. So now what mobile operators are doing is deploying what they refer to as NDCs, but it's effectively micro data centers at the edge of the mobile operator network. So you have all this data coming in over the 5G network. Great. They get analyzed, they get processed locally at the edge of the mobile operator network and you get real-time responses. And of course, as needed that data in aggregated or filter form goes back to the cloud. And so that's where the two relate. So in my view, I think edge computing architectures are important to deliver on the promise of 5G, but 5G has propelled the relevance or importance of edge computing as a solution, as a deployment architecture. So very interrelated. >> Very interrelated, very symbiotic. And of course the need for real time data real-time analytics in every industry became very prominent in the last year and a half. We had this expectation that we're going to be able to understand things in real time. And that's often a huge differentiator for businesses. We're out of time, but I want to ask you one more question Priya, and that is where can customers go to get started with Couchbase? >> Oh, absolutely. So Couchbase servers and gateway, you can deploy that, it's available as software. You can download it from our website. Couchbased lite is available for all your mobile applications. So it is available as a download, but you also have the classic package management systems through which you can download Couchbase Lite. And then of course, as I said, you can deploy this standalone, but you can also deploy it in the cloud. So we have marketplace offerings for both Couchbase server and sync gateway. So if you want to deploy it on AWS, as your Google, you can do that as well. >> Excellent. Priya, thank you so much for joining me on the program, talking about Couchbase the evolution, the changes, the opportunities with edge computing and mobile and how Couchbase is involved. I appreciate your time. >> Thank you very much. And thanks for having me. >> For Priya Rajgopal, I'm Lisa Martin. You're watching the Cubes coverage of Couchbase connect, online 2021.

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante despite all the chatter about cloud repatriation and the exorbitant cost of cloud computing customer spending momentum continues to accelerate in the post-isolation economy if the pandemic was good for the cloud it seems that the benefits of cloud migration remain lasting in the late stages of covid and beyond and we believe this stickiness is going to continue for quite some time we expect i asked revenue for the big four hyperscalers to surpass 115 billion dollars in 2021 moreover the strength of aws specifically as well as microsoft azure remain notable such large organizations showing elevated spending momentum as shown in the etr survey results is perhaps unprecedented in the technology sector hello everyone and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll share some some fresh july survey data that indicates accelerating momentum for the largest cloud computing firms importantly not only is the momentum broad-based but it's also notable in key strategic sectors namely ai and database there seems to be no stopping the cloud momentum there's certainly plenty of buzz about the cloud tax so-called cloud tax but other than wildly assumptive valuation models and some pockets of anecdotal evidence you don't really see the supposed backlash impacting cloud momentum our forecast calls for the big four hyperscalers aws azure alibaba and gcp to surpass 115 billion as we said in is revenue this year the latest etr survey results show that aws lambda has retaken the lead among all major cloud services tracked in the data set as measured in spending momentum this is the service with the most elevated scores azure overall azure functions vmware cloud on aws and aws overall also demonstrate very highly elevated performance all above that of gcp now impressively aws momentum in the all-important fortune 500 where it has always showed strength is also accelerating one concern in the most recent survey data is that the on-prem clouds and so-called hybrid platforms which we had previously reported as showing an upward spending trajectory seem to have cooled off a bit but the data is mixed and it's a little bit too early to draw firm conclusions nonetheless while hyperscalers are holding steady the spending data appears to be somewhat tepid for the on-prem players you know particularly for their cloud we'll study that further after etr drops its full results on july 23rd now turning our attention back to aws the aws cloud is showing strength across its entire portfolio and we're going to show you that shortly in particular we see notable strength relative to others in analytics ai and the all-important database category aurora and redshift are particularly strong but several other aws database services are showing elevated spending velocity which we'll quantify in a moment all that said snowflake continues to lead all database suppliers in spending momentum by a wide margin which again will quantify in this episode but before we dig into the survey let's take a look at our latest projections for the big four hyperscalers in is as you know we track quarterly revenues for the hyperscalers remember aws and alibaba ias data is pretty clean and reported in their respective earnings reports azure and gcp we have to extrapolate and strip out all a lot of the the apps and other certain revenue to make an apples-to-apples comparison with aws and alibaba and as you can see we have the 2021 market exceeding 115 billion dollars worldwide that's a torrid 35 growth rate on top of 41 in 2020 relative to 2019. aggressive yes but the data continues to point us in this direction until we see some clearer headwinds for the cloud players this is the call we're making aws is perhaps losing a sharepoint or so but it's also is so large that its annual incremental revenue is comparable to alibaba's and google's respective cloud business in total is business in total the big three u.s cloud companies all report at the end of july while alibaba is mid mid-august so we'll update these figures at that time okay let's move on and dig into the survey data we don't have the data yet on alibaba and we're limited as to what we can share until etr drops its research update on on the 23rd but here's a look at the net score timeline in the fortune 500 specifically so we filter the fortune 500 for cloud computing you got azure and the yellow aws and the black and gcp in blue so two points here stand out first is that aws and microsoft are converging and remember the customers who respond to the survey they probably include a fair amount of application software spending in their cloud answers so it favors microsoft in that respect and gcp second point is showing notable deceleration relative to the two leaders and the green call out is because this cut is from an aws point of view so in other words gcp declines are a positive for aws so that's how it should be interpreted now let's take a moment to better understand the idea of net score this is one of the fundamental metrics of the etr methodology here's the data for aws so we use that as a as a reference point net score is calculated by asking customers if they're adding a platform new that's the lime green bar that you see here in the current survey they're asking are you spending six percent or more in the second half relative to the first half of the year that's the forest green they're also asking is spending flat that's the gray or are you spending less that's the pink or are you replacing the platform i.e repatriating so not much spending going on in replacements now in fairness one percent of aws is half a billion dollars so i can see where some folks would get excited about that but in the grand scheme of things it's a sliver so again we don't see repatriation in the numbers okay back to net score subtract the reds from the greens and you get net score which in the case of aws is 61 now just for reference my personal subjective elevated net score level is 40 so anything above that is really impressive based on my experience and to have a company of this size be so elevated is meaningful same for microsoft by the way which is consistently well above the 50 mark in net score in the etr surveys so that's you can think about it that's even more impressive perhaps than aws because it's triple the revenue okay let's stay with aws and take a look at the portfolio and the strength across the board this chart shows net score for the past three surveys serverless is on fire by the way not just aws but azure and gcp functions as well but look at the aws portfolio every category is well above the 40 percent elevated red line the only exception is chime and even chime is showing an uptick and chime is meh if you've ever used chime every other category is well above 50 percent next net score very very strong for aws now as we've frequently reported ai is one of the four biggest focus areas from a spending standpoint along with cloud containers and rpa so it stands to reason that the company with the best ai and ml and the greatest momentum in that space has an advantage because ai is being embedded into apps data processes machines everywhere this chart compares the ai players on two dimensions net score on the vertical axis and market share or presence in the data set on the horizontal axis for companies with more than 15 citations in the survey aws has the highest net score and what's notable is the presence on the horizontal axis databricks is a company where high on also shows elevated scores above both google and microsoft who are showing strength in their own right and then you can see data iq data robot anaconda and salesforce with einstein all above that 40 percent mark and then below you can see the position of sap with leonardo ibm watson and oracle which is well below the 40 line all right let's look at at the all-important database category for a moment and we'll first take a look at the aws database portfolio this chart shows the database services in aws's arsenal and breaks down the net score components with the total net score superimposed on top of the bars point one is aurora is highly elevated with a net score above 70 percent that's due to heavy new adoptions redshift is also very strong as are virtually all aws database offerings with the exception of neptune which is the graph database rds dynamodb elastic document db time stream and quantum ledger database all show momentum above that all important 40 line so while a lot of people criticize the fragmentation of the aws data portfolio and their right tool for the right job approach the spending spending metrics tell a story and that that the strategy is working now let's take a look at the microsoft database portfolio there's a story here similar similar to that of aws azure sql and cosmos db microsoft's nosql distributed database are both very highly elevated as are azure database for mysql and mariadb azure cash for redis and azure for cassandra also microsoft is giving look at microsoft's giving customers a lot of options which is kind of interesting you know we've often said that oracle's strategy because we think about oracle they're building the oracle database cloud we've said oracle strategy should be to not just be the cloud for oracle databases but be the cloud for all databases i mean oracle's got a lot of specialty capability there but it looks like microsoft is beating oracle to that punch not that oracle is necessarily going there but we think it should to expand the appeal of its cloud okay last data chart that we'll show and then and then this one looks at database disruption the chart shows how the cloud database companies are doing in ibm oracle teradata in cloudera accounts the bars show the net score granularity as we described earlier and the etr callouts are interesting so first remember this is an aws this is in an aws context so with 47 responses etr rightly indicates that aws is very well positioned in these accounts with the 68 net score but look at snowflake it has an 81 percent net score which is just incredible and you can see google database is also very strong and the high 50 percent range while microsoft even though it's above the 40 percent mark is noticeably lower than the others as is mongodb with presumably atlas which is surprisingly low frankly but back to snowflake so the etr callout stresses that snowflake doesn't have a strong as strong a presence in the legacy database vendor accounts yet now i'm not sure i would put cloudair in the legacy database category but okay whatever cloudera they're positioning cdp is a hybrid platform as are all the on-prem players with their respective products and platforms but it's going to be interesting to see because snowflake has flat out said it's not straddling the cloud and on-prem rather it's all in on cloud but there is a big opportunity to connect on-prem to the cloud and across clouds which snowflake is pursuing that that ladder the cross-cloud the multi-cloud and snowflake is betting on incremental use cases that involve data sharing and federated governance while traditional players they're protecting their turf at the same time trying to compete in cloud native and of course across cloud i think there's room for both but clearly as we've shown cloud has the spending velocity and a tailwind at its back and aws along with microsoft seem to be getting stronger especially in the all-important categories related to machine intelligence ai and database now to be an essential infrastructure technology player in the data era it would seem obvious that you have to have database and or data management intellectual property in your portfolio or you're going to be less valuable to customers and investors okay we're going to leave it there for today remember these episodes they're all available as podcasts wherever you listen all you do is search breaking analysis podcast and please subscribe to the series check out etr's website at etr dot plus plus etr plus we also publish a full report every week on wikibon.com and siliconangle.com you can get in touch with me david.velante at siliconangle.com you can dm me at d vallante or you can hit hit me up on our linkedin post this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time you

(gentle music) >> Welcome to this Cube Conversation, I'm Lisa Martin. Laura Dubois joins me next, VP of product management at Dell Technologies, Laura, welcome back to the program. >> Yeah, thank you so much Lisa, it's just fantastic to be here and talking about data protection now that we're coming out of COVID, it's just wonderful to be here, thank you so much. >> Isn't it so refreshing. So, you're going to provide some updates on Dell's data protection software, some of the innovation, how you're working with customers and prospects. So let's go ahead and dig right in, let's talk about some of the innovation and the enhancements that Dell is making to its data protection suite of software and also how customers are influencing that. >> Yeah, so it's a great question Lisa and you're right. We have driven a lot of innovation and enhancements in our data protection suite. And let me just level a second. So data protection suite, is a solution that is deployed by really tens of thousands of customers. And we continue to innovate and enhance that data protection suite. Data protection suite is comprised primarily of three main data protection software capabilities. So, longstanding capabilities and customer adoption of Avamar, which continues to be a central capability on our portfolio. The second one is Networker. So Networker is also an enterprise grade, highly scalable and performance data protection solution. And then a couple of years ago, we launched a new data protection capability called power protect data manager. So, all three of these capabilities, really the foundation of our data protection suite. And as I said, enterprises around the world rely on these three sets of capabilities to protect their data, regardless of wherever it resides. And it's really central now more than ever in the face of increasing security, risks and compliance and the need to be able to have an always kind of available environment that customers rely on the capabilities and data protection suite to really make sure their enterprises resilient. >> Absolutely, and make sure that that data is recoverable if anything happens, you mentioned cybersecurity. We'll get into that in a second. But so thousands of Avamar and Networker customers, what are some of the key workloads and data that these customers are protecting with these technologies? >> Yeah, I mean, so, actually tens of thousands. >> Tens of thousands. >> Tens of thousands of customers that rely on data protection suite. And it really, I think the strength and advantage of our portfolio is its breadth, breadth in terms of client operating environments, in terms of applications and databases, in terms of workloads and specifically use cases. So I mean, the breadth that we offer is unparalleled, pretty much whether Windows, Linux, OpenVMS, NetWare, kind of going back in time a long tail of kind of operating environments and then databases, right. So everything from SQL and Oracle and Sybase and DB2 to new types of databases, like the NoSQL or content store and key value store types of NoSQL schemas, if you will. And so, and then lastly is the word they use cases, right? So being able to protect data, whether that be data that's in a data center, out in remote or branch locations or data that's out in the cloud, right. And of course, increasingly customers are placing their data in a variety of locations; on Edge, on core data centers and in cloud environments. And we actually have over six exabytes of capacity under management, across public cloud environments. So pretty extensive deployment of our data protection suite in public clouds, you know, the leading hyperscalers, cloud environments and premises as well. >> So let's talk a little bit about the customer influence 'cause obviously there's a very cooperative relationship that Dell has with its customers that help you achieve things. Like, for example, I saw that according to IDC, Dell Technologies is number one in data protection, appliances, and software, leader in the Gartner Magic Quadrant for data center backup and recovery for over 20 years now. Talk to us a little bit more about that symbiotic customer, Dell relationship. >> Yeah, so it's a great question. We see our customers as strategic partners, and we really want to understand their business, their requirements. We engage on a quarterly basis with customers and partners in advisory councils. And then of course, we are always engaging with customers outside of those cycles on a kind of a one-on-one basis. And so we are really driving the innovation and the backlogs and the roadmap for data protection suite based upon customer feedback. And approximately 79% of the fortune 100 customers, our Dell data, Dell Technologies data protection customers. Now that's not to say that that's our only customer base. We have customers in commercial accounts, in mid-market in federal agencies, but, you know, we take our customer relationships really, really seriously, and we engage with them on a regular basis, both in a group forum to provide feedback as well as in a one-on-one basis. And we're building our roadmaps and our product release is based on feedback from customers, and again, know large customer base that we take very seriously. >> Right to the customer listening obviously it is critical for Dell. So you talked a little bit about what that cycle looks like in terms of quarterly meetings and then those individual meetings. What are some of the enhancements and advancements that customers have actually influenced? >> Yeah, so we, I mean, we, I think continuing to provide simplicity and ease of use is a key element of our portfolio and our strategy, right? So continuing to modernize and update the software in terms of workflows, in terms of, you know, common experiences also increasingly customers want to automate their data protection process. So really taking an API-first strategy for how we deliver capabilities to customers, continuing to expand our client database, hypervisor environments, continue to extend out our cloud support, you know, things like protection of cloud native applications with increasingly customers containerizing and building scale-out applications. We want to be able to protect Kubernetes environment. So that's kind of an area of focus for us. Another area of focus for us is going deeper with our key strategic partners, whether that'd be a cloud partner or a hypervisor partner. And then of course, customers, in fact, one of the top three things that we consistently hear from these councils that we do is the criticality of security, security and our data protection environment but the criticality of being able to be resilient from, and in the event of a cyber attack to be able to resilient recover from that cyber attack. So that is an area where we continue to make innovations and investments in the data protection suite as well. >> And that's so critical. One of the things that we saw in the last year, 15 months plus Laura, is this massive rise in ransomware. It's now a household word, the Colonial Pipeline for example, the meat packing plant, it's now many businesses knowing it's not, if we get attacked, but it's when. So having the ability to be resilient and recover that data is table stakes for, I imagine a business in any organization. I want to understand a little bit more. So you talked about tens of thousands of customers using Avamar and Networker. So now they have the capability of also expanding and using more of the suite. Talk to me a little bit about that. >> Yeah, so, I mean, I think it starts with the customer environment and what workloads and use cases they have. And because of the breadth of capabilities indeed the data protection suite, we really optimize the solution based upon their needs, right. So if they have a large portfolio of applications that they need to maintain but they're also building applications or systems for the future, we have a solution there. If they have a single hypervisor strategy or a multiple hypervisor strategy, we have a strategy there, if they have data that's on-premise and across a range of public clouds, one large customer we have as a, kind of three-plus one strategy around cloud. So they're leveraging three different public cloud, IS environments, and then they're also have their on-premise cloud environment. So, you know, we, it really starts with the customer workload and the data, and where it lives; whether that's be out in an Edge location in a remote or branch office, on an end point somewhere, they need to protect whether it be in a core data center or multiple data centers, or rather be in the cloud. That's how we think about optimizing the solution for the customers. >> Curious if you can give me any examples of customers maybe by industry that were, have been with Dell for a long time with Avamar and Networker for a long time and how they've expanded, being able to pick, as you say, as their, or as their environment grows and we've got, now this blur of right. It's now worked from anywhere, data centers, Edge. Talk to me about some customers examples that you think really articulate the value of what Dell is delivering. >> Yeah, so, I mean, I think one customer in the financial services sector comes to mind. They have a large amount of unstructured data that they need to protect, you know, petabytes, petabytes and petabytes of data they need to protect. And so I think that's one customer that comes to mind is someone we've been with for a long time, been partnering with for a long time. Another customer I mentioned in the, it was a kind of a three-letter software company that is a really strategic partner for us with on-premise, in the cloud. You know, healthcare is a big and important sector for Dell. We have integrations into kind of leading healthcare applications. So that's another big, whether they be a healthcare provider or a healthcare insurance company, and had a fourth example, but it's escaping my mind right now, but, I would say going back to the cyber discussion, I mean, one thing that we, where we see really customers looking for guidance from us around cyber recovery and cyber resilience is in what the, you know, of course president Biden just released this executive board on his mandate for ensuring that the federal agencies but also companies in the millisecond sector, sectors be able to ensure resilience from cyber attacks. So that's companies in financial services, that's companies in healthcare, energy, oil, and gas transportation, right. Obviously in companies and industries that are critical to our economy and our infrastructure. And so that has been an area where we've seen, recently in the last, I would say 12 months increased in engagement, you mentioned Colonial Pipeline, for example. So those are some high salient highlights I think of in terms of, you know, kind of key customers. But pretty much every sector. I mean, the U.S. government, all of the the agencies, whether they be civilian, or DOD or key kind of engagement partners of ours. >> Yeah, and as you said in the last year, what a year it's been. But really a business in every industry has got to be able to be resilient and recover when something happens. Can you talk a little bit about some of the specific enhancements that you guys have made to the suite? >> Yeah, sure. So, you know, we continue to enhance our hypervisor capabilities. So we continue to enhance not only the core VMware or hyperbaric capabilities but we continue to enhance some of the extensions or plugins that we have for those. So whether that be things like our VRealized plugin or a vCloud director plugin for say, VMware. So that's kind of a big focus for us. Continuing to enhance capabilities around leveraging the cloud for long-term retention. So that's another kind of enhancement area for us. But cloud in general is an ara where we continue to drive more and more enhancement. Improving performance in cloud environments for a variety of use cases, whether that be DR to the cloud, backup or replications of the cloud or backing up workloads that are already in the cloud. There's a key use cases for us, as well as the archive to cloud use cases. So there's just some examples or areas where we've driven enhancements and you can expect to see more, you know we have a six month release cadence for Avamar and Networker, and we continue with that momentum. And at the end of this month, we have the next major release of our data protection suite. And then six months later, we'll have the next update and so on and so forth. And we've been doing that actually for the last three to four years. This is a six month release cadence for data protection suite. We continue with that momentum. And like I said, simplicity and modernity, APIs and automation, extending our workloads and hypervisors and use cases. And then cloud is a big focusing area as well, as well as security and cyber resilience. >> Right, and so a lot of flexibility in choice for Avamar and Networker customers. As things change the world continues to pivot and we know it's absolutely essential to be able to recover that data. You mentioned 70, I think 79% of the Fortune 100 are using Dell technologies for data protection software. That's probably something that's only going to continue to grow. Lots of stuff coming up. As you mention, what are some of the things that you're personally excited about as the world starts to open up and you get to actually go out and engage with customers? >> I'm in just looking forward to like in-person meetings. I mean, I just loved going and trying to understand what problems the customers are trying to solve and how we can help address those. I think, you know, what I see customers sort of struggling with is how do they kind of manage their current environment while they're building for the future? So there's a lot of interest in questions around, how do they protect some of these new types of workloads, whether they're deployed on premise or in the public cloud. So that continues to be an area where we continue to engage with customers. I'm also really personally excited about the extensions that we're doing in our cyber recovery capabilities so as you can expect to hear more about some of those in the next 12 months, because we're really seeing that as a key driver to kind of increased policies around and implementations around data protection is because of these, you know, the needs to be able to be resilient from cyber attacks. I would say we're also doing some very interesting integrations with VMware. We're going to have some first and only announcements around VMware and managing protection for VMware, you know, VM environments. So you can look forward to hearing more about that. And we have customers that have deployed our data protection solutions at scale. One customer has 150,000 clients who they're protecting with our data protection offerings, 150,000. And so we're continuing to improve the, and enhance the products to meet those kinds of scale requirements. And I'm excited by the fact that we've had this long standing relationship with this one particular customer and continue to help in flowing up where their needs go. >> And that's something that even a great job of talking about is just not just a longstanding relationships but really that dedication that Dell has to innovating with its customers. Laura, thank you for sharing some of the updates of what's new, what you're continuing to do with customers, and what you're looking forward to in the future. It sounds like we might hear some news around the VMworld timeframe. >> Yes, I think so. >> All right, Laura, thank you so much for joining me today. Appreciate your time. >> Yeah, it's been great to be here. Thanks so much. >> Excellent from Laura Dubois and Lisa Martin, you're watching this Cube Conversation. (soft music)

