(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)

>> From Orlando, Florida It's theCUBE. Covering SAP SAPPHIRE NOW 2018. Brought to you by NetApp. >> Welcome to theCUBE. I am Lisa Martin with Keith Townsend. And we are in Orlando at SAP SAPPHIRE NOW 2018. We're at the NetApp booth and we are now talking with Gerald Pfeiffer V SUSE, VP of Products and Technology Programs. Gerald, welcome to theCUBE. >> Thanks for having me. >> So thank you for bringing the SUSE Chameleon here So let's talk about open-source. What is an open-source company? What are the key hallmarks that define an open-source company? >> So when you think of open-source technically it's about the license. It's about the open-source license that the software is under. But if you want to be a real open source company there is actually, it goes beyond that. And that's where many we see many of the classic companies fail as in you take a piece of software that you've written in house you open-source it which means you put an open-source license on it. And then you throw it over the fence. You put it on an FTP server or a NetApp Server or github or somewhere and say this is an open-source project. Technically true but what open-source really is about also is how you develop the software. It's a development model. It's about the community or communities you have. And so as an open-opensource company or a true open-source company what that means is you need to change how you develop the software. And how you go about it and it involves, you need to, You need to, let go. You need to, you need to lose. Lose in a way you lose control and you need to help, if it's something that you initiated you need to make this attractive for others and easy to contribute. And so the development model the transparency collaboration, communications all that is really important for a successful open-source project. But I would argue also for a successful open-source company. >> So let's talk about the community for a little bit when it comes to open-source and especially with SUSE. SUSE's one the most successful open source companies in the world. However, your key product, SUSE Enterprise Linux you guys don't control the kernel you have to work with a community of organizations and personalities and conflicting agendas. How does SUSE organize their self that over a 25 year period you guys have consistently grown become more prominent in the industry. How have you had that when you don't even own you don't, rather, control the key technology, the kernel to your product. >> Ya, so, there is actually a trick behind it and the short answer is you cannot control but you can influence. And so how do you influence? And it's really about becoming part of the community or I usually actually when we get new employees that come from a proprietary background one of the first things I teach them is there is no such thing as the open-source community. It's actually open-source communities. There is actually many of them and even your example, the kernel there is the Linux kernel community, but inside, everyone, the group of everyone who contributes there're actually subgroups. People focusing on different aspects. And so if you want to influence that the easiest way and the hard way is you start contributing. And so you start building up rapport, you start building up credibility and that's usually not something you do overnight It's not like you can come and say Oh, I've been doing operating systems for 30 years. I'm a distinguished engineer and now I'm telling you this is how you need to do it. You start by contributing code. You start by being part of the conversations. By critiquing, constructively hopefully, other people's contributions. usually in a certain area. And then people start getting to know your name. And they start trusting you. And I've, I'm not a kernel engineer but there're a couple of open-source projects I've contributed since writing my PHD thesis And I'm still doing that usually on my weekends or evenings when I have a little time. And so there're people I've been working together for 15 years or more, who I've never met in person. And some I've met and then I realized Wait a minute, I know he's going to be at the conference and I don't know how old he is. He wrote about his children so that gives a certain or his young baby children so that may give an, an idea. But I don't know how old he is. I don't know what color of hair. What color of skin. But then you meet and because you have this relationship you actually, you know, you get together. And there is trust and once you have this trust on a personal level but also, at least as importantly or I would break both the same on a technical side. I trust your, your judgment. Then you start influencing. >> Is that what makes SUSE an open open-source company? >> Ya that's definitely one of the aspects where, when we want to we want to drive something. And I'll give you an example that's actually especially in SAP context this is really relevant is something we call live kernel patching. So you know you have this HANA system so it's lots of memories and you have all those security issues that keep popping up, now and then. And so one of the challenges is you want to apply the security update if you're an IT person but, when you do so, you need to, and it's a kernel thing, then you need to restart the server. Because other sub-systems like a web server you just restart the web server and you're down for one millisecond and nobody really notices unless you're CNN.com or whatever. But if you restart