(upbeat music) Developers have become the new king makers in the world of digital and cloud. The rise of containers and microservices has accelerated the transition to cloud native applications. A lot of people will talk about application architecture and the related paradigms and the benefits they bring for the process of writing and delivering new apps. But a major challenge continues to be, the how and the what when it comes to accessing, processing and getting insights from the massive amounts of data that we have to deal with in today's world. And with me are two experts from the data management world who will share with us how they think about the best techniques and practices based on what they see at large organizations who are working with data and developing so-called data-driven apps. Please welcome Maria Colgan and Gerald Venzl, two distinguish product managers from Oracle. Folks, welcome, thanks so much for coming on. >> Thanks for having us Dave. >> Thank you very much for having us. >> Okay, Maria let's start with you. So, we throw around this term data-driven, data-driven applications. What are we really talking about there? >> So data-driven applications are applications that work on a diverse set of data. So anything from spatial to sensor data, document data as well as your usual transaction processing data. And what they're going to do is they'll generate value from that data in very different ways to a traditional application. So for example, they may use machine learning, they are able to do product recommendations in the middle of a transaction. Or we could use graph to be able to identify an influencer within the community so we can target them with a specific promotion. It could also use spatial data to be able to help find the nearest stores to a particular customer. And because these apps are deployed on multiple platforms, everything from mobile devices as well as standard browsers, they need a data platform that's going to be both secure, reliable and scalable. >> Well, so when you think about how the workloads are shifting I mean, we're not talking about, you know it's not anymore a world of just your ERP or your HCM or your CRM, you know kind of the traditional operational systems. You really are seeing an explosion of these new data oriented apps. You're seeing, you know, modeling in the cloud, you are going to see more and more inferencing, inferencing at the edge. But Maria maybe you could talk a little bit about sort of the benefits that customers are seeing from developing these types of applications. I mean, why should people care about data-driven apps? >> Oh, for sure, there's massive benefits to them. I mean, probably the most obvious one for any business regardless of the industry, is that they not only allow you to understand what your customers are up to, but they allow you to be able to anticipate those customer's needs. So that helps businesses maintain that competitive edge and retain their customers. But it also helps them make data-driven decisions in real time based on actual data rather than on somebody's gut feeling or basing those decisions on historical data. So for example, you can do real-time price adjustments on products based on demand and so forth, that kind of thing. So it really changes the way people do business today. >> So Gerald, you think about the narrative in the industry everybody wants to be a platform player all your customers they are becoming software companies, they are becoming platform players. Everybody wants to be like, you know name a company that is huge trillion dollar market cap or whatever, and those are data-driven companies. And so it would seem to me that data-driven applications, there's nobody, no company really shouldn't be data-driven. Do you buy that? >> Yeah, absolutely. I mean, data-driven, and that naturally the whole industry is data-driven, right? It's like we all have information technologies about processing data and deriving information out of it. But when it comes to app development I think there is a big push to kind of like we have to do machine learning in our applications, we have to get insights from data. And when you actually look back a bit and take a step back, you see that there's of course many different kinds of applications out there as well that's not to be forgotten, right? So there is a usual front end user interfaces where really the application all it does is just entering some piece of information that's stored somewhere or perhaps a microservice that's not attached to a data to you at all but just receives or asks calls (indistinct). So I think it's not necessarily so important for every developer to kind of go on a bandwagon that they have to be data-driven. But I think it's equally important for those applications and those developers that build applications, that drive the business, that make business critical decisions as Maria mentioned before. Those guys should take really a close look into what data-driven apps means and what the data to you can actually give to them. Because what we see also happening a lot is that a lot of the things that are well known and out there just ready to use are being reimplemented in the applications. And for those applications, they essentially just ended up spending more time writing codes that will be already there and then have to maintain and debug the code as well rather than just going to market faster. >> Gerald can you talk to the prevailing approaches that developers take to build data-driven applications? What are the ones that you see? Let's dig into that a little bit more and maybe differentiate the different approaches and talk about that? >> Yeah, absolutely. I think right now the industry is like in two camps, it's like sort of a religious war going on that you'll see often happening with different architectures and so forth going on. So we have single purpose databases or data management technologies. Which are technologies that are as the name suggests build around a single purpose. So it's like, you know a typical example would be your ordinary key-value store. And a key-value store all it does is it allows you to store and retrieve a piece of data whatever that may be really, really fast but it doesn't really go beyond that. And then the other side of the house or the other camp would be multimodal databases, multimodal data management technologies. Those are technologies that allow you to store different types of data, different formats of data in the same technology in the same system alongside. And, you know, when you look at the geographics out there of what we have from technology, is pretty much any relational database or any database really has evolved into such a multimodal database. Whether that's MySQL that allows you to store or chase them alongside relational or even a MongoDB that allows you to do or gives you native graph support since (mumbles) and as well alongside the adjacent support. >> Well, it's clearly a trend in the industry. We've talked about this a lot in The Cube. We know where Oracle stands on this. I mean, you just mentioned MySQL but I mean, Oracle Databases you've been extending, you've mentioned JSON, we've got blockchain now in there you're infusing, you know ML and AI into the database, graph database capabilities, you know on and on and on. We talked a lot about we compared that to Amazon which is kind of the right tool, the right job approach. So maybe you could talk about, you know, your point of view, the benefits for developers of using that converged database if I can use that word approach being able to store multiple data formats? Why do you feel like that's a better approach? >> Yeah, I think on a high level it comes down to complexity. You are actually avoiding additional complexity, right? So not every use case that you have necessarily warrants to have yet another data management technology or yet the special build technology for managing that data, right? It's like many use cases that we see out there happily want to just store a piece of a chase and document, a piece of chase in a database and then perhaps retrieve it again afterwards so write some simple queries over it. And you really don't have to get a new database technology or a NoSQL database into the mix if you already have some to just fulfill that exact use case. You could just happily store that information as well in the database you already have. And what it really comes down to is the learning curve for developers, right? So it's like, as you use the same technology to store other types of data, you don't have to learn a new technology, you don't have to associate yourself with new and learn new drivers. You don't have to find new frameworks and you don't have to know how to necessarily operate or best model your data for that database. You can essentially just reuse your knowledge of the technology as well as the libraries and code you have already built in house perhaps in another application, perhaps, you know framework that you used against the same technology because it is still the same technology. So, kind of all comes down again to avoiding complexity rather than not fragmenting you know, the many different technologies we have. If you were to look at the different data formats that are out there today it's like, you know, you would end up with many different databases just to store them if you were to fully religiously follow the single purpose best built technology for every use case paradigm, right? And then you would just end up having to manage many different databases more than actually focusing on your app and getting value to your business or to your user. >> Okay, so I get that and I buy that by the way. I mean, especially if you're a larger organization and you've got all these projects going on but before we go back to Maria, Gerald, I want to just, I want to push on that a little bit. Because the counter to that argument would be in the analogy. And I wonder if you, I'd love for you to, you know knock this analogy off the blocks. The counter would be okay, Oracle is the Swiss Army knife and it's got, you know, all in one. But sometimes I need that specialized long screwdriver and I go into my toolbox and I grab that. It's better than the screwdriver in my Swiss Army knife. Why, are you the Swiss Army knife of databases? Or are you the all-in-one have that best of breed screwdriver for me? How do you think about that? >> Yeah, that's a fantastic question, right? And I think it's first of all, you have to separate between Oracle the company that has actually multiple data management technologies and databases out there as you said before, right? And Oracle Database. And I think Oracle Database is definitely a Swiss Army knife has many capabilities of since the last 40 years, you know that we've seen object support coming that's still in the Oracle Database today. We have seen XML coming, it's still in the Oracle Database, graph, spatial, et cetera. And so you have many different ways of managing your data and then on top of that going into the converge, not only do we allow you to store the different data model in there but we actually allow you also to, you apply all the security policies and so forth on top of it something Maria can talk more about the mission around converged database. I would also argue though that for some aspects, we do actually have to or add a screwdriver that you talked about as well. So especially in the relational world people get very quickly hung up on this idea that, oh, if you only do rows and columns, well, that's kind of what you put down on disk. And that was never true, it's the relational model is actually a logical model. What's probably being put down on disk is blocks that align themselves nice with block storage and always has been. So that allows you to actually model and process the data sort of differently. And one common example or one good example that we have that we introduced a couple of years ago was when, column and databases were very strong and you know, the competition came it's like, yeah, we have In-Memory column that stores now they're so much better. And we were like, well, orienting the data role-based or column-based really doesn't matter in the sense that we store them as blocks on disks. And so we introduced the in memory technology which gives you an In-Memory column, a representation of your data as well alongside your relational. So there is an example where you go like, well, actually you know, if you have this use case of the column or analytics all In-Memory, I would argue Oracle Database is also that screwdriver you want to go down to and gives you that capability. Because not only gives you representation in columnar, but also which many people then forget all the analytic power on top of SQL. It's one thing to store your data columnar, it's a completely different story to actually be able to run analytics on top of that and having all the built-in functionalities and stuff that you want to do with the data on top of it as you analyze it. >> You know, that's a great example, the kilometer 'cause I remember there was like a lot of hype around it. Oh, it's the Oracle killer, you know, at Vertica. Vertica is still around but, you know it never really hit escape velocity. But you know, good product, good company, whatever. Natezza, it kind of got buried inside of IBM. ParXL kind of became, you know, red shift with that deal so that kind of went away. Teradata bought a company, I forget which company it bought but. So that hype kind of disapated and now it's like, oh yeah, columnar. It's kind of like In-Memory, we've had a In-Memory databases ever since we've had databases you know, it's a kind of a feature not a sector. But anyway, Maria, let's come back to you. You've got a lot of customer experience. And you speak with a lot of companies, you know during your time at Oracle. What else are you seeing in terms of the benefits to this approach that might not be so intuitive and obvious right away? >> I think one of the biggest benefits to having a multimodel multiworkload or as we call it a converged database, is the fact that you can get greater data synergy from it. In other words, you can utilize all these different techniques and data models to get better value out of that data. So things like being able to do real-time machine learning, fraud detection inside a transaction or being able to do a product recommendation by accessing three different data models. So for example, if I'm trying to recommend a product for you Dave, I might use graph analytics to be able to figure out your community. Not just your friends, but other people on our system who look and behave just like you. Once I know that community then I can go over and see what products they bought by looking up our product catalog which may be stored as JSON. And then on top of that I can then see using the key-value what products inside that catalog those community members gave a five star rating to. So that way I can really pinpoint the right product for you. And I can do all of that in one transaction inside the database without having to transform that data into different models or God forbid, access different systems to be able to get all of that information. So it really simplifies how we can generate that value from the data. And of course, the other thing our customers love is when it comes to deploying data-driven apps, when you do it on a converged database it's much simpler because it is that standard data platform. So you're not having to manage multiple independent single purpose databases. You're not having to implement the security and the high availability policies, you know across a bunch of different diverse platforms. All of that can be done much simpler with a converged database 'cause the DBA team of course, is going to just use that standard set of tools to manage, monitor and secure those systems. >> Thank you for that. And you know, it's interesting, you talk about simplification and you are in Juan's organization so you've big focus on mission critical. And so one of the things that I think is often overlooked well, we talk about all the time is recovery. And if things are simpler, recovery is faster and easier. And so it's kind of the hallmark of Oracle is like the gold standard of the toughest apps, the most mission critical apps. But I wanted to get to the cloud Maria. So because everything is going to the cloud, right? Not all workloads are going to the cloud but everybody is talking about the cloud. Everybody has cloud first mentality and so yes, it's a hybrid world. But the natural next question is how do you think the cloud fits into this world of data-driven apps? >> I think just like any app that you're developing, the cloud helps to accelerate that development. And of course the deployment of these data-driven applications. 'Cause if you think about it, the developer is instantly able to provision a converged database that Oracle will automatically manage and look after for them. But what's great about doing something like that if you use like our autonomous database service is that it comes in different flavors. So you can get autonomous transaction processing, data warehousing or autonomous JSON so that the developer is going to get a database that's been optimized for their specific use case, whatever they are trying to solve. And it's also going to contain all of that great functionality and capabilities that we've been talking about. So what that really means to the developer though is as the project evolves and inevitably the business needs change a little, there's no need to panic when one of those changes comes in because your converged database or your autonomous database has all of those additional capabilities. So you can simply utilize those to able to address those evolving changes in the project. 'Cause let's face it, none of us normally know exactly what we need to build right at the very beginning. And on top of that they also kind of get a built-in buddy in the cloud, especially in the autonomous database. And that buddy comes in the form of built-in workload optimizations. So with the autonomous database we do things like automatic indexing where we're using machine learning to be that buddy for the developer. So what it'll do is it'll monitor the workload and see what kind of queries are being run on that system. And then it will actually determine if there are indexes that should be built to help improve the performance of that application. And not only does it bill those indexes but it verifies that they help improve the performance before publishing it to the application. So by the time the developer is finished with that app and it's ready to be deployed, it's actually also been optimized by the developers buddy, the Oracle autonomous database. So, you know, it's a really nice helping hand for developers when they're building any app especially data-driven apps. >> I like how you sort of gave us, you know the truth here is you don't always know where you're going when you're building an app. It's like it goes from you are trying to build it and they will come to start building it and we'll figure out where it's going to go. With Agile that's kind of how it works. But so I wonder, can you give some examples of maybe customers or maybe genericize them if you need to. Data-driven apps in the cloud where customers were able to drive more efficiency, where the cloud buddy allowed the customers to do more with less? >> No, we have tons of these but I'll try and keep it to just a couple. One that comes to mind straight away is retrace. These folks built a blockchain app in the Oracle Cloud that allows manufacturers to actually share the supply chain with the consumer. So the consumer can see exactly, who made their product? Using what raw materials? Where they were sourced from? How it was done? All of that is visible to the consumer. And in order to be able to share that they had to work on a very diverse set of data. So they had everything from JSON documents to images as well as your traditional transactions in there. And they store all of that information inside the Oracle autonomous database, they were able to build their app and deploy it on the cloud. And they were able to do all of that very, very quickly. So, you know, that ability to work on multiple different data types in a single database really helped them build that product and get it to market in a very short amount of time. Another customer that's doing something really, really interesting is MindSense. So these guys operate the largest mines in Canada, Chile, and Peru. But what they do is they put these x-ray devices on the massive mechanical shovels that are at the cove or at the mine face. And what that does is it senses the contents of the buckets inside these mining machines. And it's looking to see at that content, to see how it can optimize the processing of the ore inside in that bucket. So they're looking to minimize the amount of power and water that it's going to take to process that. And also of course, minimize the amount of waste that's going to come out of that project. So all of that sensor data is sent into an autonomous database where it's going to be processed by a whole host of different users. So everything from the mine engineers to the geo scientists, to even their own data scientists utilize that data to drive their business forward. And what I love about these guys is they're not happy with building just one app. MindSense actually use our built-in low core development environment, APEX that comes as part of the autonomous database and they actually produce applications constantly for different aspects of their business using that technology. And it's actually able to accelerate those new apps to the business. It takes them now just a couple of days or weeks to produce an app instead of months or years to build those new apps. >> Great, thank you for that Maria. Gerald, I'm going to push you again. So, I said upfront and talked about microservices and the cloud and containers and you know, anybody in the developer space follows that very closely. But some of the things that we've been talking about here people might look at that and say, well, they're kind of antithetical to microservices. This is our Oracles monolithic approach. But when you think about the benefits of microservices, people want freedom of choice, technology choice, seen as a big advantage of microservices and containers. How do you address such an argument? >> Yeah, that's an excellent question and I get that quite often. The microservices architecture in general as I said before had architectures, Linux distributions, et cetera. It's kind of always a bit of like there's an academic approach and there's a pragmatic approach. And when you look at the microservices the original definitions that came out at the early 2010s. They actually never said that each microservice has to have a database. And they also never said that if a microservice has a database, you have to use a different technology for each microservice. Just like they never said, you have to write a microservice in a different programming language, right? So where I'm going with this is like, yes you know, sometimes when you look at some vendors out there, some niche players, they push this message or they jump on this academic approach of like each microservice has the best tool at hand or I'd use a different database for your purpose, et cetera. Which almost often comes across like us. You know, we want to stay part of the conversation. Nothing stops a developer from, you know using a multimodal database for the microservice and just using that as a document store, right? Or just using that as a relational database. And, you know, sometimes I mean, it was actually something that happened that was really interesting yesterday I don't know whether you follow Dave or not. But Facebook had an outage yesterday, right? And Facebook is one of those companies that are seen as the Silicon Valley, you know know how to do microservices companies. And when you add through the outage, well, what happened, right? Some unfortunate logical error with configuration as a force that took a database cluster down. So, you know, there you have it where you go like, well, maybe not every microservice is actually in fact talking to its own database or its own special purpose database. I think there, you know, well, what we should, the industry should be focusing much more on this argument of which technology to use? What's the right tool for a job? Is more to ask themselves, what business problem actually are we trying to solve? And therefore what's the right approach and the right technology for this. And so therefore, just as I said before, you know multimodal databases they do have strong benefits. They have many built-in functionalities that are already there and they allow you to reduce this complexity of having to know many different technologies, right? And so it's not only to store different data models either you know, treat a multimodal database as a chasing documents store or a relational database but most databases are multimodal since 20 plus years. But it's also actually being able to perhaps if you store that data together, you can perhaps actually derive additional value for somebody else but perhaps not for your application. But like for example, if you were to use Oracle Database you can actually write queries on top of all of that data. It doesn't really matter for our query engine whether it's the data is format that then chase or the data is formatted in rows and columns you can just rather than query over it. And that's actually very powerful for those guys that have to, you know get the reporting done the end of the day, the end of the week. And for those guys that are the data scientists that they want to figure out, you know which product performed really well or can we tweak something here and there. When you look into that space you still see a huge divergence between the guys to put data in kind of the altarpiece style and guys that try to derive new insights. And there's still a lot of ETL going around and, you know we have big data technologies that some of them come and went and some of them came in that are still around like Apache Spark which is still like a SQL engine on top of any of your data kind of going back to the same concept. And so I will say that, you know, for developers when we look at microservices it's like, first of all, is the argument you were making because the vendor or the technology you want to use tells you this argument or, you know, you kind of want to have an argument to use a specific technology? Or is it really more because it is the best technology, to best use for this given use case for this given application that you have? And if so there's of course, also nothing wrong to use a single purpose technology either, right? >> Yeah, I mean, whenever I talk about Oracle I always come back to the most important applications, the mission critical. It's very difficult to architect databases with microservices and containers. You have to be really, really careful. And so and again, it comes back to what we were talking before about with Maria that the complexity and the recovery. But Gerald I want to stay with you for a minute. So there's other data management technologies popping out there. I mean, I've seen some people saying, okay just leave the data in an S3 bucket. We can query that, then we've got some magic sauce to do that. And so why are you optimistic about you know, traditional database technology going forward? >> I would say because of the history of databases. So one thing that once struck me when I came to Oracle and then got to meet great people like Juan Luis and Andy Mendelsohn who had been here for a long, long time. I come to realization that relational databases are around for about 45 years now. And, you know, I was like, I'm too young to have been around then, right? So I was like, what else was around 45 years? It's like just the tech stack that we have today. It's like, how does this look like? Well, Linux only came out in 93. Well, databases pre-date Linux a lot rather than as I started digging I saw a lot of technologies come and go, right? And you mentioned before like the technologies that data management systems that we had that came and went like the columnar databases or XML databases, object databases. And even before relational databases before Cot gave us the relational model there were apparently these networks stores network databases which to some extent look very similar to adjacent documents. There wasn't a harder storing data and a hierarchy to format. And, you know when you then start actually reading the Cot paper and diving a little bit more into the relation model, that's I think one important crux in there that most of the industry keeps forgetting or it hasn't been around to even know. And that is that when Cot created the relational model, he actually focused not so much on the application putting the data in, but on future users and applications still being able to making sense out of the data, right? And that's kind of like I said before we had those network models, we had XML databases you have adjacent documents stores. And the one thing that they all have along with it is like the application that puts the data in decides the structure of the data. And that's all well and good if you had an application of the developer writing an application. It can become really tricky when 10 years later you still want to look at that data and the application that the developer is no longer around then you go like, what does this all mean? Where is the structure defined? What is this attribute? What does it mean? How does it correlate to others? And the one thing that people tend to forget is that it's actually the data that's here to stay not someone who does the applications where it is. Ideally, every company wants to store every single byte of data that they have because there might be future value in it. Economically may not make sense that's now much more feasible than just years ago. But if you could, why wouldn't you want to store all your data, right? And sometimes you actually have to store the data for seven years or whatever because the laws require you to. And so coming back then and you know, like 10 years from now and looking at the data and going like making sense of that data can actually become a lot more difficult and a lot more challenging than having to first figure out and how we store this data for general use. And that kind of was what the relational model was all about. We decompose the data structures into tables and columns with relationships amongst each other so therefore between each other. So that therefore if somebody wants to, you know typical example would be well you store some purchases from your web store, right? There's a customer attribute in it. There's some credit card payment information in it, just some product information on what the customer bought. Well, in the relational model if you just want to figure out which products were sold on a given day or week, you just would query the payment and products table to get the sense out of it. You don't need to touch the customer and so forth. And with the hierarchical model you have to first sit down and understand how is the structure, what is the customer? Where is the payment? You know, does the document start with the payment or does it start with the customer? Where do I find this information? And then in the very early days those databases even struggled to then not having to scan all the documents to get the data out. So coming back to your question a bit, I apologize for going on here. But you know, it's like relational databases have been around for 45 years. I actually argue it's one of the most successful software technologies that we have out there when you look in the overall industry, right? 45 years is like, in IT terms it's like from a star being the ones who are going supernova. You have said it before that many technologies coming and went, right? And just want to add a more really interesting example by the way is Hadoop and HDFS, right? They kind of gave us this additional promise of like, you know, the 2010s like 2012, 2013 the hype of Hadoop and so forth and (mumbles) and HDFS. And people are just like, just put everything into HDFS and worry about the data later, right? And we can query it and map reduce it and whatever. And we had customers actually coming to us they were like, great we have half a petabyte of data on an HDFS cluster and we have no clue what's stored in there. How do we figure this out? What are we going to do now? Now you had a big data cleansing problem. And so I think that is why databases and also data modeling is something that will not go away anytime soon. And I think databases and database technologies are here for quite a while to stay. Because many of those are people they don't think about what's happening to the data five years from now. And many of the niche players also and also frankly even Amazon you know, following with this single purpose thing is like, just use the right tool for the job for your application, right? Just pull in the data there the way you wanted. And it's like, okay, so you use technologies all over the place and then five years from now you have your data fragmented everywhere in different formats and, you know inconsistencies, and, and, and. And those are usually when you come back to this data-driven business critical business decision applications the worst case scenario you can have, right? Because now you need an army of people to actually do data cleansing. And there's not a coincidence that data science has become very, very popular the last recent years as we kind of went on with this proliferation of different database or data management technologies some of those are not even database. But I think I leave it at that. >> It's an interesting talk track because you're right. I mean, no schema on right was alluring, but it definitely created some problems. It also created an entire, you know you referenced the hyper specialized roles and did the data cleansing component. I mean, maybe technology will eventually solve that problem but it hasn't up at least up tonight. Okay, last question, Maria maybe you could start off and Gerald if you want to chime in as well it'd be great. I mean, it's interesting to watch this industry when Oracle sort of won the top database mantle. I mean, I watched it, I saw it. It was, remember it was Informix and it was (indistinct) too and of course, Microsoft you got to give them credit with SQL server, but Oracle won the database wars. And then everything got kind of quiet for awhile database was sort of boring. And then it exploded, you know, all the, you know not only SQL and the key-value stores and the cloud databases and this is really a hot area now. And when we looked at Oracle we said, okay, Oracle it's all about Oracle Database, but we've seen the kind of resurgence in MySQL which everybody thought, you know once Oracle bought Sun they were going to kill MySQL. But now we see you investing in HeatWave, TimesTen, we talked about In-Memory databases before. So where do those fit in Maria in the grand scheme? How should we think about Oracle's database portfolio? >> So there's lots of places where you'd use those different things. 'Cause just like any other industry there are going to be new and boutique use cases that are going to benefit from a more specialized product or single purpose product. So good examples off the top of my head of the kind of systems that would benefit from that would be things like a stock exchange system or a telephone exchange system. Both of those are latency critical transaction processing applications where they need microsecond response times. And that's going to exceed perhaps what you might normally get or deploy with a converged database. And so Oracle's TimesTen database our In-Memory database is perfect for those kinds of applications. But there's also a host of MySQL applications out there today and you said it yourself there Dave, HeatWave is a great place to provision and deploy those kinds of applications because it's going to run 100 times faster than AWS (mumbles). So, you know, there really is a place in the market and in our customer's systems and the needs they have for all of these different members of our database family here at Oracle. >> Yeah, well, the internet is basically running in the lamp stack so I see MySQL going away. All right Gerald, will give you the final word, bring us home. >> Oh, thank you very much. Yeah, I mean, as Maria said, I think it comes back to what we discussed before. There is obviously still needs for special technologies or different technologies than a relational database or multimodal database. Oracle has actually many more databases that people may first think of. Not only the three that we have already mentioned but there's even SP so the Oracle's NoSQL database. And, you know, on a high level Oracle is a data management company, right? And we want to give our customers the best tools and the best technology to manage all of their data. Rather than therefore there has to be a need or there should be a part of the business that also focuses on this highly specialized systems and this highly specialized technologies that address those use cases. And I think it makes perfect sense. It's like, you know, when the customer comes to Oracle they're not only getting this, take this one product you know, and if you don't like it your problem but actually you have choice, right? And choice allows you to make a decision based on what's best for you and not necessarily best for the vendor you're talking to. >> Well guys, really appreciate your time today and your insights. Maria, Gerald, thanks so much for coming on The Cube. >> Thank you very much for having us. >> And thanks for watching this Cube conversation this is Dave Vellante and we'll see you next time. (upbeat music)

(upbeat music) >> It'll be five, four, three and then silent two, one, and then you guys just follow my lead. We're just making some last minute adjustments. Like I said, we're down two hands today. So, you good Alex? Okay, are you guys ready? >> I'm ready. >> Ready. >> I got to get get one note here. >> So I noticed Maria you stopped anyway, so I have time. >> Just so they know Dave and the Boston Studio, are they both kind of concurrently be on film even when they're not speaking or will only the speaker be on film for like if Gerald's drawing while Maria is talking about-- >> Sorry but then I missed one part of my onboarding spiel. There should be, if you go into gallery there should be a label. There should be something labeled Boston live switch feed. If you pin that gallery view you'll see what our program currently being recorded is. So any time you don't see yourself on that feed is an excellent time to take a drink of water, scratch your nose, check your notes. Do whatever you got to do off screen. >> Can you give us a three shot, Alex? >> Yes, there it is. >> And then go to me, just give me a one-shot to Dave. So when I'm here you guys can take a drink or whatever >> That makes sense? >> Yeah. >> Excellent, I will get my recordings restarted and we'll open up when Dave's ready. >> All right, you guys ready? >> Ready. >> All right Steve, you go on mute. >> Okay, on me in 5, 4, 3. Developers have become the new king makers in the world of digital and cloud. The rise of containers and microservices has accelerated the transition to cloud native applications. A lot of people will talk about application architecture and the related paradigms and the benefits they bring for the process of writing and delivering new apps. But a major challenge continues to be, the how and the what when it comes to accessing, processing and getting insights from the massive amounts of data that we have to deal with in today's world. And with me are two experts from the data management world who will share with us how they think about the best techniques and practices based on what they see at large organizations who are working with data and developing so-called data-driven apps. Please welcome Maria Colgan and Gerald Venzl, two distinguish product managers from Oracle. Folks, welcome, thanks so much for coming on. >> Thanks for having us Dave. >> Thank you very much for having us. >> Okay, Maria let's start with you. So, we throw around this term data-driven, data-driven applications. What are we really talking about there? >> So data-driven applications are applications that work on a diverse set of data. So anything from spatial to sensor data, document data as well as your usual transaction processing data. And what they're going to do is they'll generate value from that data in very different ways to a traditional application. So for example, they may use machine learning, they are able to do product recommendations in the middle of a transaction. Or we could use graph to be able to identify an influencer within the community so we can target them with a specific promotion. It could also use spatial data to be able to help find the nearest stores to a particular customer. And because these apps are deployed on multiple platforms, everything from mobile devices as well as standard browsers, they need a data platform that's going to be both secure, reliable and scalable. >> Well, so when you think about how the workloads are shifting I mean, we're not talking about, you know it's not anymore a world of just your ERP or your HCM or your CRM, you know kind of the traditional operational systems. You really are seeing an explosion of these new data oriented apps. You're seeing, you know, modeling in the cloud, you are going to see more and more inferencing, inferencing at the edge. But Maria maybe you could talk a little bit about sort of the benefits that customers are seeing from developing these types of applications. I mean, why should people care about data-driven apps? >> Oh, for sure, there's massive benefits to them. I mean, probably the most obvious one for any business regardless of the industry, is that they not only allow you to understand what your customers are up to, but they allow you to be able to anticipate those customer's needs. So that helps businesses maintain that competitive edge and retain their customers. But it also helps them make data-driven decisions in real time based on actual data rather than on somebody's gut feeling or basing those decisions on historical data. So for example, you can do real-time price adjustments on products based on demand and so forth, that kind of thing. So it really changes the way people do business today. >> So Gerald, you think about the narrative in the industry everybody wants to be a platform player all your customers they are becoming software companies, they are becoming platform players. Everybody wants to be like, you know name a company that is huge trillion dollar market cap or whatever, and those are data-driven companies. And so it would seem to me that data-driven applications, there's nobody, no company really shouldn't be data-driven. Do you buy that? >> Yeah, absolutely. I mean, data-driven, and that naturally the whole industry is data-driven, right? It's like we all have information technologies about processing data and deriving information out of it. But when it comes to app development I think there is a big push to kind of like we have to do machine learning in our applications, we have to get insights from data. And when you actually look back a bit and take a step back, you see that there's of course many different kinds of applications out there as well that's not to be forgotten, right? So there is a usual front end user interfaces where really the application all it does is just entering some piece of information that's stored somewhere or perhaps a microservice that's not attached to a data to you at all but just receives or asks calls (indistinct). So I think it's not necessarily so important for every developer to kind of go on a bandwagon that they have to be data-driven. But I think it's equally important for those applications and those developers that build applications, that drive the business, that make business critical decisions as Maria mentioned before. Those guys should take really a close look into what data-driven apps means and what the data to you can actually give to them. Because what we see also happening a lot is that a lot of the things that are well known and out there just ready to use are being reimplemented in the applications. And for those applications, they essentially just ended up spending more time writing codes that will be already there and then have to maintain and debug the code as well rather than just going to market faster. >> Gerald can you talk to the prevailing approaches that developers take to build data-driven applications? What are the ones that you see? Let's dig into that a little bit more and maybe differentiate the different approaches and talk about that? >> Yeah, absolutely. I think right now the industry is like in two camps, it's like sort of a religious war going on that you'll see often happening with different architectures and so forth going on. So we have single purpose databases or data management technologies. Which are technologies that are as the name suggests build around a single purpose. So it's like, you know a typical example would be your ordinary key-value store. And a key-value store all it does is it allows you to store and retrieve a piece of data whatever that may be really, really fast but it doesn't really go beyond that. And then the other side of the house or the other camp would be multimodal databases, multimodal data management technologies. Those are technologies that allow you to store different types of data, different formats of data in the same technology in the same system alongside. And, you know, when you look at the geographics out there of what we have from technology, is pretty much any relational database or any database really has evolved into such a multimodal database. Whether that's MySQL that allows you to store or chase them alongside relational or even a MongoDB that allows you to do or gives you native graph support since (mumbles) and as well alongside the adjacent support. >> Well, it's clearly a trend in the industry. We've talked about this a lot in The Cube. We know where Oracle stands on this. I mean, you just mentioned MySQL but I mean, Oracle Databases you've been extending, you've mentioned JSON, we've got blockchain now in there you're infusing, you know ML and AI into the database, graph database capabilities, you know on and on and on. We talked a lot about we compared that to Amazon which is kind of the right tool, the right job approach. So maybe you could talk about, you know, your point of view, the benefits for developers of using that converged database if I can use that word approach being able to store multiple data formats? Why do you feel like that's a better approach? >> Yeah, I think on a high level it comes down to complexity. You are actually avoiding additional complexity, right? So not every use case that you have necessarily warrants to have yet another data management technology or yet the special build technology for managing that data, right? It's like many use cases that we see out there happily want to just store a piece of a chase and document, a piece of chase in a database and then perhaps retrieve it again afterwards so write some simple queries over it. And you really don't have to get a new database technology or a NoSQL database into the mix if you already have some to just fulfill that exact use case. You could just happily store that information as well in the database you already have. And what it really comes down to is the learning curve for developers, right? So it's like, as you use the same technology to store other types of data, you don't have to learn a new technology, you don't have to associate yourself with new and learn new drivers. You don't have to find new frameworks and you don't have to know how to necessarily operate or best model your data for that database. You can essentially just reuse your knowledge of the technology as well as the libraries and code you have already built in house perhaps in another application, perhaps, you know framework that you used against the same technology because it is still the same technology. So, kind of all comes down again to avoiding complexity rather than not fragmenting you know, the many different technologies we have. If you were to look at the different data formats that are out there today it's like, you know, you would end up with many different databases just to store them if you were to fully religiously follow the single purpose best built technology for every use case paradigm, right? And then you would just end up having to manage many different databases more than actually focusing on your app and getting value to your business or to your user. >> Okay, so I get that and I buy that by the way. I mean, especially if you're a larger organization and you've got all these projects going on but before we go back to Maria, Gerald, I want to just, I want to push on that a little bit. Because the counter to that argument would be in the analogy. And I wonder if you, I'd love for you to, you know knock this analogy off the blocks. The counter would be okay, Oracle is the Swiss Army knife and it's got, you know, all in one. But sometimes I need that specialized long screwdriver and I go into my toolbox and I grab that. It's better than the screwdriver in my Swiss Army knife. Why, are you the Swiss Army knife of databases? Or are you the all-in-one have that best of breed screwdriver for me? How do you think about that? >> Yeah, that's a fantastic question, right? And I think it's first of all, you have to separate between Oracle the company that has actually multiple data management technologies and databases out there as you said before, right? And Oracle Database. And I think Oracle Database is definitely a Swiss Army knife has many capabilities of since the last 40 years, you know that we've seen object support coming that's still in the Oracle Database today. We have seen XML coming, it's still in the Oracle Database, graph, spatial, et cetera. And so you have many different ways of managing your data and then on top of that going into the converge, not only do we allow you to store the different data model in there but we actually allow you also to, you apply all the security policies and so forth on top of it something Maria can talk more about the mission around converged database. I would also argue though that for some aspects, we do actually have to or add a screwdriver that you talked about as well. So especially in the relational world people get very quickly hung up on this idea that, oh, if you only do rows and columns, well, that's kind of what you put down on disk. And that was never true, it's the relational model is actually a logical model. What's probably being put down on disk is blocks that align themselves nice with block storage and always has been. So that allows you to actually model and process the data sort of differently. And one common example or one good example that we have that we introduced a couple of years ago was when, column and databases were very strong and you know, the competition came it's like, yeah, we have In-Memory column that stores now they're so much better. And we were like, well, orienting the data role-based or column-based really doesn't matter in the sense that we store them as blocks on disks. And so we introduced the in memory technology which gives you an In-Memory column, a representation of your data as well alongside your relational. So there is an example where you go like, well, actually you know, if you have this use case of the column or analytics all In-Memory, I would argue Oracle Database is also that screwdriver you want to go down to and gives you that capability. Because not only gives you representation in columnar, but also which many people then forget all the analytic power on top of SQL. It's one thing to store your data columnar, it's a completely different story to actually be able to run analytics on top of that and having all the built-in functionalities and stuff that you want to do with the data on top of it as you analyze it. >> You know, that's a great example, the kilometer 'cause I remember there was like a lot of hype around it. Oh, it's the Oracle killer, you know, at Vertica. Vertica is still around but, you know it never really hit escape velocity. But you know, good product, good company, whatever. Natezza, it kind of got buried inside of IBM. ParXL kind of became, you know, red shift with that deal so that kind of went away. Teradata bought a company, I forget which company it bought but. So that hype kind of disapated and now it's like, oh yeah, columnar. It's kind of like In-Memory, we've had a In-Memory databases ever since we've had databases you know, it's a kind of a feature not a sector. But anyway, Maria, let's come back to you. You've got a lot of customer experience. And you speak with a lot of companies, you know during your time at Oracle. What else are you seeing in terms of the benefits to this approach that might not be so intuitive and obvious right away? >> I think one of the biggest benefits to having a multimodel multiworkload or as we call it a converged database, is the fact that you can get greater data synergy from it. In other words, you can utilize all these different techniques and data models to get better value out of that data. So things like being able to do real-time machine learning, fraud detection inside a transaction or being able to do a product recommendation by accessing three different data models. So for example, if I'm trying to recommend a product for you Dave, I might use graph analytics to be able to figure out your community. Not just your friends, but other people on our system who look and behave just like you. Once I know that community then I can go over and see what products they bought by looking up our product catalog which may be stored as JSON. And then on top of that I can then see using the key-value what products inside that catalog those community members gave a five star rating to. So that way I can really pinpoint the right product for you. And I can do all of that in one transaction inside the database without having to transform that data into different models or God forbid, access different systems to be able to get all of that information. So it really simplifies how we can generate that value from the data. And of course, the other thing our customers love is when it comes to deploying data-driven apps, when you do it on a converged database it's much simpler because it is that standard data platform. So you're not having to manage multiple independent single purpose databases. You're not having to implement the security and the high availability policies, you know across a bunch of different diverse platforms. All of that can be done much simpler with a converged database 'cause the DBA team of course, is going to just use that standard set of tools to manage, monitor and secure those systems. >> Thank you for that. And you know, it's interesting, you talk about simplification and you are in Juan's organization so you've big focus on mission critical. And so one of the things that I think is often overlooked well, we talk about all the time is recovery. And if things are simpler, recovery is faster and easier. And so it's kind of the hallmark of Oracle is like the gold standard of the toughest apps, the most mission critical apps. But I wanted to get to the cloud Maria. So because everything is going to the cloud, right? Not all workloads are going to the cloud but everybody is talking about the cloud. Everybody has cloud first mentality and so yes, it's a hybrid world. But the natural next question is how do you think the cloud fits into this world of data-driven apps? >> I think just like any app that you're developing, the cloud helps to accelerate that development. And of course the deployment of these data-driven applications. 