the kernel the whole machine reboots. And then you know you scan the memory and you have a HANA machine with 12 terabyte of memory or 16. So the start up takes and then why is HANA so fast? Because all it did is in memory. Now, doing that isn't, isn't fast. >> So that's really interesting as you look through, I love the integration between SAP and SUSE the in-memory, the continuous kernel, patching, the ability to integrate the two solutions. It's interesting, you guys have a partnership you have outside of SAP with these companies that not necessarily, from a licensing perspective the application is close-sourced. So there is a myth I think, in the industry that close-sourced software versus open-sourced software one is more secure, the other one's more stable Random religious arguments. What are we seeing in the... Wow, what are... How are customers embracing the SUSE relationship along with the SAP relationship. >> You know in a way (laughs) and that's a, nhat's a tricky statement to make but in a way at first approximations customers don't care whether it's open-sourced or proprietary? As a customer, I care that it works. And if I'm a SAP customer my (mumbles) workload needs to stay up. And so what I'm looking for is performance is security, is scalability, is availability, high availability. And so whatever platform gives that to me is the platform that I choose or in the case of HANA for, actually, SAP choose. So if you look at HANA, it's an interesting sample the only operating system it's available on the only platform it's available on is Linux. So SAP actually has done their research and they looked into it and said okay, we need certain characteristics what's... Where do we get the best solution? And it turns out Linux offered that. And so I don't see, when it comes to applications in particular our workloads I don't see it as much as being open-sourced or proprietary It's really what's the best technical solution and then there obviously is the question behind the question is how do you actually get to the best solution? And that's where the open-source model where it's not just one company doing that we have lots of engineers contributing to the kernel and other parts. But it's only one part. Many of our partners contribute Our competitors contribute And so in this open-source arena Things move. Just to improve, for example, the linux kernel and you get a better outcome than any proprietary vendor would actually be been able to deliver with a classic Unix system for example. >> You talked about, you know, customers not caring about the technology. It just needs to work. And it's kinda the same thing I think of when you look at a technology like ERP software that's largely invisible. Right? So is SUSE. And SAP wants to be one of the top ten most valuable global brands. And this morning during the keynote Bill McDermott said that they're now number 17. So they're getting up there to the big brands Like Apple, Coca Cola, Google who all have products that we can kinda see and touch. So when you're in a partnership with SAP how do you articulate the value of what you guys can deliver to help the customer not care about what's under the hood here but also ensure that they're actually able to deliver what they need to to their customers. What are some of those unique maybe customer examples that you have where customers with SAP on SUSE are transforming their businesses or their industries. >> Yeah so, much... Much of this transformation really comes from the SAP stack. What we contribute is really the stability of the platform. And so, Obviously, obviously at the technical level people do care do care actually about open-source because the one thing open-source provides you is the transparency. You can see an SAP engineer actually developing HANA for example. But also other things we do together They have been looking at the source code trying to understand what's going on and then optimize HANA. So when I said customers don't care that's in a first approximation because it needs to work. If it doesn't work, everything else doesn't matter. But if, so there are people who care about the technical more details. Often these days or usually when When it's like at the CLO level or an IT director level what they care more about is things like high-availability scenarios or blueprints. So it's not just one bit of technology or even how HANA runs on SUSE but they know a server is going to fail at one point. How do you-- >> When I ran a SAP environment one of the things that we did, we did a bake-off of Linux distributions for our appliances and these are appliances! In theory you get an appliance you turn it on and you install your SAP app and life goes on, no one should care about the underlying appliance but for us it was about the OS and the availability. You know, we were coming from a non-stop XP, HP, titanium shop and we were very happy with the non-stop capability but going to X86 there's a lot of thought that goes into making that non-stop Can you talk to the relationship between NetApp, SAP and, and SUSE from a community perspective because this is related to the conversation around open-source and making that happen and to your point, how do you care why would an IT director care about SUSE verses some other distribution. >> So, you know, if you look at the conversations I'm having often it's then looking at it at the solutions level So if you can point out that you have the blueprints or reference architectures or whatever you want to call it. You have customer success stories etc. Where