'Cause if you think about it, the developer is instantly able to provision a converged database that Oracle will automatically manage and look after for them. But what's great about doing something like that if you use like our autonomous database service is that it comes in different flavors. So you can get autonomous transaction processing, data warehousing or autonomous JSON so that the developer is going to get a database that's been optimized for their specific use case, whatever they are trying to solve. And it's also going to contain all of that great functionality and capabilities that we've been talking about. So what that really means to the developer though is as the project evolves and inevitably the business needs change a little, there's no need to panic when one of those changes comes in because your converged database or your autonomous database has all of those additional capabilities. So you can simply utilize those to able to address those evolving changes in the project. 'Cause let's face it, none of us normally know exactly what we need to build right at the very beginning. And on top of that they also kind of get a built-in buddy in the cloud, especially in the autonomous database. And that buddy comes in the form of built-in workload optimizations. So with the autonomous database we do things like automatic indexing where we're using machine learning to be that buddy for the developer. So what it'll do is it'll monitor the workload and see what kind of queries are being run on that system. And then it will actually determine if there are indexes that should be built to help improve the performance of that application. And not only does it bill those indexes but it verifies that they help improve the performance before publishing it to the application. So by the time the developer is finished with that app and it's ready to be deployed, it's actually also been optimized by the developers buddy, the Oracle autonomous database. So, you know, it's a really nice helping hand for developers when they're building any app especially data-driven apps. >> I like how you sort of gave us, you know the truth here is you don't always know where you're going when you're building an app. It's like it goes from you are trying to build it and they will come to start building it and we'll figure out where it's going to go. With Agile that's kind of how it works. But so I wonder, can you give some examples of maybe customers or maybe genericize them if you need to. Data-driven apps in the cloud where customers were able to drive more efficiency, where the cloud buddy allowed the customers to do more with less? >> No, we have tons of these but I'll try and keep it to just a couple. One that comes to mind straight away is retrace. These folks built a blockchain app in the Oracle Cloud that allows manufacturers to actually share the supply chain with the consumer. So the consumer can see exactly, who made their product? Using what raw materials? Where they were sourced from? How it was done? All of that is visible to the consumer. And in order to be able to share that they had to work on a very diverse set of data. So they had everything from JSON documents to images as well as your traditional transactions in there. And they store all of that information inside the Oracle autonomous database, they were able to build their app and deploy it on the cloud. And they were able to do all of that very, very quickly. So, you know, that ability to work on multiple different data types in a single database really helped them build that product and get it to market in a very short amount of time. Another customer that's doing something really, really interesting is MindSense. So these guys operate the largest mines in Canada, Chile, and Peru. But what they do is they put these x-ray devices on the massive mechanical shovels that are at the cove or at the mine face. And what that does is it senses the contents of the buckets inside these mining machines. And it's looking to see at that content, to see how it can optimize the processing of the ore inside in that bucket. So they're looking to minimize the amount of power and water that it's going to take to process that. And also of course, minimize the amount of waste that's going to come out of that project. So all of that sensor data is sent into an autonomous database where it's going to be processed by a whole host of different users. So everything from the mine engineers to the geo scientists, to even their own data scientists utilize that data to drive their business forward. And what I love about these guys is they're not happy with building just one app. MindSense actually use our built-in low core development environment, APEX that comes as part of the autonomous database and they actually produce applications constantly for different aspects of their business using that technology. And it's actually able to accelerate those new apps to the business. It takes them now just a couple of days or weeks to produce an app instead of months or years to build those new apps. >> Great, thank you for that Maria. Gerald, I'm going to push you again. So, I said upfront and talked about microservices and the cloud and containers and you know, anybody in the developer space follows that very closely. But some of the things that we've been talking about here people might look at that and say, well, they're kind of antithetical to microservices. This is our Oracles monolithic approach. But when you think about the benefits of microservices, people want freedom of choice, technology choice, seen as a big advantage of microservices and containers. How do you address such an argument? >> Yeah, that's an excellent question and I get that quite often. The microservices architecture in general as I said before had architectures, Linux distributions, et cetera. It's kind of always a bit of like there's an academic approach and there's a pragmatic approach. And when you look at the microservices the original definitions that came out at the early 2010s. They actually never said that each microservice has to have a database. And they also never said that if a microservice has a database, you have to use a different technology for each microservice. Just like they never said, you have to write a microservice in a different programming language, right? So where I'm going with this is like, yes you know, sometimes when you look at some vendors out there, some niche players, they push this message or they jump on this academic approach of like each microservice has the best tool at hand or I'd use a different database for your purpose, et cetera. Which almost often comes across like us. You know, we want to stay part of the conversation. Nothing stops a developer from, you know using a multimodal database for the microservice and just using that as a document store, right? Or just using that as a relational database. And, you know, sometimes I mean, it was actually something that happened that was really interesting yesterday I don't know whether you follow Dave or not. But Facebook had an outage yesterday, right? And Facebook is one of those companies that are seen as the Silicon Valley, you know know how to do microservices companies. And when you add through the outage, well, what happened, right? Some unfortunate logical error with configuration as a force that took a database cluster down. So, you know, there you have it where you go like, well, maybe not every microservice is actually in fact talking to its own database or its own special purpose database. I think there, you know, well, what we should, the industry should be focusing much more on this argument of which technology to use? What's the right tool for a job? Is more to ask themselves, what business problem actually are we trying to solve? And therefore what's the right approach and the right technology for this. And so therefore, just as I said before, you know multimodal databases they do have strong benefits. They have many built-in functionalities that are already there and they allow you to reduce this complexity of having to know many different technologies, right? And so it's not only to store different data models either you know, treat a multimodal database as a chasing documents store or a relational database but most databases are multimodal since 20 plus years. But it's also actually being able to perhaps if you store that data together, you can perhaps actually derive additional value for somebody else but perhaps not for your application. But like for example, if you were to use Oracle Database you can actually write queries on top of all of that data. It doesn't really matter for our query engine whether it's the data is format that then chase or the data is formatted in rows and columns you can just rather than query over it. And that's actually very powerful for those guys that have to, you know get the reporting done the end of the day, the end of the week. And for those guys that are the data scientists that they want to figure out, you know which product performed really well or can we tweak something here and there. When you look into that space you still see a huge divergence between the guys to put data in kind of the altarpiece style and guys that try to derive new insights. And there's still a lot of ETL going around and, you know we have big data technologies that some of them come and went and some of them came in that are still around like Apache Spark which is still like a SQL engine on top of any of your data kind of going back to the same concept. And so I will say that, you know, for developers when we look at microservices it's like, first of all, is the argument you were making because the vendor or the technology you want to use tells you this argument or, you know, you kind of want to have an argument to use a specific technology? Or is it really more because it is the best technology, to best use for this given use case for this given application that you have? And if so there's of course, also nothing wrong to use a single purpose technology either, right? >> Yeah, I mean, whenever I talk about Oracle I always come back to the most important applications, the mission critical. It's very difficult to architect databases with microservices and containers. You have to be really, really careful. And so and again, it comes back to what we were talking before about with Maria that the complexity and the recovery. But Gerald I want to stay with you for a minute. So there's other data management technologies popping out there. I mean, I've seen some people saying, okay just leave the data in an S3 bucket. We can query that, then we've got some magic sauce to do that. And so why are you optimistic about you know, traditional database technology going forward? >> I would say because of the history of databases. So one thing that once struck me when I came to Oracle and then got to meet great people like Juan Luis and Andy Mendelsohn who had been here for a long, long time. I come to realization that relational databases are around for about 45 years now. And, you know, I was like, I'm too young to have been around then, right? So I was like, what else was around 45 years? It's like just the tech stack that we have today. It's like, how does this look like? Well, Linux only came out in 93. Well, databases pre-date Linux a lot rather than as I started digging I saw a lot of technologies come and go, right? And you mentioned before like the technologies that data management systems that we had that came and went like the columnar databases or XML databases, object databases. And even before relational databases before Cot gave us the relational model there were apparently these networks stores network databases which to some extent look very similar to adjacent documents. There wasn't a harder storing data and a hierarchy to format. And, you know when you then start actually reading the Cot paper and diving a little bit more into the relation model, that's I think one important crux in there that most of the industry keeps forgetting or it hasn't been around to even know. And that is that when Cot created the relational model, he actually focused not so much on the application putting the data in, but on future users and applications still being able to making sense out of the data, right? And that's kind of like I said before we had those network models, we had XML databases you have adjacent documents stores. And the one thing that they all have along with it is like the application that puts the data in decides the structure of the data. And that's all well and good if you had an application of the developer writing an application. It can become really tricky when 10 years later you still want to look at that data and the application that the developer is no longer around then you go like, what does this all mean? Where is the structure defined? What is this attribute? What does it mean? How does it correlate to others? And the one thing that people tend to forget is that it's actually the data that's here to stay not someone who does the applications where it is. Ideally, every company wants to store every single byte of data that they have because there might be future value in it. Economically may not make sense that's now much more feasible than just years ago. But if you could, why wouldn't you want to store all your data, right? And sometimes you actually have to store the data for seven years or whatever because the laws require you to. And so coming back then and you know, like 10 years from now and looking at the data and going like making sense of that data can actually become a lot more difficult and a lot more challenging than having to first figure out and how we store this data for general use. And that kind of was what the relational model was all about. We decompose the data structures into tables and columns with relationships amongst each other so therefore between each other. So that therefore if somebody wants to, you know typical example would be well you store some purchases from your web store, right? There's a customer attribute in it. There's some credit card payment information in it, just some product information on what the customer bought. Well, in the relational model if you just want to figure out which products were sold on a given day or week, you just would query the payment and products table to get the sense out of it. You don't need to touch the customer and so forth. And with the hierarchical model you have to first sit down and understand how is the structure, what is the customer? Where is the payment? You know, does the document start with the payment or does it start with the customer? Where do I find this information? And then in the very early days those databases even struggled to then not having to scan all the documents to get the data out. So coming back to your question a bit, I apologize for going on here. But you know, it's like relational databases have been around for 45 years. I actually argue it's one of the most successful software technologies that we have out there when you look in the overall industry, right? 45 years is like, in IT terms it's like from a star being the ones who are going supernova. You have said it before that many technologies coming and went, right? And just want to add a more really interesting example by the way is Hadoop and HDFS, right? They kind of gave us this additional promise of like, you know, the 2010s like 2012, 2013 the hype of Hadoop and so forth and (mumbles) and HDFS. And people are just like, just put everything into HDFS and worry about the data later, right? And we can query it and map reduce it and whatever. And we had customers actually coming to us they were like, great we have half a petabyte of data on an HDFS cluster and we have no clue what's stored in there. How do we figure this out? What are we going to do now? Now you had a big data cleansing problem. And so I think that is why databases and also data modeling is something that will not go away anytime soon. And I think databases and database technologies are here for quite a while to stay. Because many of those are people they don't think about what's happening to the data five years from now. And many of the niche players also and also frankly even Amazon you know, following with this single purpose thing is like, just use the right tool for the job for your application, right? Just pull in the data there the way you wanted. And it's like, okay, so you use technologies all over the place and then five years from now you have your data fragmented everywhere in different formats and, you know inconsistencies, and, and, and. And those are usually when you come back to this data-driven business critical business decision applications the worst case scenario you can have, right? Because now you need an army of people to actually do data cleansing. And there's not a coincidence that data science has become very, very popular the last recent years as we kind of went on with this proliferation of different database or data management technologies some of those are not even database. But I think I leave it at that. >> It's an interesting talk track because you're right. I mean, no schema on right was alluring, but it definitely created some problems. It also created an entire, you know you referenced the hyper specialized roles and did the data cleansing component. I mean, maybe technology will eventually solve that problem but it hasn't up at least up tonight. Okay, last question, Maria maybe you could start off and Gerald if you want to chime in as well it'd be great. I mean, it's interesting to watch this industry when Oracle sort of won the top database mantle. I mean, I watched it, I saw it. It was, remember it was Informix and it was (indistinct) too and of course, Microsoft you got to give them credit with SQL server, but Oracle won the database wars. And then everything got kind of quiet for awhile database was sort of boring. And then it exploded, you know, all the, you know not only SQL and the key-value stores and the cloud databases and this is really a hot area now. And when we looked at Oracle we said, okay, Oracle it's all about Oracle Database, but we've seen the kind of resurgence in MySQL which everybody thought, you know once Oracle bought Sun they were going to kill MySQL. But now we see you investing in HeatWave, TimesTen, we talked about In-Memory databases before. So where do those fit in Maria in the grand scheme? How should we think about Oracle's database portfolio? >> So there's lots of places where you'd use those different things. 'Cause just like any other industry there are going to be new and boutique use cases that are going to benefit from a more specialized product or single purpose product. So good examples off the top of my head of the kind of systems that would benefit from that would be things like a stock exchange system or a telephone exchange system. Both of those are latency critical transaction processing applications where they need microsecond response times. And that's going to exceed perhaps what you might normally get or deploy with a converged database. And so Oracle's TimesTen database our In-Memory database is perfect for those kinds of applications. But there's also a host of MySQL applications out there today and you said it yourself there Dave, HeatWave is a great place to provision and deploy those kinds of applications because it's going to run 100 times faster than AWS (mumbles). So, you know, there really is a place in the market and in our customer's systems and the needs they have for all of these different members of our database family here at Oracle. >> Yeah, well, the internet is basically running in the lamp stack so I see MySQL going away. All right Gerald, will give you the final word, bring us home. >> Oh, thank you very much. Yeah, I mean, as Maria said, I think it comes back to what we discussed before. There is obviously still needs for special technologies or different technologies than a relational database or multimodal database. Oracle has actually many more databases that people may first think of. Not only the three that we have already mentioned but there's even SP so the Oracle's NoSQL database. And, you know, on a high level Oracle is a data management company, right? And we want to give our customers the best tools and the best technology to manage all of their data. Rather than therefore there has to be a need or there should be a part of the business that also focuses on this highly specialized systems and this highly specialized technologies that address those use cases. And I think it makes perfect sense. It's like, you know, when the customer comes to Oracle they're not only getting this, take this one product you know, and if you don't like it your problem but actually you have choice, right? And choice allows you to make a decision based on what's best for you and not necessarily best for the vendor you're talking to. >> Well guys, really appreciate your time today and your insights. Maria, Gerald, thanks so much for coming on The Cube. >> Thank you very much for having us. >> And thanks for watching this Cube conversation this is Dave Vellante and we'll see you next time. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of Actifio data driven 2020 brought to you by Actifio. >> Welcome back, I'm Stu Miniman. This is the cubes coverage of Actifio data driven, happy to welcome to the program, the Co-founder and Chief Product Officer David Chang with Actifio. Thanks so much for joining us. Great to have you back on theCUBE. >> Great to be here, Stu, thank you. >> All right, so the big theme of the event is the next normal, of course, we've been talking about transformation of data for many years, but the global pandemic has put a real emphasis on some of the transformations that customers are going through and alluding to that next normal because definitely things have changed a bit. What, give us if you could kind of a high level, you know what you've been seeing, you've been there since the start for Actifio. So, you know, what is that next normal for customers? >> Yeah, absolutely, so I would say over the span of the last two years, we've seen definitely a accelerated ramp into the Clouds. But I think this whole pandemic has really accelerated. I mean, this really telltale sign came in, we actually, prior to the pandemic hit, we closed a large European customer. And within a span of two weeks, they were saying, you know, "I can't get access to my data center for all the important work that I have to do." And with Actifio like to move everything into the Cloud. So within the span of three weeks, we were able to move a lot of their critical workloads with them. So I think that gave us the telltale sign that this thing is really truly accelerating. >> Yeah, it absolutely there's that acceleration. It's tough to move data, though. It's not like we can just say, "Okay, hey, you know, we've got petabytes, you know, that the laws of physics still are in place." And also with that move to Cloud, you know, backup and recovery, you know, disaster recovery, you know, still critically important so and any learnings that you've had this year or things where you've had to, you know, help out customers, as they say, "We need to move fast, but we also need to stay secure. And we need to make sure that our data is safe." >> Absolutely, so I think there's a major difference between the lift and shift model in terms of your way at your application infrastructure, and then the actual foundation, building block you're using those pieces are very difficult to lift and shift, because Cloud fundamentally present different set of building blocks. A great example here is that object storage, you know, it's the most scalable and lowest cost storage available in the public sort of Cloud hyperscaler infrastructure. And without that, trying to move to the Cloud would be very difficult indeed, trying to make the infrastructure match. >> So let's dig in and talk a little bit about how Cloud really transforms storage, you know, back in the storage industry, we've talked for a long time that you know, object was the future and that's, what Cloud was built on. So you've got large scalability, you've got some great cost efficiencies. You know, what does that mean for the Actifio solutions and your customer? >> Yeah, I think from the very beginning, I would say recall this conversation three or four years ago, when we were looking at what are some of the next generation architecture we want to build the Actifio technology on? It was very clearly that object storage needs to be front and center in everything we do. It's not a it's a maybe a little known fact. But Amazon AWS service initially started with the S3 architecture and that was the very first service they brought live within the AWS sort of product portfolio. So it is as fundamental to the Cloud as you know, EC2 more so than containers and so on and so forth. And the fact that you have this almost linear scalability horizontally to exabytes of storage and the fact that you can essentially leverage all the performance you need to get out the object storage that's all built into the environment. Those are some of the critical pieces and obviously, the low cost, you know, compared with SSD or spinning drives on the on the EC2 environment, those are all some of the critical elements on why object storage is so critical in this whole Cloud migration, if you will. >> Yeah, I wonder if we could talk a little bit about the application sort of things because of course, the architecture matters, but it's really the the outcomes, it's the reason we have infrastructures for the applications and of course one of the most mission critical applications. We've talked about data, it's those databases. We've seen a lot of transformation in the database world. Most customers I talked to now, it's not their one central source of truth. They now have many databases, especially in the Cloud we've seen that kind of Cambrian explosion of options out there. What does that meant for your customers? You know, take us inside, you know, that most important database world. >> Yeah, I think any customer with their interest to go into the Cloud or minimize the on premise environment anyway, the very first thing they think about is what are I most critical application I need to move right? Database are typically it. You know, there are companies that has a lot of, I would say, projects around migrating some of the traditional databases into NoSQL, or even hosted services like RDS. But I would say the vast majority of the database population that's in fact, that's essentially in production today are some of the traditional databases. So that tend to be also tend to be the most difficult problem in terms of trying to migrate the workload to the Cloud or DR or business continuity into the Cloud. >> So David, how about you know what is new from Actifio now? What should customers be looking at when we talk about the storage capabilities? >> So I would say the first thing is that Actifio allow our customers to kind of maintain the legacy databases they use. And by using Actifio, we normalize the entire Cloud infrastructures. So you can get all the same RPOs and RTOs that you're used to on premise into the Cloud. And through the adoption or of object storage down deep into their foundation blocks of our architecture, now, you can have sort of the best of both world. You can have this on demand capability or using from the public Clouds. You are, you know, getting capability as you need them. But also you can leverage sort of object storage without changing your application architecture, to get that performance and get to the sort of the cost point that you need to make that entire business viable. I think relatively recently, we did ESG sort of project that really validated that you can get 95 to 97% of the performance of SSD, but rather on object storage. And from a cost saving perspective, that cost say that cost actually went down by 88%. So it is indeed the best of both worlds, if you will. >> Yeah, you know, maybe explain that more a little bit more, if you could, yeah. Because, right, you want that scalability, you want high performance, but, you know, there's always been those architectural trade offs. So what is it that Actifio does, you're talking about the object storage that pairing with the Cloud capabilities? Help us understand, you know, what is differentiated about that solution? >> Yeah, absolutely, so I think in some ways, object storage has been getting a bad rap in terms of people's perception of slow performance and so on, so forth. But I think the real reason is because other vendors aren't using it incorrectly if you will. A lot of things we've seen in the past has, like legacy backup vendors taking sort of a looking at object storage as they tape replacement. With all object storage system, there is a fundamental limit on a per object performance you can get out the entire object infrastructure. But really the secret sauce Actifio came up with is to design an infrastructure that natively translate block or file storage that for example, Oracle SQL consumes, and then taking that data, sort of, if you will, from the application perspective, and divided into hundreds of thousands if not millions of objects and that can be spread across the entire object storage infrastructure. And this is how we get you get the performance if you will. That's very very similar if not almost identical to SSD even on object storage. >> Yeah, I saw a blog post on the Actifio site making a comparison to the SnowFlake database, of course, you know, super hot company lots of adoption in their Cloud service, help us understand a little bit, you know that that comparison that your team is making? >> Yeah, absolutely, I think it's a very interesting insight. I think both Actifio and SnowFlake probably independently arrived at the same conclusion about four or five years ago, that object storage is the foundation building block. And this is how you scale massive infrastructure at a cost that's effective for our business models, right? So I think, in many ways, if you look at how SnowFlakes works is they leverage this almost infinite scalability of object storage to consume sort of this data lake to store this data lake, and therefore they can effectively offer that basic service to your customer at a very low cost point. And then when they actually decide, the customer decide to use that information, this is where the business model works and they actually did start charging the customer. So that foundation building block of object storage on, you know, in terms of the fundamental building block for the SnowFlake service, I would argue is also the reasons why they're so popular today. >> Yeah, and David, you know, we've seen, you know, quite a change in the landscape since the early days of Actifio, it's interesting to hear you talk about those analogies with some of those, you know, Cloud native solutions. Give us a little bit of inside, you're the Chief Product officer. You know, what's the biggest change you'd say of Actifio today versus, you know, maybe how, when people first heard of their copy data management, you know, technology? >> Yeah, I would say I think we were kind of fortunate that when we started the company, the fundamental premise of being very efficient, very scalable, and instant reuse is a sort of fundamental premise of our product and architecture that has held true through technology evolution, you know, three or four different waves in the last, I would say 10 years. So I think what's currently the biggest difference between I would say, now versus Actifio five years ago, is that everything with everything we do, we're thinking Cloud first. This is how, you know, essentially, the Actifio platform has evolved into this normalization platform for enterprise customer to achieve the same RPOs and RTO the same applications and be able to using the some of the same building blocks across both their you know, hybrid infrastructure and also public Cloud infrastructure. >> Yeah, absolutely, that hybrid discussion has really dominated a lot of discussion the last couple of years. A challenge for, you know, the engineering teams is architecting into those environments. It's not just once you've got Amazon, you've got Azure, you've got Google, you've got others out there. What do you say? It doesn't feel like we have a standardization. And there's specific work that you need to do. But your ultimate customer, they want to be able to do it the same way no matter where they are. Give us a little bit of what you know, what you're seeing is some of the challenges and how Actifio is facing that. >> Yeah, I think there are fundamentally two ways to go to the Cloud. I think one is to entirely consume a log or higher log of functionality that Amazon Google and Azure has, right? That mean that does mean rewriting your application from scratch to take advantage of that. I think some of the benefit there is you have some very low entry cost and you don't have to worry about operationally how to keep that going. But I think more commonly, what we're seeing customer enterprise customer do is to taking their existing stack, rewriting portions of this and kind of build it on EC2 you know, and a container environment. And those are sort of I think, more of the more popular choices that people are making in terms of making the move to the Cloud at least from my enterprise customer perspective. And that is an area that Actifio could really help, by again, normalizing what they're familiar with on premise to the Cloud and we can provide the same service level and provide really this level of flexibility for you to shift workloads back and forth to make that work for your business case. >> Yeah, I'm curious, I remember back you talked about Actifio five years ago, and some of the early days it was like, "Well, you know, the traditional storage companies might not like Actifio because at the end of the day, they're going to sell less capacity. And that's really how they price things." Feels like the Cloud providers, think about it very different, you don't really think as much about, you know, "I don't buy capacity, I have scalability, I build out my applications in a certain way." Do you see that Cloud model taking over any other comparisons you'd make kind of the Cloud world versus the data center world? >> Yeah, I think it really I think that really the switches is very, very telling, right? It's very, I would say in some ways surprised a lot of people at the pace and and that it has happened. But I think it is, that pace is pretty solid at this point. I mean, we are seeing broad adoption of sort of that strategy all over the world, and it's only accelerating. >> All right, final question I have let's bring it on home that next normal, what do you want customers to have as their takeaway from this year's data driven event? >> I think they are, I think the probably the most important thing we want to communicate to our customer and potential prospect is that you can have the best of both world, right? It's not a one or two or other decision you have to make, you could be in the Cloud and enjoy a lot of the same benefits and saw a lot of the same service level that you're used to, but taken advantage of that, you know, there is a separate, very large company running world class operations for you in the Cloud. The elasticity of that capability is very important as well. But with Actifio without having to rewrite your application per se, you can have advantage if you will to of the new world, still maintaining the presence of the old and you can manage both environment in the same way. >> David Chang, thank you so much for the updates. Great to catch up with you and thanks for having us at the event. >> Thank you, Stu for having me. >> I'm Stu Miniman, and thank you for watching theCUBE. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of Actifio data driven 2020 brought to you by Actifio. >> Welcome back, I'm Stu Miniman. This is the cubes coverage of Actifio data driven, happy to welcome to the program, the Co-founder and Chief Product Officer David Chang with Actifio. Thanks so much for joining us. Great to have you back on theCUBE. >> Great to be here, Stu, thank you. >> All right, so the big theme of the event is the next normal, of course, we've been talking about transformation of data for many years, but the global pandemic has put a real emphasis on some of the transformations that customers are going through and alluding to that next normal because definitely things have changed a bit. What, give us if you could kind of a high level, you know what you've been seeing, you've been there since the start for Actifio. So, you know, what is that next normal for customers? >> Yeah, absolutely, so I would say over the span of the last two years, we've seen definitely a accelerated ramp into the Clouds. But I think this whole pandemic has really accelerated. I mean, this really telltale sign came in, we actually, prior to the pandemic hit, we closed a large European customer. And within a span of two weeks, they were saying, you know, "I can't get access to my data center for all the important work that I have to do." And with Actifio like to move everything into the Cloud. So within the span of three weeks, we were able to move a lot of their critical workloads with them. So I think that gave us the telltale sign that this thing is really truly accelerating. >> Yeah, it absolutely there's that acceleration. It's tough to move data, though. It's not like we can just say, "Okay, hey, you know, we've got petabytes, you know, that the laws of physics still are in place." And also with that move to Cloud, you know, backup and recovery, you know, disaster recovery, you know, still critically important so and any learnings that you've had this year or things where you've had to, you know, help out customers, as they say, "We need to move fast, but we also need to stay secure. And we need to make sure that our data is safe." >> Absolutely, so I think there's a major difference between the lift and shift model in terms of your way at your application infrastructure, and then the actual foundation, building block you're using those pieces are very difficult to lift and shift, because Cloud fundamentally present different set of building blocks. A great example here is that object storage, you know, it's the most scalable and lowest cost storage available in the public sort of Cloud hyperscaler infrastructure. And without that, trying to move to the Cloud would be very difficult indeed, trying to make the infrastructure match. >> So let's dig in and talk a little bit about how Cloud really transforms storage, you know, back in the storage industry, we've talked for a long time that you know, object was the future and that's, what Cloud was built on. So you've got large scalability, you've got some great cost efficiencies. You know, what does that mean for the Actifio solutions and your customer? >> Yeah, I think from the very beginning, I would say recall this conversation three or four years ago, when we were looking at what are some of the next generation architecture we want to build the Actifio technology on? It was very clearly that object storage needs to be front and center in everything we do. It's not a it's a maybe a little known fact. But Amazon AWS service initially started with the S3 architecture and that was the very first service they brought live within the AWS sort of product portfolio. So it is as fundamental to the Cloud as you know, EC2 more so than containers and so on and so forth. And the fact that you have this almost linear scalability horizontally to exabytes of storage and the fact that you can essentially leverage all the performance you need to get out the object storage that's all built into the environment. Those are some of the critical pieces and obviously, the low cost, you know, compared with SSD or spinning drives on the on the EC2 environment, those are all some of the critical elements on why object storage is so critical in this whole Cloud migration, if you will. >> Yeah, I wonder if we could talk a little bit about the application sort of things because of course, the architecture matters, but it's really the the outcomes, it's the reason we have infrastructures for the applications and of course one of the most mission critical applications. We've talked about data, it's those databases. We've seen a lot of transformation in the database world. Most customers I talked to now, it's not their one central source of truth. They now have many databases, especially in the Cloud we've seen that kind of Cambrian explosion of options out there. What does that meant for your customers? You know, take us inside, you know, that most important database world. >> Yeah, I think any customer with their interest to go into the Cloud or minimize the on premise environment anyway, the very first thing they think about is what are I most critical application I need to move right? Database are typically it. You know, there are companies that has a lot of, I would say, projects around migrating some of the traditional databases into NoSQL, or even hosted services like RDS. But I would say the vast majority of the database population that's in fact, that's essentially in production today are some of the traditional databases. So that tend to be also tend to be the most difficult problem in terms of trying to migrate the workload to the Cloud or DR or business continuity into the Cloud. >> So David, how about you know what is new from Actifio now? What should customers be looking at when we talk about the storage capabilities? >> So I would say the first thing is that Actifio allow our customers to kind of maintain the legacy databases they use. And by using Actifio, we normalize the entire Cloud infrastructures. So you can get all the same RPOs and RTOs that you're used to on premise into the Cloud. And through the adoption or of object storage down deep into their foundation blocks of our architecture, now, you can have sort of the best of both world. You can have this on demand capability or using from the public Clouds. You are, you know, getting capability as you need them. But also you can leverage sort of object storage without changing your application architecture, to get that performance and get to the sort of the cost point that you need to make that entire business viable. I think relatively recently, we did ESG sort of project that really validated that you can get 95 to 97% of the performance of SSD, but rather on object storage. And from a cost saving perspective, that cost say that cost actually went down by 88%. So it is indeed the best of both worlds, if you will. >> Yeah, you know, maybe explain that more a little bit more, if you could, yeah. Because, right, you want that scalability, you want high performance, but, you know, there's always been those architectural trade offs. So what is it that Actifio does, you're talking about the object storage that pairing with the Cloud capabilities? Help us understand, you know, what is differentiated about that solution? >> Yeah, absolutely, so I think in some ways, object storage has been getting a bad rap in terms of people's perception of slow performance and so on, so forth. But I think the real reason is because other vendors aren't using it incorrectly if you will. A lot of things we've seen in the past has, like legacy backup vendors taking sort of a looking at object storage as they tape replacement. With all object storage system, there is a fundamental limit on a poor object performance you can get out the entire object infrastructure. But really the secret sauce Actifio came up with is to design an infrastructure that natively translate block or file storage that for example, Oracle SQL consumes, and then taking that data, sort of, if you will, from the application perspective, and divided into hundreds of thousands if not millions of objects and that can be spread across the entire object storage infrastructure. And this is how we get you get the performance if you will. That's very very similar if not almost identical to SSD even on object storage. >> Yeah, I saw a blog post on the Actifio site making a comparison to the SnowFlake database, of course, you know, super hot company lots of adoption in their Cloud service, help us understand a little bit, you know that that comparison that your team is making? >> Yeah, absolutely, I think it's a very interesting insight. I think both Actifio and SnowFlake probably independently arrived at the same conclusion about four or five years ago, that object storage is the foundation building block. And this is how you scale massive infrastructure at a cost that's effective for our business models, right? So I think, in many ways, if you look at how SnowFlakes works is they leverage this almost infinite scalability of object storage to consume sort of this data lake to store this data lake, and therefore they can effectively offer that basic service to your customer at a very low cost point. And then when they actually decide, the customer decide to use that information, this is where the business model works and they actually did start charging the customer. So that foundation building block of object storage on, you know, in terms of the fundamental building block for the SnowFlake service, I would argue is also the reasons why they're so popular today. >> Yeah, and David, you know, we've seen, you know, quite a change in the landscape since the early days of Actifio, it's interesting to hear you talk about those analogies with some of those, you know, Cloud native solutions. Give us a little bit of inside, you're the Chief Product officer. You know, what's the biggest change you'd say of Actifio today versus, you know, maybe how, when people first heard of their copy data management, you know, technology? >> Yeah, I would say I think we were kind of fortunate that when we started the company, the fundamental premise of being very efficient, very scalable, and instant reuse is a sort of fundamental premise of our product and architecture that has held true through technology evolution, you know, three or four different waves in the last, I would say 10 years. So I think what's currently the biggest difference between I would say, now versus Actifio five years ago, is that everything with everything we do, we're thinking Cloud first. This is how, you know, essentially, the Actifio platform has evolved into this normalization platform for enterprise customer to achieve the same RPOs and RTO the same applications and be able to using the some of the same building blocks across both their you know, hybrid infrastructure and also public Cloud infrastructure. >> Yeah, absolutely, that hybrid discussion has really dominated a lot of discussion the last couple of years. A challenge for, you know, the engineering teams is architecting into those environments. It's not just once you've got Amazon, you've got Azure, you've got Google, you've got others out there. What do you say? It doesn't feel like we have a standardization. And there's specific work that you need to do. But your ultimate customer, they want to be able to do it the same way no matter where they are. Give us a little bit of what you know, what you're seeing is some of the challenges and how Actifio is facing that. >> Yeah, I think there are fundamentally two ways to go to the Cloud. I think one is to entirely consume a log or higher log of functionality that Amazon Google and Azure has, right? That mean that does mean rewriting your application from scratch to take advantage of that. I think some of the benefit there is you have some very low entry cost and you don't have to worry about operationally how to keep that going. But I think more commonly, what we're seeing customer enterprise customer do is to taking their existing stack, rewriting portions of this and kind of build it on EC2 you know, and a container environment. And those are sort of I think, more of the more popular choices that people are making in terms of making the move to the Cloud at least from my enterprise customer perspective. And that is an area that Actifio could really help, by again, normalizing what they're familiar with on premise to the Cloud and we can provide the same service level and provide really this level of flexibility for you to shift workloads back and forth to make that work for your business case. >> Yeah, I'm curious, I remember back you talked about Actifio five years ago, and some of the early days it was like, "Well, you know, the traditional storage companies might not like Actifio because at the end of the day, they're going to sell less capacity. And that's really how they price things." Feels like the Cloud providers, think about it very different, you don't really think as much about, you know, "I don't buy capacity, I have scalability, I build out my applications in a certain way." Do you see that Cloud model taking over any other comparisons you'd make kind of the Cloud world versus the data center world? >> Yeah, I think it really I think that really the switches is very, very telling, right? It's very, I would say in some ways surprised a lot of people at the pace and and that it has happened. But I think it is, that pace is pretty solid at this point. I mean, we are seeing broad adoption of sort of that strategy all over the world, and it's only accelerating. >> All right, final question I have let's bring it on home that next normal, what do you want customers to have as their takeaway from this year's data driven event? >> I think they are, I think the probably the most important thing we want to communicate to our customer and potential prospect is that you can have the best of both world, right? It's not a one or two or other decision you have to make, you could be in the Cloud and enjoy a lot of the same benefits and saw a lot of the same service level that you're used to, but taken advantage of that, you know, there is a separate, very large company running world class operations for you in the Cloud. The elasticity of that capability is very important as well. But with Actifio without having to rewrite your application per se, you can have advantage if you will to of the new world, still maintaining the presence of the old and you can manage both environment in the same way. >> David Chang, thank you so much for the updates. Great to catch up with you and thanks for having us at the event. >> Thank you, Stu for having me. >> I'm Stu Miniman, and thank you for watching theCUBE. (upbeat music)