you can say, look this is, in a scenario like this in your, in your market or in your in your vertical this is what you can do and this is how it'll be supported. So that your guys don't have to start from zero but it's actually really easy to go high availability or in fact we have a dedicated team that sits in the... That sits in the Linux lab with all the other partners you named and many more where SAP, and that's actually a really clever thing they did, creating this Linux lab and they also have a partner board where talking about communities, they have created this level of community where different vendors come together you know and you have hallway conversations and you want to do something say okay, how do you do this with the SUSE side how do you do this on the NetApp side and then at an engineering level and at a solution level you build something that actually works technically and then obviously the support relationship is really important. So that's, that's one of the challenges open-source had in the beginning compared to proprietary because if you look at some of the old full stack companies or established ones. They used to deliver hardware and then the operating system and then middleware or database and application top. So you had one phone number to call when there's a problem. And originally with open-source you know, you got this piece here and then you got the storage from from NetApp say and .. And who do you call? And then the finger pointing starts. So what's made open-source also successful is the establishment of, of really, processes, agreements and just practical workflows so that our companies work together and the customer, they can pick up the phone in fact, if you look at, let's say SAP applications what we have set up in this SAP environment is you can call SAP and that's the only phone number you ever need to call. And everything behind that happens fully transparently. So all the vendors get together. >> So, to sum up it sounds like what you're talking about that's really key for SUSE is openness, transparency, trust, collaboration. >> Yes, and at the open-source level at Linux kernel, compiler and the individual pro checks but essentially the same. Exactly what you explained. Also at the business level, what we do with partners and what we do with customers. >> And we hear that in the keynote this morning Bill McDermott really kinda was talking about trust as the new currency. So Gerald you're right in line with that. Thank you so much for joining Keith and me today. >> Thank you for having me. >> We wanna thank you for watching theCUBE I'm Lisa Martin with Keith Townsend from SAP SAPPHIRE NOW 2018 Thanks for watching.

(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)

>>from around the globe. >>It's the Cube with >>coverage of Coop Khan and Cloud Native Con Europe 2020 Virtual brought to you by Red Hat Cloud, >>Native Computing Foundation and >>Ecosystem Partners. Hi. And welcome back. I'm stew Minuteman. And this is the cube coverage of que con cognitive con 2020. The Europe virtual addition Course kubernetes won the container wars as we went from managing a few containers that managing clusters, too many customers managing multiple clusters and that and get more complicated. So to help understand those challenges and how solutions are being put out to solve them, having a welcome back to the from one of our cube alumni do if it Gerald is the vice president and general manager of the management business unit at Red Hat. Joe, good to see you again. Thanks so much for joining us >>two. Thanks for having me back. >>All right, so at Red Hat Summit, one of the interesting conversation do you and I add, was talking about advanced cluster management or a CME course. That was some people and some technology that came over to Red hat from IBM post acquisition. So it was tech preview give us the update. What's the news? And, you know, just level set for the audience. You know what cluster management is? >>Sure, So advanced Cluster manager or a CMS, We actually falling, basically, is a way to manage multiple clusters. Ross, even different environments, right? As people have adopted communities and you know, we have at several 1000 customers running open shift on their starting to push it in some very, very big ways. And so what they run into is a stay scale. They need better ways to manage. It would make those environments, and a CMS is a huge way to help manage those environments. It was early availability back at Summit end of April, and in just a few months now it's generally available. We're super excited about that. >>Well, that that Congratulations on moving that from technical preview to general availability so fast. What can you tell us? How many customers have you had used this? What have you learned in talking to them about this solution? >>So, first of all, we're really pleasantly surprised by the amount of people that were interested in the tech preview. Integrity is not a product that's ready to use in production yet so a lot of times accounts are not interested in. They want to wait for the production version. We had over 100 customers in our tech review across. Not only geography is all over the world Asia, America, Europe, us across all different verticals. There's a tremendous amount of interest in it. I think that just shows you know, how applicable it is to these environments of people trying to manage. So tremendous had update. We got great feedback from that. And in just a few months, we incorporate that feedback into the now generally