(upbeat music) >> From our studios in the heart of Silicone Valley Palo Alto, California. This is a CUBE conversation. >> Hello everyone. Welcome to this CUBE conversation here at our Palo Alto CUBE studios. I'm John Furrier, host of theCUBE. Got a great conversation here with Matt Cain, CEO of Couchbase. Matt, welcome to theCUBE. >> John, thanks for having me here. >> So it's great to have you on because we've been following Couchbase really from the beginning but in 2011 that was the big movement with Couchbase and Membase coming together. Since then quite a tear. Couple of things, one from a business standpoint, good mix of you guys. And then you've got the cloud trend just absolute change the game with scale. So enterprise is now a reeling, cloud is there, the roll of data's changed. Now data's now a part of everything. This has been a big part of the successful companies in this next cloud 2.0 or this next shift. Give us an update on Couchbase. What's going on with the company? You've been the CEO for a couple of years, what's new? >> Yeah, so I'm 2 1/2 years in, John. It's been a great ride so far. Let's talk a little bit about how successful the company is and then we'll spend some time on the market. We just finished the first half of our fiscal year and the business is on a phenomenal trajectory. We're up 70% year on year. Average contract values up 50%. Total contract value up over 100%. We now call 30% of the Fortune 100 customers. So in terms of business success we're really proud of what we're able to do and the problems that we're solving for our customers. The backdrop, and what we're so excited about is the market transition that we're participating in. And it's our belief at Couchbase that the world of databases represents the single biggest market transition that's going to occur in technology over the next couple years. And I think there are two fundamental drivers behind that transition which you talked about. One of them is a technology disruption and the other is business disruption. On the business side we believe deeply in digital transformation or the fourth industrial revolution. And we spend our time going around the world talking to enterprise customers and everyone of 'em is figuring out how to use technology to get closer to their customers and change their business. In order to do that they need to build next generation applications that change our customer experience as both professionals and our personal lives. To enable that though, you need a completely different approach to the database. And how you manage the underlying data to enable those experiences and Couchbase sits at the intersection of those two transitions. >> Want to get into some of the database software dynamics from being a software company, a database company. You guys are, you're on a good wave, you've got a good surfboard as we say in California. But the couple of things I want to get your thoughts on, you see the database market like the oracles of the world. The database that rules the world, that's changed. Now there's multiple databases out there. Different needs for different workloads. And then you've got open-source. So you've got the two things going on I want to get your reaction to. One is the changing landscape of the database market. And two, the impact of open-source because both have been changing and growing and evolving. What's your reaction to those two dynamics? >> So let's talk databases first. I think to reflect on databases one needs to think about the applications that those databases have been architected to support. And if you look at legacy solutions, legacy systems, it was really built on relational technology. And the applications those were optimized for and have been really running for the last many decades were big monolithic applications. And I like to say the implementation of one of those at a large financial firm in New York probably wasn't much different than a consumer company in Seattle. That is changing now in the world of microservices and customer experiences and applications demand a different type of database. And so as we think about what is an application literally everything that we do between the human world and the digital world goes via an application. Whether it's our, you know, checking our banking statements, how we engage with our health care provider, how we travel, how we buy things, whether we're in a store or we're doing it from the comfort of our home. Everything is via an application and what we've come to expect is I want that application to work my way which is different than your way. Well that's a very different thing than legacy applications that were built for CRM or ERP and so databases are going through this big transformation because of that business transition that I talked about where we as consumer are demanding different ways of engaging. And if you look at enterprise success in digital transformation it's very tied to the experiences that they're creating which necessitate a database that is capable of handling those. So we're seeing a massive shift in database technologies or proliferation of new companies that are supporting next generation applications. With respect to open-source, when I talk to enterprises they want the flexibility of a new way of acquiring technology. And people are very used to, "I want to examine things "in the way I want to learn about it. "And I want to play with technology "to make sure that it's going to meet my needs." In the case of databases, does it have the scale and performance? Does it have the usability? And so as an open-source company we want to enable our application developers, our enterprise architects, our dev-ops teams to use the technology and see what's it like. And I think enterprises really appreciate that model. So I think open-source is not only unique to databases, it's how enterprises want to-- >> And certainly is growing and changing as well. So you mentioned open-source and databases. I want to get your thoughts on the cloud impact because if you look at the success of Amazon which I call them the leaders and they won the cloud 1.0 game, or the first inning, or the first game of the double header as some say. APIs led itself well to decoupling and creating highly cohesive workloads. Using APIs and (mumbles). There you got to store data in the databases. You might have one workload with one database and another workload using other databases. So have you have a diverse database landscape. >> For sure. >> So that's kind of out there. So if that's the case how do I as an enterprise deal with this because now I'm thinking, "Okay, I want to stitch it all together. "I got to maintain security. "Now I'm dealing with multiple clouds." It's become a discussion and design point for dealing with all these new dimensions. What's the mind of the customer in all this? >> Yeah, and on top of that I want to do it without dramatically increasing my total cost of ownership. And so I talk a lot to enterprises that represent that very challenge. What they say is I have to change the customer experience. In order to do that I need to understand who they are. What are their preferences? What inventory do I have as an organization? What do I have in physical locations? What we talk about is different data silos. And the reality is data has been in those silos for a long time and in some cases it's not coming out anytime soon. So one of the new approaches with data platforms is how do I take advantage of existing investment and infrastructure and layer in new technology platforms that can sit between the application and the legacy systems? And then you can suck that data into a data store that is helping feed the applications on a real time basis whether that's in the cloud or out to the edge. And Couchbase is one of the examples of a database that can handle that but can handle it at scale unlike any other company on the planet. So when we talk to customers it's how do you extract all that different information which has rich potential if they application logic can present it in a way that's customized but do that in a way that's constantly on, available from anywhere in the network topology and reliable. So it is a challenge and it's one of the greatest computer science challenges in the enterprise right now. >> On that point I want to ask you, what's the number one story or trend that people should be paying attention to? >> Yeah, so you asked a question on cloud, which I think is fundamental, and enterprise is like pay as you go models and utilization based economics which make complete sense. A lot of the architecture therefor is being driven in a centralized manor. So bring information into centralized cloud take advantage of bundling effects. I believe that one of the best kept secrets if you will or biggest trends that people aren't spending as much time on is edge. If you think about us in this studio right now there isn't a cloud sitting behind us and yet you're working on your machine, I was on my device a moment ago and I'm expecting real time information across all my applications. We are constantly manipulating, moving, accessing data and we expect to be able to do that at all times. Well in order to do that at the scale in which we're talking you have to have database technology at the edge. And by definition if you're expecting a roundtrip of data processing, which you're potentially doing, is increasing latency. And that's if you have a reliable connection. If you don't have a reliable connection you're dead in the water with it with that application. So if you think about the future of healthcare, if you think about next generation retail, if you think about connected homes and connected cars, the reality is we're going to expect massive processing of data out at the edge. And I think data platform companies have to be mindful of what they're architecting for. Now Couchbase is uniquely positioned in NoSQL databases that we can run in any public cloud and we can run that same platform out to the edge and orchestrate the movement of applications and data between every point of the network topology. And that's when our enterprises say, "Wow, this is game changing technology "that allows me to serve my customers "the way they want to be served." >> Most people might not know this about you, and I'm going to put you on the spot here, is that you had almost a 10 year run at Cisco. >> Yeah, that's right. >> From the 2000 timeframe. Those were the years that Cisco was cutting its teeth into going from running the internet routes to building application layers and staring see... And the debate at that time was should Cisco move up the stack. I'm sure you were involved in a lot of those conversations. They never did and they're kind of staying in their swim lane. But the network is the network and we're in a distributed network with the cloud, so the question is what is the edge now? So is the edge just the network edge? Is it the persons body? Is it the wearable? How do you guys define the edge? >> I think the edge is constantly being pushed further and further, right? One of the things that we talk a lot about is mobile devices, right? If we think about the device that we as humans ultimately touch at the end where we're not dependent on sensors and things, it is our mobile devices and we all know the impact that's had. I'd be willing to bet you that cup of coffee that you have Couchbase database running in your mobile device because we can actually embed it inside the application and allow the application architect to determine how much data you want to use. But the way we've architected things is we think for the future. This isn't just mobile devices, this is the ability to put things directly into sensors. And if we think about how applications are working the amount of data that you can draw with machine learning algorithms, which we've enabled in our latest release, imagine a world where we're embedding a database instance inside of a sensor. So companies aren't quite there today, but we're not that far off where that's going to be the case. >> Well I bring up the Cisco example because you obviously at that time the challenge was moving packets around from point A to point B. You mentioned storage, you store things from here to there. Move packets around in point A to point B. That's the general construct. But when we think about data they're not packets you're talking about sometimes megabytes and betabytes of data. So the general theme is don't move data around the network. How does that impact your business? How does that impact a customer? Because okay they maybe have campuses or wide area networks or SD-WAN, whatever they got. They still want a instrument, they still want to run compute at the edge, but moving the data around has become persona non gratae in **. So how do people get around that? What's the design point? >> So you and I remember these examples when we use to go into conference rooms and ask for ethernet cables, right? The days of what is my wifi connectivity weren't there yet. If we think about that philosophical challenge that was I'm used to a certain experience with connectivity, how do I enable that same connectivity and performance as I get further and further away from the central topology? And so what we did at Cisco is put more and more sophistication into branch routing and make sure that we had reliability and performance between all points of the topology. The reality is if you were to take that same design approach to databases, what you end up with is that centralized cloud model which a lot of companies have chosen. The problem with it occurs when you're running truly business critical applications that demand real-time performance and processing of massive applications. And so-- >> Like what, retail? >> Yeah. So at Couchbase what we've decided to do is take the data logic where the data resides. So we actually now call four of the top 10 retailers in the world customers. And what they are doing is changing our experience as consumers. Omnichannel. When I walk into a store, imagine if you're at a do-it-yourself retailer, somethings popped off the back of your washing machine and you say, "I don't know how old the washing machine is. "I don't know what the part is." Go into one of these mega stores that we know, with the application now via Couchbase in a mobile phone I could take a picture of that. With machine learning algorithms I'm now running technology to say, "Do I have this in inventory?" "What is it compatible with?" "Oh, and it happens to be on aisle 5." Or, "We don't have it and we're going to ship it out." I mean that's game-changing stuff. Well to enable that use case I need to understand who you are. I need to know what you've bought before. I need to understand our product catalog, what things are compatible with. You're literally storing, in that case, three or four billion instances in a data store that you need to access on a real-time basis. >> In milliseconds. >> In less than 2 1/2 second millisecond response rates. To make the challenge even more exciting, those customers come to us and they say, "Well what if there's a hurricane?" "What if there is no internet connectivity?" "What if I don't have a cellular connection?" I still want my users to have a great customer experience. Well now all of a sudden that isn't an extension of a cloud, that becomes it's own cloud. Now to orchestrate the movement of information and applications from that point and have consistency across all your other stores, you need to figure out orchestrating applications, orchestrating massive amounts of data, having consistency. And so the way to do it, bring the data logic where the data resides and then really understand how applications want to move things around. >> So first of all, my database antenna goes up. The comparison of the old days was you had to go to a database, run packets across the network, access the database, do a lookup, send it back and then go back again. >> Right, right. And that's not possible. That's interesting modern approach. But you also mentioned all that complexity that's involved in that. Okay, no power or no connectivity you have to have an almost a private cloud instance right there. I mean this is complex. >> Very complex. >> And this is some of the kinds of things we saw with the recent Jedi proposal that Amazon and Microsoft fought over. Microsoft won to deal with the battle fields. All this complexity where there's no bandwidth, you got to have the data stored locally, it's got to use the back hall properly. So there's a lot of things going on in the system. There's a lot to keep track of. How do you guys manage that from a product standpoint because there's somethings are out of your control. >> Yeah. >> How does Couchbase make that scale work? >> So that's a great question. Let me again complete the problem statement which is databases need to account for all that complexity but application developers and dev-ops teams don't want to deal with the specifics of a database. And so when we're selling into enterprises at this magnitude we need to be very relevant to application developers where they want speed and agility and familiarity of tools they know and yet we need to have the robustness and completeness of a platform that can literally run business critical applications. And so part of the power of Couchbase is that we engineer with extreme elegance, that we put a lot of that sophistication into the database and our job is to write the code that manages that complexity. But what we also do is we go to enterprise and we say we give you the full power of this NoSQL engine that is in memory, shared nothing, scale out, highest performance on the planet but we allow you all the power and familiarity of the language you know which is SQL. You've got this, I'm sure back to your database education you were familiar with, SQLs a programing language, well there's an entire world of database people and architects that understand that as an interface. So how do I account for that complexity but then go to you and say, "You know that language "that you've been speaking the whole time "talking to your old database? "Well you can speak with that same language "on your new database." And that's how you can really break through enabling customers to modernize their applications with all this complexity but do so in a way that they're comfortable with and is aligned to the skills that they-- >> So you extract away the interface, or language NoSQL I know there are others and modernize onto the covers? >> Correct. >> And at scale? >> At the highest scale. >> All right, I got to ask you about multi-cloud because multi-cloud is something that we were talking before we came on camera around cloud sprawl, inheriting clouds, M&A. Companies have multiple clouds they're dealing with but no one's, well my opinion, no one's architecting to build the best multi-cloud system. They're dealing with multi-clouds and design point which you mentioned which is interesting. I want to get your thoughts on this because you're hearing a lot of multi-cloud buzz. And it's a reality but it's also a challenge for application developers. And I want to get your thoughts on this. How should people thinking about multi-cloud in your opinion? >> Yeah, so my perspective starts with what we hear from our customers. And our customers say for truly business critical applications that they are running their business on, whether it's core booking engines, customer platforms, the touchpoint between users and stores, they say, "Look, I need to design a system "that's reliable and higher performing "and public cloud is a reality. "At the same time I have legacy data center on-prem, "I've got things out at the edge," and so they have to architect a multi-cloud, hybrid cloud, and distributed environment. And so depending on the layer of the stack that you're in I think the cloud companies would talk about their multi-cloud strategy. I come at it a different way which is how do we build a data platform that supports the applications that demand a hybrid multi-cloud environment? And so when we have a certain application that's running on-prem, how do we alive for a reliable failover instance to be running in a public cloud? To me that is truly fulfilling on the demands that enterprises have. And so I think multi-cloud is a strategy of all enterprises. Giving the flexibility with things like Kubernetes to avoid cloud lock in. Making sure your system can handle migration of workloads and active, active, active, passive scenario. I think that's our approach to multi-cloud. >> It's interesting, again back to this Jedi thing which was front and center in the news. Kind of speaks to the modern era of what the needs are. The Department of Defense has a multi-cloud strategy, they have multiple clouds, and well turns out Microsoft might be the sole source. But their idea was it's okay to have a sole source cloud for a workload but still deal within a multi-cloud framework. What's your thoughts on this? Some people are saying, "Hey, if you've got a workload "that runs great on cloud, do it." >> Yeah. I don't want to make that decision for the enterprise, I want them to determine what the best instance is based on the application that they're enabling. So I ask all my enterprise customers, "How many applications do you have in your environment?" Thousands of applications. It would be wrong for me to go dictate and say, "Well I have the answer "for every one of those applications." Instead we want to build a sophisticated platform that says look, if these are the requirements, the performance requirements, run your database in this instance and you determine if that's the best for you. If you have a legacy application that needs an underlying mainframe or relational database, that's fine. We're not asking you to forklift upgrade that. Put the database in there that's going to give you the performance and requirements you want. And so again, it's where do application developers want to stand up their application for the best performance? I'll tell you what, in the 2 1/2 years I've been at Couchbase I've sat down with Fortune 100 CIOs that have absolutely told me, "Here is our cloud strategy "with public cloud vendor number one." Come back two years later and they said, "We have shifted for X, Y, and Z reason "and we are going to public cloud vendor number two." If we had chosen one specific deployment and not given thought to how enterprises are eventually going to want to have that flexibility we would be having a very different conversation. And so when we talk about we're enterprise class, multi-cloud to edge, NoSQL database, it's giving enterprises this flexibility at a database-- >> So on that example of I went with cloud number one and then moved to cloud number two, was that a I'm stopping with cloud one going to cloud two or I'm going to move a little bit to cloud two or both? >> I think it varies depending on the CIO that you're talking to. It could be they didn't handle GDPR the way I wanted to or it could be they're not deployed in a certain geographic reason. It could be-- >> Capabilities issue. >> Capabilities. Could be business relationship. You know, I have a particular commercial relationship over here therefor I have an incentive to move here. Some of 'em have dual strategies, so I think it's very dangerous for companies like us to try to-- >> Beauty's in the eye of the beholder as I always say with cloud. You pick your cloud based on what you're trying to do. Final question, security obviously, cloud security you're seeing. Amazon just had a recent even called re:Inforce which was I think the first cloud security show, RSA, there's a bunch of other shows that go on, they're all different. But security clearly is being baked in everywhere. Kind of like data, kind of horizontally embedded, need real time, you need a lot of complexity involved. They want to make it easier. What's your view on how security is playing out for Couchbase? >> Look, it's a paramount design principle for us. And we think that to build a database for business critical applications you need to have reliability, you need to have performance, you need to have scalability, you have to have security. So it's part of how we think about every component from cloud to edge and everything in between. How do we have encryption? How do we have multi-factor authentication? How do we ensure that not just securing the data itself, but how do we give the operational controls to the database teams to orchestrate the movement of data and synchronize it in a reliable way. So absolutely important to us because it's important to our customers. >> Awesome. Matt Cain, CEO of Couchbase here inside theCUBE for CUBE conversation. Matt, I want to give you a chance to get the plug in for the company. Give the pitch if I'm a customer or prospect. Hey Couchbase I heard a little buzz. You guys got momentum going on, got good references. What's the pitch to me? >> Yeah so look, Couchbase is the only company on the planet that can make the following claim. We bring the best of NoSQL with the power and familiarity of SQL in one elegant solution from the public cloud to the edge. So let me walk through that. Our architecture was enabled for the highest performance in the world. Billions of documents. We have a customer who on a daily basis is running 8 million operations per second with less than two millisecond response time. Their business is running on Couchbase. You can't do that if you have the best data schema, the architecture for scalability, scale out, do that at high total cost of ownership. At the same time we want to bring the familiarity of programing languages that people know so that application developers don't have a big barrier to entry in deploying Couchbase. And that's where we've uniquely enabled the SQL query language for both query's, our operational analytics capability, that combination is extremely powerful. To be able to run in anyone of the public clouds, which we do via the marketplace or customers bring in their own nodes to their instances knowing that that's a changing thing per our conversation. But having a seamless integrated platform where you can run the same query in the public cloud as you can at the edge and then synchronizing that back together, that is a very powerful thing. One elegant platform we have, you know, we're a multi-model database. We can run a key-value cache, we can run a JSON database. We give you advanced querying, we give you indexing. To do that in one integrated platform no one else has thought about that and future proof their solution. Let me give you an example of how that all wraps up. One of the more innovative industries right now believe it or not, are cruise lines. And so we talk about digital transformation which is by definition customer experience. Well if you're in the cruise line business, if you're not creating a great customer experience, it's not like airline travel where you've got to get from point A to point B so you chose the best. This is I'm opting for an experience if this isn't great. so one of the most leading edge cruise lines out there has deployed Couchbase and they give every passenger a wearable. That wearable now fundamentally changes the interface between me as a passenger and the physical boat, the digital services, and the other people on the ship. And this is in a world... It's a floating device. There is no cloud, there is no cellular connections. So let's say we happen to be on the same ship. We end up at sports bar after we drop our family off, maybe we're talking databases, maybe we're talking something else. And we have beer, we have a second beer, what we don't know is that this cruise line is using our device. They know who we are, they know where we are, they're using geospatial technology back in e-commerce. They have a hypothesis that we're now friends, right? Or at least maybe we want to see each other again. Unbeknownst to us the next day we get a promotion that says 50% off at the sports bar for the next game. Wow that's great, I'm going to go. And then I run into you and it's like, "Wow, what are the chances that I run into you?" Well the chances in the old world very slim. The chances in new world very good. If I had little kids the digital content in the cabin is different. If there's a movie getting out how it navigates me around the ship is different. All of this is empowered by massive amounts of data processing, data collection and they've embedded that now in a device. Now if you're in that business and now you've got weeks worth of information on what we like, ship comes back to shore, how do you take all that information, extract it back to a cloud, improve the algorithm, start to offer different shipping option. They're literally changing the physical display of the boats to optimize customer experience. So think about that. Power of processing massive amounts of information in real time. If I'm getting a promotion and it's too late and I miss a game, does me no good. The combination of all those different data silos, right? Doing that where application developers can be agile and swift and make changes in an innovative way and stay ahead of their competition. Cloud to edge. Right? I mean that's literally a ship comes back, it goes to cloud, it enables it in this consistent... We're the only company on the planet that can do that. >> Lot of complexity involved. >> Yeah. >> Awesome. Quick plug. Are you guys hiring? What's going on with the company? What are you looking for? >> As quickly as possible. Based on our conversation earlier and your knowledge of databases, we're looking for quota carriers and engineers. So if you want to come on over we're-- >> I was thinking about the cruise ship and having a couple of beers with you watching some sports. My (mumbles) says >> Sounds like sports-- >> "Hey John's had so many beers "why don't you hit the tables?" >> Sounds like-- >> "We'll take your money." >> Sound like more a rep than an engineer. (both laughing) >> Matt, thanks for coming to theCUBE. Really appreciate it. Matt Cain, CEO of Couchbase. I'm John Furrier with theCUBE. Thanks for watching. (upbeat music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Splunk .conf19. Brought to you by Splunk. Okay, welcome back, everyone. This is day three live CUBE coverage here in Las Vegas for Splunk's .conf. Its 10 years anniversary of their big customer event. I'm John Furrier, theCUBE. This is our seventh year covering, riding the wave with Splunk. From scrappy startup, to going public company, massive growth, now a market leader continuing to innovate. We're here with the CEO, Doug Merritt of Splunk. Thanks for joining me, good to see you. >> Thank you for being here, thanks for having me. >> John: How ya feelin'? (laughs) >> Exhausted and energized simultaneously. (laughs) it was a fun week. >> You know, every year when we have the event we discuss Splunk's success and the loyalty of the customer base, the innovation, you guys are providing the value, you got a lot of happy customers, and you got a great ecosystem and partner network growing. You're now growing even further, every year it just gets better. This year has been a lot of big highlights, new branding, so you got that next level thing goin' on, new platform, tweaks, bringing this cohesive thing. What's your highlights this year? I mean, what's the big, there's so much goin' on, what's your highlights? >> So where you started is always my highlight of the show, is being able to spend time with customers. I have never been at a company where I feel so fortunate to have the passion and the dedication and the enthusiasm and the gratitude of customers as we have here. And so that, I tell everyone at Splunk this is similar to a holiday function for a kid for me where the energy keeps me going all year long, so that always is number one, and then around the customers, what we've been doing with the technology architecture, the platform, and the depth and breadth of what we've been working on honestly for four plus years. It really, I think, has come together in a unique way at this show. >> Last year you had a lot of announcements that were intentional announcements, it's coming. They're coming now, they're here, they're shipping. >> They're here, they're here. >> What is some of the feedback you're hearing because a lot of it has a theme where, you know, we kind of pointed this out a couple of years ago, it's like a security show now, but it's not a security show, but there's a lot of security in there. What are some of the key things that have come out of the oven that people should know about that are being delivered here? >> So the core of what we're trying to communicate with Data-to-Everything is that you need a very multifaceted data platform to be able to handle the huge variety of data that we're all dealing with, and Splunk has been known and been very successful at being able to index data, messy, non-structured data, and make sense of it even though it's not structured in the index, and that's been, still is incredibly valuable. But we started almost four years ago on a journey of adding in stream processing before the data gets anywhere, to our index or anywhere else, it's moving all around the world, how do you actually find that data and then begin to take advantage of it in-flight? And we announced that the beta of Data Stream Processor last year, but it went production this year, four years of development, a ton of patents, a 40 plus person, 50 plus person, development team behind that, a lot of hard engineering, and really elegant interface to get that there. And then on the other end, to complement the index, data is landing all over the place, not just in our index, and we're very aware that different structures exist for different needs. A data warehouse has different properties than a relational database which has different properties than a NoSQL column store in-memory database, and data is going to only continue to be more dispersed. So again, four plus years ago we started on what now is Data Fabric Search which we pre-announced in beta format last year. That went production at this show, but the ability to address a distributed Splunk landscape, but more importantly we demoed the integration with HTFS and S3 landscapes as the proof point of we've built a connector framework, so that this really cannot just be a incredibly high-speed, high-cardinality search processing engine, but it really is a federated search engine as well. So now we can operate on data in the stream when it's in motion. We obviously still have all the great properties of the Splunk index, and I was really excited about Splunk 8.0 and all the features in that, and we can go get data wherever it lives across a distributed Splunk environment, but increasingly across the more and more distributed data environment. >> So this is a data platform. This is absolutely a data platform, so that's very clear. So the success of platforms, in the enterprise at least, not just small and medium-sized businesses, you can have a tool and kind of look like a platform, there's some apps out there that I would point to and say, "Hey, that looks like a tool, it's really not a platform." You guys are a platform. But the success of a platform are two things, ecosystem and apps, because if you're in a platform that's enabling value, you got to have those. Talk about how you see the ecosystem success and the app success. Is that happening in your view? >> It is happening. We have over 2,000 apps on our Splunkbase framework which is where any of our customers can go and download the application to help draw value of a Palo Alto firewall, or ensure integration with a ServiceNow trouble ticketing system, and thousands of other examples that exist. And that has grown from less than 300 apps, when I first got here six years ago, to over 2,000 today. But that is still the earliest inning, for earliest pitch and your earliest inning journey. Why are there 20,000, 200,000, two million apps out there? A piece of it is we have had to up the game on how you interface with the platform, and for us that means through a stable set of services, well-mannered, well-articulated, consistently maintained services, and that's been a huge push with the core Splunk index, but it's also a big amount of work that we've been doing on everything from the separation between Phantom runbooks and playbooks with the underlying orchestration automation, it's a key component of our Stream Processor, you know, what transformations are you doing, what enrichments are you doing? That has to live separate than the underlying technology, the Kafka transport mechanism, or Kinesis, or whatever happens in the future. So that investment to make sure we got a effective and stable set of services has been key, but then you complement that with the amazing set of partners that are out here, and making sure they're educated and enabled on how to take advantage of the platform, and then feather in things like the Splunk Ventures announcement, the Innovation Fund and Social Impact Fund, to further double down on, hey, we are here to help in every way. We're going to help with enablement, we're going to help with sell-through and marketing, and we'll help with investment. >> Yeah, I think this is smart, and I think one of the things I'll point out is that feedback we heard from customers in conversations we had here on theCUBE and the hallway is, there's a lot of great feedback on the automation, the machine learning toolkit, which is a good tell sign of the engagement level of how they're dealing with data, and this kind of speaks to data as a value... The value creation from data seems to be the theme. It's not just data for data's sake, I mean, managing data is all hard stuff, but value from the data. You mentioned the Ventures, you got a lot of tech for good stuff goin' on. You're investing in companies where they're standing up data-driven companies to solve world problems, you got other things, so you guys are adjusting. In the middle innings of the data game, platform update, business model changes. Talk about some of the consumption changes, now you got Splunk Cloud, what's goin' on on (laughs) how you charge, how are customers consuming, what moves did you guys make there and what's the result? >> Yeah, it's a great intro on data is awesome, but we all have data to get to decisions first and actions second. Without an action there is no point in gathering data, and so many companies have been working their tails off to digitize their landscapes. Why, well you want a more flexible landscape, but why the flexibility? Because there's so much data being generated that if you can get effective decisions and then actions, that landscape can adapt very, very rapidly, which goes back to machine learning and eventual AI-type opportunities. So that is absolutely, squarely where we've been focused, is translating that data into value and into actual outcomes, which is why our orchestration automation piece was so important. One of the gating factors that we felt has existed is for the Splunk index, and it's only for the Splunk index, the pricing mechanism has been data volume, and that's a little bit contrary to the promise, which is you don't know where the value is going to be within data, and whether it's a gigabyte or whether it's a petabyte, why shouldn't you be able to put whatever data you want in to experiment? And so we came out with some updates in pricing a month and change ago that we were reiterating at the show and will continue to drive on a, hopefully, very aggressive and clear marketing and communications framework, that for people that have adjusted to the data volume metric, we're trying to make that much simpler. There's now a limited set of bands, or tiers, from 100 gigs to unlimited, so that you really get visibility on, all right, I think that I want to play with five terabytes, I know what that band looks like and it's very liberal. So that if you wind up with six and a half terabytes you won't be penalized, and then there's a complimentary metric which I think is ultimately going to be the more long-lived metric for our infrastructurally-bound products, which is virtual CPU or virtual core. And when I think about our index, stream processing, federated search, the execution of automation, all those are basically a factor of how much infrastructure you're going to throw at the problem, whether it's CPU or whether it's storage or network. So I can see a day when Splunk Enterprise and the index, and everything else at that lower level, or at that infrastructure layer, are all just a series of virtual CPUs or virtual cores. But I think both, we're offering choice, we really are customer-centric, and whether you want a more liberal data volume or whether you want to switch to an infrastructure, we're there and our job is to help you understand the value translation on both of those because all that matters is turning it into action and into doing. >> It's interesting, in the news yesterday quantum supremacy was announced. Google claims it, IBM's debating it, but quantum computing just points to the trend that more compute's coming. So this is going to be a good thing for data. You mentioned the pricing thing, this brings up a topic we've been hearing all week on theCUBE is, diverse data's actually great for machine learning, great for AI. So bringing in diverse data gives you more aperture into data, and that actually helps. With the diversity comes confusion and this is where the pricing seems to hit. You're trying to create, if I get this right, pricing that matches the needs of the diverse use of data. Is that kind of how you guys are thinkin' about it? >> Meets the needs of diverse data, and also provides a lot of clarity for people on when you get to a certain threshold that we stop charging you altogether, right? Once you get above 10s of terabytes to 100 terabytes, just put as much data in as you want. The foundation of Splunk, going back to the first data, is we're the only technology that still exists on the index side that takes raw, non-formatted data, doesn't force you to cleanse or scrub it in any way, and then takes all that raw data and actually provides value through the way that we interact with the data with our query language. And that design architecture, I've said it for five, six years now, is completely unique in the industry. Everybody else thinks that you've got to get to the data you want to operate on, and then put it somewhere, and the way that life works is much more organic and emergent. You've got chaos happening, and then how do you find patterns and value out of that chaos? Well, that chaos winds up being pretty voluminous. So how do we help more organizations? Some of the leading organizations are at five to 10 petabytes of data per day going through the index. How do we help everybody get there? 'Cause you don't know the nugget across that petabyte or 10 petabyte set is going to be the key to solving a critical issue, so let's make it easy for you to put that data in to find those nuggets, but then once you know what the pattern is, now you're in a different world, now you're in the structured data world of metrics, or KPIs, or events, or multidimensional data that is much more curated, and by nature that's going to be more fine-grained. There's not as much volume there as there is in the raw data. >> Doug, I notice also at the event here there's a focus on verticals. Can you comment on the strategy there, is that by design? Is there a vertical focus? >> It's definitely by design. >> Share some insight into that. >> So we launched with an IT operations focus, we wound up progressing over the years to a security operations focus, and then our doubling down with Omnition, SignalFx, VictorOps, and now Streamlio is a new acquisition on the DevOps and next gen app dev buying centers. As a company and how we go to market and what we are doing with our own solutions, we stay incredibly focused on those three very technical buying centers, but we've also seen that data is data. So the data you're bringing in to solve a security problem can be used to solve a manufacturing problem, or a logistics and supply chain problem, or a customer sentiment analysis problem, and so how do you make use of that data across those different buying centers? We've set up a verticals group to seed, continue to seed, the opportunity within those different verticals. >> And that's compatible with the horizontally scalable Splunk platform. That's kind of why that exists, right? >> That the overall platform that was in every keynote, starting with mine, is completely agnostic and horizontal. The solutions on top, the security operations, ITOps, and DevOps, are very specific to those users but they're using the horizontal platform, and then you wind up walking into the Accenture booth and seeing how they've taken similar data that the SecOps teams gathered to actually provide insight on effective rail transport for DB cargo, or effective cell tower triangulation and capacity for a major Australian cell company, or effective manufacturing and logistics supply chain optimization for a manufacturer and all their different retail distribution centers. >> Awesome, you know, I know you've talked with Jeff Frick in the past, and Stu Miniman and Dave Vellante about user experience, I know that's something that's near and dear to your heart. You guys, it has been rumored, there's going to be some user experience work done on the onboarding for your Splunk Cloud and making it easier to get in to this new Splunk platform. What can we expect on the user experience side? (laughs) >> So, for any of you out there that want to try, we've got Splunk Investigate, that's one of the first applications on top of the fully decomposed, services layered, stateless Splunk Cloud. Mission Control actually is a complementary other, those are the first two apps on top of that new framework. And the UI and experience that is in Splunk Investigate I think is a good example of both the ease of coming to and using the product. There's a very liberal amount of data you get for free just to experiment with Splunk Investigate, but then the onboarding experience of data is I think very elegant. The UI is, I love the UI, it's a Jupyter-style workbook-type interface, but if you think about what do investigators need, investigators need both some bread crumbs on where to start and how to end, but then they also need the ability to bring in anybody that's necessary so that you can actually swarm and attack a problem very efficiently. And so when you go back and look at, why did we buy VictorOps? Well, it wasn't because we think that the IT alerting space is a massive space we're going to own, it's because collaboration is incredibly important to swarm incidents of any type, whether they're security incidents or manufacturing incidents. So the facilities at VictorOps gave, on allowing distributed teams and virtual teams to very quickly get to resolution. You're going to find those baked into all products like Mission Control 'cause it's one of the key facilities of, that Tim talked about in his keynote, of indulgent design, mobility, high collaboration, 'cause luckily people still matter, and while ML is helping all of us be more productive it isn't taking away the need for us, but how do you get us to cooperate effectively? And so our cloud-based apps, I encourage any of you out there, go try Splunk Investigate, it's a beautiful product and I think you'll be blown away by it. >> Great success on the product side, and then great success on the customer side, you got great, loyal customers. But I got to ask you about the next level Splunk. As you look at this event, what jumps out at me is the cohesiveness of the story around the platform and the apps, ecosystem's great, but the new branding, Data-to-Everything. It's not product-specific 'cause you have product leadership. This is a whole next level Splunk. What is the next level Splunk vision? >> And I love the pink and orange, in bold colors. So when I've thought about what are the issues that are some of the blockers to Splunk eventually fulfilling the destiny that we could have, the number one is awareness. Who the heck is Splunk? People have very high variance of their understanding of Splunk. Log aggregation, security tool, IT tool, and what we've seen over and over is it is much more this data platform, and certainly with the announcements, it's becoming more of this data fabric or platform that can be used for anything. So how do we bring awareness to Splunk? Well, let's help create a category, and it's not up to us to create the category, it's up to all of you to create the category, but Data-to-Everything in our minds represents the power of data, and while we will continue internally to focus on those technical buying centers, everything is solvable with data. So we're trying to really reinforce the importance of data and the capabilities that something like Splunk brings. Cloud becomes a really important message to that because that makes it, execution to that, 'cause it makes it so much easier for people to immediately try something and get value, but on-prem will always be important as well 'cause data has gravity, data has risk, data has cost to move. And there are so many use cases where you would just never push data to the cloud, and it's not because we don't love cloud. If you have a factory that's producing 100 terabytes an hour in a area where you've got poor bandwidth, there's no option for a cloud connect there of high scale, so you better be able to process, make sense of, and act on that data locally. >> And you guys are great in the cloud too, on-premise, but final word, I want to get your thoughts to end this segment, I know you got to run, thanks for your time, and congratulations on all your success. Data for good. There's a lot of tech for bad kind of narratives goin' on, but there's a real resurgence of tech for good. A lot of people, entrepreneurs, for-profit, for-nonprofit, are doing ventures for good. Data is a real theme. Data for good is something that you have, that's part of the Data-to-Everything. Talk about the data for good real quick. >> Yeah, we were really excited about what we've done with Splunk4Good as our nonprofit focused entity. The Splunk Pledge which is a classic 1-1-1 approach to make sure that we're able to help organizations that need the help do something meaningful within their world, and then the Splunk Social Impact Fund which is trying to put our money where our mouth is to ensure that if funding and scarcity of funds is an issue of getting to effective outcomes, that we can be there to support. At this show we've featured three awesome charities, Conservation International, NetHope, and the Global Emancipation Network, that are all trying to tackle really thorny problems with different, in different ways, different problems in different ways, but data winds up being at the heart of one of the ways to unlock what they're trying to get done. We're really excited and proud that we're able to actually make meaningful donations to all three of those, but it is a constant theme within Splunk, and I think something that all of us, from the tech community and non-tech community are going to have to help evangelize, is with every invention and with every thing that occurs in the world there is the power to take it and make a less noble execution of it, you know, there's always potential harmful activities, and then there's the power to actually drive good, and data is one of those. >> Awesome. >> Data can be used as a weapon, it can be used negatively, but it also needs to be liberated so that it can be used positively. While we're all kind of concerned about our own privacy and really, really personal data, we're not going to get to the type of healthcare and genetic, massive shifts in changes and benefits without having a way to begin to share some of this data. So putting controls around data is going to be important, putting people in the middle of the process to decide what happens to their data, and some consequences around misuse of data is going to be important. But continuing to keep a mindset of all good happens as we become more liberal, globalization is good, free flow of good-- >> The value is in the data. >> Free flow of people, free flow of data ultimately is very good. >> Doug, thank you so much for spending the time to come on theCUBE, and again congratulations on great culture. Also is worth noting, just to give you a plug here, because it's, I think, very valuable, one of the best places to work for women in tech. You guys recently got some recognition on that. That is a huge accomplishment, congratulations. >> Thank you, thank you, we had a great diversity track here which is really important as well. But we love partnering with you guys, thank you for spending an entire week with us and for helping to continue to evangelize and help people understand what the power of technology and data can do for them. >> Hey, video is data, and we're bringin' that data to you here on theCUBE, and of course, CUBE cloud coming soon. I'm John Furrier here live at Splunk .conf with Doug Merritt the CEO. We'll be back with more coverage after this short break. (futuristic music)

>> Narrator: From Burlingame, California, it's theCUBE. Covering Sumo Logic Illuminate 2019. Brought to you by Sumo Logic. >> Hey, welcome back, everybody, Jeff Frick here with theCUBE. We're at Sumo Logic Illuminate 2019. It's at the Hyatt Regency San Francisco Airport. We're excited to be back. It's our second year, so third year of the show, and really, one of the key tenants of this whole event is the report. It's the fourth year of the report. It's The Continuous Intelligence Report, and here to tell us all about it is the VP of Product Marketing, Kalyan Ramanathan. He's, like I said, VP, Product Management of Sumo Logic. Great to see you again. >> All right, thank you, Jeff. >> What a beautiful report. >> Absolutely, I love the cover and I love the data in the report even more. >> Yeah, but you cheat, you cheat. >> How come? >> 'Cause it's not a survey. You guys actually take real data. >> Ah, that's exactly right, exactly right. >> No, I love them, let's jump into it. No, it's a pretty interesting fact, though, and it came out in the keynote that this is not a survey. Tell us how you get the data. >> Yeah, I mean, so as you already know, Sumo Logic is a continuous intelligence platform. And what we do is to help our customers manage the operations and security of the mission critical application. And the way we do that is by collecting machine data from our customers, and many of our customers, we have two thousand, our customers, they're all running modern applications in the cloud, and when we collect this machine data, we can grade insights into how are these customers building their applications, how are these customers running and securing their application, and that insight is what is reflected in this report. And so, you're exactly right, this is not a survey. This is data from our customers that we bring into our system and then what we do is really treat things once we get this data into our system. First and foremost, we completely anonymize this data. So, we don't-- >> I was going to say Let's make sure we have to get that out. >> Yes, absolutely, so we don't have any customer references in this data. Two, we genericize this data. So, we're not looking for anomalies. We are looking for broad patterns, broad trends that we can apply across all of our customers and all of these enterprises that are running modern mission critical applications in the cloud. And then three, we analyze ten weeks to Sunday. We look at these datas, we look at what stands out in terms of good sample sizes, and that's what we reflect in this report. >> Okay, and just to close a loop on that, are there some applications that you don't include? 