available product. So great uptick during the tech created >>Excellent Bring assigned side a little bit, you know, When would I use this solution? If I just have a single cluster, Does it make sense for May eyes? Is it only for multi clusters? You know, what's the applicability of the offering? Yes, sir, even for >>single clusters that the things that ACM really does fall into three major areas right allows closer lifecycle management. Of course, that would mean that you have more than one cluster ondas people grow. They do for a number of reasons. Also, policy based management the ability to enforced and fig policies and enforce compliance across even your single cluster to make sure that stays perfect in terms of settings and configuration and things like that. Any other application. Lifecycle management The ability to deploy applications in more advanced way, even if you're on a single cluster, gets even better for multi cluster. But you can deploy your APS to just the clusters that are tagged a certainly, but lots of capabilities, even for application, even a single cluster. So we find even people that are running a single cluster need it askew, deployed more more clusters. You're definitely >>that's great. Any you mentioned you had feedback from customers. What are the things that I guess would be the biggest pain points that this solves for them that they were struggling with in the past? Well, >>first of being able to sort of Federated Management multiple clusters, right, as opposed to having to manage each cluster individually, but the ability to do policy based configuration management to just express the way you want things to stay, have them stay that way to adopt a more of a getups ethnology in terms of how they're managing their your open ships environments. There's lots more feedback, but those were some of the ones that seem to be fairly common, repetitive across the country. >>Yeah, and you know, Joe, you've also gotten automation in the management suite. How do I think about this? How does this fit into the broader management automation that customers were using? Well, >>I think as people in employees environments. And it was a long conversation about platform right? But there's a lot of things that have to go with the platform and red hats actually in very good about that, in terms of providing all the things you necessary that you would find necessary to make the five form successful in your environment. Right? So I was seen by four. We need storage, then development environments management, the automation ability to train on it. We have our open innovation labs. There's lots of things that are beyond the platform that people acquire in order to be successful. In the case of management automation, ACM was a huge advancement. Terms had managed these environments, but we're not done. We're gonna continue to ADM or automation integration with things like answerable mawr, integration with observe ability and analytics so far from done. But we want to make sure that open ship stays the best managed environment that's out there. I also do want to make a call out to the fact that you know, this team has been working on this technology for the past couple of years. And so, you know, it's only been a red hat for five months. This technology is actually very mature, but it is quite an accomplishment for any company to take a new team in a new technology. And in five months, do what Red Hat does to it in terms of making it consumable for the enterprise. So then kudos continue. Really not >>well. And I know a piece of that is, you know, moving that along to be open source. So, you know, where are we with the solution? Now that is be a How does that fit in tow being open? Source. >>Eso supports that are open source Already. When the process of open sourcing the rest of it, as you've seen over time read, it has a perfect record here of acquiring technologies that were either completely closed Source Open core in some cases where part it was open. It was closed. But that was the case with Ansell a few years ago. But basically our strategy is everything has to be open source. That takes time in the process of going through all of the processes necessary to open source parts of ACM on. We think that will find lots of interest in the community around the different projects inside of >>Yeah. How about what? One of the bigger concerns talking to customers in general about kubernetes even Mawr in 2020 is. What about security? How does a CME help customers make sure that their environment to secure? >>Yeah, so you know, configuration policies and forcing you can actually sent with ACM that you want things to be a certain way that somebody changes them that automatically either warn you about them or enforcement would set them back. So it's got some very strong security chops in terms of keeping the configurations just the way you want. That gets harder as you get more and more clusters. Imagine trying to keep everything but the same levels, settings, software, all the parts and pieces so affected you have ACM that can do this across any and all of your clusters really took the burden off people trying to maintain secure environments, >>okay, and so generally available. Now, anything you can share about how this solution is priced, how it fits in tow. The broader open shift offerings, >>Yes. Oh, so it's an add on for open shift is priced very similarly to open shift in terms of the, you know, core pricing. One thing I do want to mention about ACM, which maybe doesn't come out just by a description product is the fact that a scene was built from scratch for communities, environments and optimize for open shift. We're seeing a lot of competition out there that's taking products that were built for other environments, trying to sort of been member coerce them into managing kubernetes environments. We don't think people are going to be successful at that. Haven't been successful to date. So one things that we find as sort of a competitive differentiator for ACM and market is the fact that it was built from scratch designed for communities environments. So it is really well designed for the environment it's trying to manage, and we think that's gonna keep your competitive edge? >>Well, always. Joe. When you have a new architecture, you advantage of things. Any examples that you have is what, what a new architecture like this can do that that an older architecture might struggle with or not believe. Be able to do even though when you look at the product sheet, the words sound similar. But when you get underneath the covers, it's just not a good architect well fit. >>Yeah, so it's very similar sort of the shift from physical to virtual. You can't have a paradigm shift in the infrastructure and not have a sort of a corresponding paradigm shift in management tool. So the way you monitor these environments, where you secure them the way they scale and expand, we do resource management, security. All those things are vastly different in this environment compared to, let's say, a virtual more physical environment. So this has improved many times in the past. You know, paradigm shift in the infrastructure or the application environment will drive a commensurate paradigm shift in management. That's what you're seeing here. So that's why we thought it was super important to have management that was built for these environments. by design. So it's not trying to do sort of unnatural things north manage the environment. >>Yeah, I wondered. I love to hear just a little bit your philosophy as to what's needed in this space. You know, I look back to previous generations, look at virtualization. You know, Microsoft did very well at managing their environment, the M where did the same for their environments. But, you know, we've had generations of times where solutions have tried to be management of everything, and that could be challenging. So, you know, what's Red Hat in a CM's position and what do we need in the community space, you know, today and for the next couple of years. >>So kubernetes itself is the automation platform you talked about, you know, early on in the second. So you know, Cooper navies itself provides, you know, a lot of automation around container management. What a CME does is build a top it out and then capture, you know, data and events and configuration items in the environment and then allows you to define policies. People want to move away from manual processes. Certainly, but they wanna be able to get to a more state full expression of the way things should be. You want to be able to use more about, you know, sort of get up, you know, kind of philosophy where they say, this is how I want things today. Check the version in, keep it at that level. If it changes, put it back. Tell me about it. But sort of the era of chasing. You know, management with people is changing. You're seeing a huge premium now on probation. So automation at all levels. And I think this is where a cm's automation on top of open shift automation on down the road, combined with things like ansell, will provide the most automated environment you can have for these container platforms. Um, so it's definitely changing your seeing observe ability, ai ops getups type of philosophies Coming in these air very different manager in the past helps you seeing innovation across the whole management landscape in the communities environment because they are so different. The physics of them are different than the previous environments. We think with ACM answerable or insights product and some over analytics that we've got the right thing for this environment >>and can give us a little bit of a look forward, you know? How often should we expect to see updates on this? Of course. You mentioned getting feedback from the community from the technical preview to G A. So give us a little bit. Look, you know, what should we be expecting to see from a CME down the right the So >>the ACM team is far from done, right? So they're going to continue to rev, you know, just like we read open shift, that very, very fast base we're gonna be reading ACM and fast face. Also, you see a lot of integration between ACM. A lot of the partners were already working with in the application monitoring space and the analytics space security automation I would expect to see in the uncivil fest time frame, which is mid October, will cease, um, integration with danceable on ACM around things. That insult does very well combined with what ACM does. A sand will continue to push out on Mawr cluster management, more policy based management and certainly advancing the application life cycles that people are very interested in ruined faster. They want to move faster with a higher degree of certainty in their application. Employments on ACM is right there. >>It just final question for you, Joe, is, you know, just in the broader space, looking at management in this kind of cube con cloud, native con ecosystem final words, you want customers to understand where we are today and where we need to go down the road. >>So I think the you know, the market and industry has decided