'Cause they're just legacy applications that're running on the cloud that doesn't give you good information, or you're basically taking them all in? >> Yeah, it's a good point, I mean we collect all data and we collect all applications, so we don't opt-in applications or out applications for that matter because we don't care about it. But what we do look for is significant sample size because we want to make sure that we're not talking about onesie-twosie applications here or there. We're looking for applications that have significant eruption in the cloud and that's what gets reflected in this report. >> Okay, well, let's jump into it. We don't have time to go through the whole thing here now, but people can get it online. They can download their own version and go through it at their leisure. Biggest change from last year as the fourth year of the report. >> Yeah, I mean, look, there are three big insights that we see in this report. The first one is, while we continue to see AWS rule in the cloud and that's not surprising at all, we're starting to see pretty dramatic adoption of multi-cloud technologies. So, two years ago, we saw a smidgen of multi-cloud in this report. Now, we have seen almost a 50% growth year over year in terms of multi-cloud adoption amongst enterprises who are in the cloud, and that's a substantial jump albeit from a smaller baseline. >> Do you have visibility if those are new applications or are those existing ones that are migrating to different platforms? Are they splitting? Do you have any kind of visibility into that? >> Yeah, I mean, it's an interesting point, and part of this is very related to the growth of Kubernetes that we also see in this report. What ypu've seen is that, in AWS itself, Kubernetes adoption has gone up significantly, what's even more interesting is that, as you think about multi-cloud adoption, we see a lot of Kubernetes, Kubernetes as the platform that is driving this multi-cloud adoption. There is a very interesting chart in this report on page nine. Obviously, I think you guys can see this if they want to download the report. If you're looking at AWS only, we see one in five customers are adopting Kubernetes. If you're looking at AWS and GCP, Google Cloud Platform, we see almost 60% of our customers are adopting Kubernetes. Now, when you put in AWS-- >> One in five at AWS, 60% we got Google, so that means four out of five at GCP are using Kubernetes and bring that average up. >> And then, if you look at AWS, Azure, and GCP, now you're talking about the creme de la creme customers who want to adopt all three clouds, it's almost 80% adoption of Kubernetes, so what it tells you is that Kubernetes has almost become this new Linux in the cloud world. If I want to deploy my application across multiple clouds, guess what, Kubernetes is that platform that enables me to deploy my application and then port it and re-target it to any other cloud or, for that matter, even an on-prem environment. >> Now, I mean, you don't see motivation behind action, but I'm just curious how much of it is now that I have Kubernetes. I can do multi-cloud or I've been wanting to do multi-cloud, and now that I have Kubernetes, I have an avenue. >> Yeah, it started another question. What's the chicken and what's the egg right here? My general sense, and we've debated this endlessly in our company, our general sense has been that the initiative to go multi-cloud typically comes top down in an organization. It's usually the CIO or the CSO who says, you know what, we need to go multi-cloud. And there are various reasons to go multi-cloud, some of which you heard in our keynote today. It could be for more reliability, it could be for more choice that you may want, it could be because you don't want to get logged into any one cloud render, so that decision usually comes top down. But then, now, the engineering teams, the ops teams have to support that decision, and what these engineering teams and these ops teams have realized is that, if they deploy Kubernetes, they have a very good option available now to port their applications very easily across these various cloud platforms. So, Kubernetes, in some sense, is supporting the top down decision to go multi-cloud which is something that is shown in spades as a result of this report. >> So, another thing that jumped out at me, or is there another top trend you want to make sure we cover before we get in some of those specifics? >> I mean we can talk to-- >> Yeah, one of them, one of them that jumped out at me was Docker. The Docker adoption. So, Docker was the hottest thing since sliced bread about four years ago, and is the shade of Kubernetes, not that they're replacements for one another specifically, but it definitely put a little bit of appall in the buzz that was the Docker, yet here, the Docker utilization, Docker use is growing year over year. 30%! >> I'll be the first one to tell you that Docker adoption has not stalled at all. This is shown in the report. It's shown in customers that we talk to. I mean, everyone is down the path of containerizing their application. The value of Docker is indisputable. That I get better agility, that I get better portability with Docker cannot be questioned. Now, what is indeed happening is that everyone who is deploying Docker today is choosing a orchestration technology and that orchestration technology happens to be Kubernetes. Again, Kubernetes is the king of the hill. If I'm deploying Docker, I'm deploying Kubernetes along with it. >> Okay, another one that jumped out at me, which shouldn't be a big surprise, but I'm a huge fan of Andy Jassy, we do all the AWS shows, and one of always the shining moments is he throws up the slide, he's got the Customer slide. >> There you go. >> It's the Services slide which is, in like, 2.6 font across a 100-foot screen that fills Las Vegas, and yet, your guys' findings is that it's really: the top ten applications are the vast majority of the AWS offerings that are being consumed. >> Yep, not just that. It's that the top services in AWS are the infrastructure-as-a-service services. These are the core services that you need if you have to build an application in AWS. You need ECDO, I need Esri, I need identity access management. Otherwise, I can't even log into AWS. So, this again goes back to that first point that I was making was that multi-cloud adoption is top of mind for many, many customers right now. It's something that many enterprises think of, and so, if I want to indeed be able to port my application from AWS to any other environment, guess what I should be doing? I shouldn't be adopting every AWS service out there because if I frankly adopted all these AWS services, the tentacles of the cloud render are just so that I will not be able to port away from my cloud render to any other cloud service out there. So, to a certain extent, many of the data points that we have in this report support the story that enterprises are becoming more conscious of the cloud platform choices that they are making. They want to at least keep an option of adopting the second or the third cloud out there, and they're consciously, therefore choosing the services that they are building their applications with. >> So, another hot topic, right? Computer 101 is databases. We're just up the road from Oracle. Oracle OpenWorld's next week. A lot of verbal jabs between Oracle and some of the cloud providers on the databases, et cetera. So, what do the database findings come back as? >> I mean, look at the top four databases: Redis, MySQL, Postgres, Mongo. You know what's common across them? They're all open-source. They're all open-source database, so if you're building your application, find standard components that you can then build your application on, whether it's a community that you can then take and move to any other cloud that you want to. That's takeaway number one. Takeaway number two, look at where Oracle is in this report. I think they're the eighth database in the cloud. I actually talked to a few customers of ours today. >> Now, are you sampling from Oracle's cloud? Is that a dataset? >> No, this is-- >> Yes, right, okay. So, I thought I want to make sure. >> And, if AWS is almost the universe of cloud today, we can debate at some bids, but it is close enough, I'd say, it tells you where Oracle is in this cloud universe, so our friends at Redwood City may talk about cloud day in and day out, but it's very clear that they're not making much of intent in the cloud at this point. >> And then, is this the first year the rollup of the type of database that NoSQL exceeded relational database? >> No, I mean, we've been doing this for the last two years, and it's very clear that NoSQL is ahead of SQL in the cloud, and I think the way we think about it is primarily because, when you are re-architecting your applications in the cloud, the cloud gives you a timeline, it gives you an opportunity to reconsider how you build out your data layer, and many of our customers are saying NoSQL is the way to go. The scalability demands, the reliability demands, so if my application was such that I now have the opportunity to rethink and redo my data layer, and frankly, NoSQL is winning the game. >> Right, it's winning big time. Another big one: serverless, Lambda. Actually, I'm kind of surprised it took so long to get to Lambda 'cause we've been going to smaller atomic units of compute, store, and networking for so, so long, but it sounds like, looks like we're starting to hit some critical mass here. >> Yeah, I mean, look, Lambda's ready for primetime. I mean we have seen that tipping point out here. Almost one in three customers of ours are using Lambda in production environments. And then, if you cast a wider net, go beyond production and even look at dev tests, what we see is that almost 60% of Sumo Logic's customers, and if you look at 2,000 customers, that's a pretty big sample size. Almost 60% of enterprises are using Lambda in some way, shape, or form. So, I think it's not surprising that Lambda is getting used quite well in the enterprise. The question really is: what are these people doing with Lambda? What's the intent behind the use of Lambda? And that's where I think we have to do some more research. My general sense, and I think it's shared widely within Sumo Logic, is that Lambda's still at the edges of the application. It's not at the core of the application. People are not building your mission critical application on Lambda yet because I think that that paradigm of thinking about event-driven application is still a little foreign to many organizations, so I think it'll take a few more years for an entire application to be built on Lambda. >> But you would think, if it's variable demand applications, whether that's a marketing promotion around the Super Bowl or running the books at the end of the month, I guess it's easy enough to just fire up the servers versus doing a pure Lambda at this point in time, but it seems like a natural fit. >> If you're doing the utility type application and you want to start it and you want to kill it and not use it after an event has come and gone, absolutely, Lambda's the way to go. The economics of Lambda. Lambda absolutely makes sense. Having said that, I mean, if you're to build a true mission critical application that you're going to be keeping on for a while to come, I'm not seeing a lot of that in Lambda yet, but it's definitely getting there. I mean we have lots of customers who are building some serious stuff on Lambda. >> Well, a lot of great information. It's nice to have the longitudinal aspect as you do this year over year, and again, we're glad you're cheating 'cause you're getting good data. >> (chuckles) >> (laughs) You're not asking people questions. >> Yeah, I mean, I'd like to finish out by saying this is a report that Sumo Logic builds every year, not because we want to sell Sumo Logic. It's because we want to give back to our community. We want our community to build great apps. We want them to understand how their peers are building some amazing mission critical apps in the cloud and so, please download this report, learn from how your peers are doing things, and that's our only intent and goal from this report. >> Great, well, thanks for sharing the information and a great catch-up, nice event. >> All right, thank you very much, Jeff. >> All right, he's Kalyan, I'm Jeff. You're watching theCUBE. We're at Sumo Logic Illuminate 2019. Thanks for watching, we'll see you next time. (upbeat electronic music)

>> Announcer: Live from San Francisco. Celebrating 10 years of high tech coverage, it's theCUBE. Covering VMWorld 2019, brought to you by VMware and it's ecosystem partners. >> Stu: Welcome back. I'm Stu Miniman, my co-host Justin Warren. And you are watching theCUBE. We have two sets, three days, here at VMWorld 2019. Our 10th year of the show. And happy to welcome back to our program, two of our theCUBE Alumni. We were at VeeamON earlier this year down in Miami, but sitting to my right is Ratmir Timashev, who is the co-founder and executive vice president of global sales and marketing with Veeam, and joining us also is Danny Allan, who's the vice president of product strategy also at Veeam. Thank you so much both for joining us. >> Thanks for having us Stu. >> Thank you. >> All right so, Ratmir, let's start. Veeam has been very transparent as to how the company is doing. You know, there's all this talks about unicorns and crazy evaluations or anything like that? But give us the update on, you know, actual dollars and actually what's happening in your business. >> Ratmir: Absolutely, we're always transparent. So actually, there's this term, unicorn, right? So does it mean one billion in valuation, or one billion in revenue (chuckles)? >> Stu: It is valuation. >> Yeah, I know that. So, Veeam is not unicorn anymore, right? Veeam is one billion in bookings. So, yeah, the major trend in the industry, is that we're moving from perpetual to subscription, because we're moving on-prem to hybrid cloud. And Veeam is actually leading that wave. So where we've been always known to be very customer friendly to do business with, easy to do business with, from the channel, from the customer perspective, and that's the major trend. If the customers are moving to hybrid cloud, we have to move to there, from our business model to a hybrid cloud. So we're changing our business model, to make it very easy for customers. >> Ratmir, that's not an easy adjustment. We've watched some public companies go through a little bit of challenges as you work through, you know there's the financial pieces, there's the sales pieces of that, since... Give us a little bit of the, how that works? You know, you just retrain the sales force and go or-- >> That is awesome, awesome question. That that is awesome point, that it's extremely painful. Extremely painful, and for some company, like everybody says Adobe is the best example of moving from perpetual or traditional business model to a subscription, right. So annual, even monthly subscription. For us it's even ten times more difficult than Adobe, because, we're not only moving from perpetual to subscription. We're moving, we're changing our licensing unit, per socket which is VMware traditional to pure VM or pure workload or pure instance, right. What we call instance, basically means, so it's extremely painful, we have to change how we do business, how we incentivize our sales people, how we incentivize our channel, how we incentivize our customers. But that's inevitable, we're moving to a hybrid cloud where sockets don't exist. Sockets, there are no sockets in the hybrid cloud. There are workloads and data. Data and applications. So we have to change our business model, but we also have to keep our current business model. And it's very difficult in terms of the bookings and revenue, when we give a customer an option to buy this way or that way. Of course they will choose the way that is the less expensive for them, and we're ready to do that. We can absorb that, because we're a private company, and we're approachable and we're fast growing. So we can afford that, unlike some of the public companies or companies that, venture capital finance. >> So how do you make that kind of substantial change to the... I mean changing half your company, really. To change that many structures. How do do you do that without losing the soul of the company? And like Veeam, Veeam is famous for being extremely Veeamy. How do you make all those sorts of changes and still not lose the soul of the company like that? How do you keep that there? >> That's an awesome question, because that's 50% of executive management discussions, are about that questions, right. What made Veeam successful? Core value, what we call, core values, there are family values, there are company core values every company has. So that's the most important. And one of them is, be extremely customer friendly, right. So easy to do business with. That's the number one priority. Revenue, projects, number two, number three, being doing the right things for the customer is number one. That's how we're discussing, and we're introducing a major change on October 1st. >> Ah yes. >> Another major change. We've done this major changes in the last two years, moving to subscription. So we started that move, two, two-and-a-half years ago, by introducing our product for Office 365, backup, when that was available only for, on subscription basis, not perpetual. So we're moving in subscription, to the subscription business model in the the last three years. On October 1st, 2019, in one month, we introducing another major change. We are extremely simplifying our subscription licensing and introducing, what we will call Veeam Universal License. Where you can buy once and move or close everywhere. From physical to VMware to Hyper-V to a double SS, ash or back to VMware and back to physical. I'm joking. (lauging) >> All right, Danny, bring us inside the product. We've watched the maturity, ten years of theCUBE here, Veeam was one of the early big ecosystems success stories, of course it went into Multi-Hypervisor, went into Multicloud. You know Ratmir, just went through all of the changes there. Exciting the VUL I guess we'll call it. >> Ratmir: VUL >> VUL, absolutely. So on the product piece, how's the product keeping in line with all these things. >> So our vision is to be the most trusted provider, backup solutions that enable high data management. So backup is still a core of it and it's the start of everything that we do. But if you look what we've done over the course of this year, it's very much about the cloud. So we added the ability, for example, to tier things into object storage in the hyperscale public cloud and that has been taking off, gang busters into S3 and into Azure Blob storage. And so that's a big part of it. Second part of it, in cloud data management is the ability to recover, if you're sending your data into the cloud, why not recover there? So we've added the ability to recover workloads in Azure, recover workloads in EC2. And lastly of course, once your workloads are in the cloud, then you want to protect it, using cloud-native technology. So we've addressed all of these solutions, and we've been announcing all these exciting things over the course of 2019. >> The product started off as being VM-centrical, VM Only back in the day. And then you've gradually added different capabilities to it as customers demanded, and it was on a pretty regular cadence as well. And you've recently added, added cloud functionality and backups there. What's the next thing, customers are asking for? 'Cause we've got lots of workloads being deployed in edge, we've got lots of people doing things with NoSQL backups, we've got Kubernetes, is mentioned every second breath at this show. So where are you seeing demand for customers that you need to take the product next? So we've heard a lot about Kubernetes obviously, the shows, the containers it's obviously a focus point. But one of the things we demoed yesterday. We actually had a breakout session, is leveraging an API from VMR called the VCR API for IO filtering. So it basically enables you to fork the rights when you're writing down to the storage level, so that you have continuous replication in two environments. And that just highlights the relationship we have with VMware. 80% of our customers are running on VMware. But that's the exciting things that we're innovating on. Things like making availability better. Making the agility and movement between clouds better. Making sure that people can take copies of their data to accelerate their business. These all areas that we are focusing on. >> Yeah, a lot of companies have tried to, multiple times have tried to go away from backup and go into data management. I like that you don't shy away from, ah, yeah we do backup and it's an important workload, and you're not afraid to mention that. Where's some other companies seem to be quite scared of saying, we do backup, 'cause it's not very cool or sexy. Although well, it doesn't have to be cool and sexy to be important. So I like that you actually say that yes we do backup. But we are also able to do some of these other bits and pieces. And it's enabled by that backup. So you know, copy, data management, so we can take copies of things and do this. Where is some of the demand coming around what to do with that data management side of things. I know there's, people are interested in things like, for example, data masking, where you want to take a copy of some data and use it for testing. There's a whole bunch of issue and risks around in doing that. So companies look for assistance from companies like Veeam to do that sort of thing. Is that where you're heading with some of that product? >> It is, there's four big use cases, DevOps is certainly one of them, and we've been talking about Kubernetes, right, which is all about developers and DevOps type development, so that's a big one. And one of the interesting things about that use case is, when you make copies of data, compliance comes into play. If you need to give a copy of the data to the developer, you don't want to give them credit card numbers or health information, so you probably want to mask that out. We have the capability today in Veeam, we call it, Staged Restore, that you could actually open the data in the sandbox to manipulate it, before you give it to the developer. But that's certainly one big use case, and it's highlighted at conferences like this. Another one is security, I spent a decade in security. I get passionate about it, but pentesting or forensics. If you do an invasive test on a production system, you'll bring the system down. And so another use case of the data is, take a copy, give it to the security team to do that test without impacting the production workload. A third one would be, IT operations, patching and updating all the systems. One of the interesting things about Veeam customers. They're far more likely to be on the most recent versions of software, because you can test it easily, by taking a copy. Test the patch, test the update and then roll it forward. And then a forth huge use case that we can not ignore is the GDPR in analytics and compliance. There's just this huge demand right now. And I think there's going to be market places opened in the public cloud, around delegating access to the data, so that they can analyze it and give you more intelligence about it. So GDPR is just a start, right. Were is my personally identifiable information? But I can imagine workload where a market place or an offering, where someone comes in and says, hey, I'll pay you some money and I'll classify your data for you, or I'll archive it smartly for you. And the business doesn't have to that. All they have to do is delegate access to the data, so that they can run some kind of machine learning algorithm on that data. So these are all interesting use cases. I go back, DevOps, security IT operations and analytics, all of those. >> So Ratmir, when I go to the keynote, it did feel like it was Kubernetes world? When I went down the show floor it definitely felt like data protection world. So it's definitely been one of the buzzier conversations the last couple of years at this show. But you look, walk through the floor, whether it be some of the big traditional vendors, lots of brand new start ups, some of the cloud-native players in this space. How do you make sure that Veeam gets the customers, keeps the customers that they have and can keep growing on the momentum that you've been building on? >> That's a great question, Stu. Like Pat Gelsinger mention that, number of applications has grown in the last five years, from 50 million to something like 330 million, and will grow to another almost 800 million in the next five years, by 2024. Veeam is in the right business, Veeam is the leader, Veeam is driving the vision and the strategy, right. Yeah, we have good competition in the form of legacy vendors and emerging vendors, but we have very good position because we own the major part of your hybrid cloud, which is the private cloud. And we're providing a good vision for how the hybrid cloud data management, not just data protection, which just Danny explained, should be done, right. I think we're in a good position and I feel very comfortable for the next five, ten years for Veeam. >> It's a good place to be. I mean feeling confident about the future is... I don't know five to ten years, that's a long way out. I don't know. >> Yeah I agree, I agree, it used to be like that, now you cannot predict more than six moths ahead, right. >> Justin I'm not going to ask him about Simon now, it's-- >> Six months is good yeah, six months maximum, what we can predict-- >> We were asking Michael Dell about the impact of China these days, so there's a lot of uncertainty in the world these day. >> Ratmir: Totally. >> Anything macro economic, you know that, you look at your global footprint. >> No we're traditional global technology company that generates most of the revenue between Europe and North America and we have emerging markets like Asia-Pac and Latin. We're no different than any other global technology company, in terms of the revenue and our investment. The fastest growing region of course is Asia-Pac, but our traditional markets is North America and Europe. >> Hailing from Asia-Pac, I do know the region reasonably well and Veeam is, yeah Veeam is definitely, has a very strong presence there and growing. Australia used to be there, one of our claims to fame, was one of the highest virtualized workload-- >> And Mohai is the cloud adapter. >> Cloud adoption. >> Yes, we like new shiny toys, so adopt it very, very quickly. Do you see any innovation coming out of Asia-Pac, because we use these things so much, and we tend to be on that leading edge. Do you see things coming out of the Asia-Pac teams that notice how customers are using these systems and is that placing demand on Veeam. >> Absolutely, but Danny knows better because he just came back from the Asia-Pacific trip. >> Justin: That's right, you did. >> Yeah, I did, I always say you live in the future, because you're so many hours ahead. But the reality is actually, the adoption of things like Hyper-convergence infrastructure, was far faster in areas like NZ, the adoption of the cloud. And it's because of New Zealand is part of the DAid, Australia is very much associated with taking that. One of the things that we're seeing there is consumption based model. I was just there a few weeks ago and the move to a consumption and subscription based model is far further advanced in other parts of the world. So I go there regularly, mostly because it gives me a good perspective on what the US is going to do two years later, And maybe AMEA three years later. It gives us a good perspective of where the industry is going-- >> It's not to the US it comes to California first then it spreads from there. (lauging) >> Are you saying he's literally using the technology of tomorrow in his today, is what we're saying. >> Maybe me I can make predictions a little bit further ahead there. >> Well you live in the future. >> All right I want to give you the both, just a final word here, VMWorld 2019. >> It's always the best show for us. VMWorld is the, I mean like Danny said, 80% of our customers is VMware, so it's always the best. We've been here for the last 12 years, since 2007. I have so many friends, buddies, love to come here, like to spend three, four days with my best friends, in the industry and just in life. >> I love the perspective here of the Multicloud worlds, so we saw some really interesting things, the moving things across clouds and leveraging Kubernetes and containers. And I think the focus on where the industry is going is very much aligned with Veeam. We also believe that, while it starts with backup up, the exciting thing is what's coming in two, three years. And so we have a close alignment, close relationship. It's been a great conference. >> Danny, Ratmir, thank you so much for the updates as always and yeah, have some fun with some of your friends, in the remaining time that we have. >> We have a party tonight Stu, so Justin too. >> Yeah, I think most people that have been to VMWorld are familiar with the Veeam party, it is famous, definitely. >> For Justin Warren, I'm Stu Miniman, we'll be back with more coverage here, from VMWorld 2019. Thanks for watching theCUBE. (electronic music)