communities is the platform of future right? And certainly we were one of the earliest to invest in container management platforms with open shift were one of the first to invest in communities. We have thousands of customers running open shift back Russell Industries on geography is so we bet on that a long time ago. Now we're betting on the management automation of those environments and bringing them to scale. And the other thing I think that redhead is unique on is that we think that people gonna want to run their kubernetes environments across all different kinds of environments, whether it's on premise visible in virtual multiple public clouds, where we have offerings as well as at the edge. Right. So this is gonna be an environment that's going to be very, very ubiquitous. Pervasive, deported scale. And so the management of a nation has become a necessity. And so but had investing in the right areas to make sure that enterprises continues communities particularly open shift in all the environments that they want at the scale. >>All right. Excellent. Well, Joe, I know we'll be catching up with you and your team for answerable fest. Ah, coming in the fall. Thanks so much for the update. Congratulations to you in the team on the rapid progression of ACM now being G A. >>Thanks to appreciate it, we'll see you soon. >>All right, Stay tuned for more coverage from que con club native con 2020 in Europe, the virtual addition on still minimum and thanks, as always, for watching the Cube.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back, everyone. Live Cube coverage here in San Francisco, California Mosconi North were in the lobby for VM World 2019. I'm John for a day. Volante are 10 years covering VM World's been exciting, Dave, and we've watched all the changes and our next guest is going to illuminate all the benefits at the top of the stack, as I call the end user experience. Shaker Ire, Who's the V S v. P. And general manager End User Computing within VM, where what that means is, he takes care of all the stuff that we're virtualization creates those efficiencies. I think what Palmer's just called end user computing still, but they still have that name back then, if I remember correctly, >> yeah, you >> know the name is stuck because it's ah, it's sort of income, passes all the technologies and uses use right as digital interface is. So that's why it's and use the computing. It's any digital interface that anybody at work uses. Now, the interesting thing is people don't work in an office anymore, and the interface is no longer just a laptop. >> Well, I want to get into some stupid questions around the work environment cause whether you working at a cafe or at home is all kinds of security issues. Also, user experiences. Collaboration software. But let's first get the news out of the way. Digital work, Space news What's the What's going on? The show? What you guys announcing? Yeah, so >> before we get to >> the news that we met me, frame it up a little bit right? Because when you think about organizations today, especially with the changing demographics, where they're going in terms of new devices, the mobility phenomenon, right, the transformation they're going through in terms of just their own cloud and APS and so on, right it. Every every one of those things effects employees, right. And at the end of the day, you know what organizations want is for the employees to have a great experience all the way, as we call it from higher to retire. Not to do that, you know you need a platform because I can just give you a pretty apt running in the laptop and say, Great, that's That's the end of the employees experience, right? It's fundamentally transforming the own whole environment. That's why it's still retains its term and use the computing. And to do that, you have to hit at least three facets, right? One is, of course, How do you deliver a great experience for the employees where they can get any app, any device, anywhere, any form? Anyway, that's one aspect of it. The second aspect of it is from a nightie standpoint. I've gotta manage all this complexity, right, and it's only growing. It's not shrinking with all the head virginity, so there's a management angle of it, and then the tone angle of it is, you know, security. As you pointed out, right security so important. In fact, what you users want is they don't want any security driven compromises. What is an example of security, even compromise, that I have to go through three passwords because he simply don't trust me? Heck, figure it out. Is what the user's Saito I t especially the millennials. Right. So s So you gotta address that. So the platform that we have workspace one actually addresses all three So we have innovations today and news in all three areas, right? So it's an example. Employ experiences, something we've been driving with enterprises and corporations for at least two years. Now we've upped the ante. We have now introducing a virtual assistant that employees can use either through voice or text to essentially ask questions. Hey, what's how do I get into WiFi? What's my employee directory? Um, you know who I go to first? You know this and that, right? As employed onboard the organization. Those examples of virtual assistant can do it. So we released the virtual assistant. That's a big piece of news in the employ experience. Sadie. Another big piece of news is we are introducing a tech preview of what we call digital employees experience management, which means I t now has a user expedient score that they can look at and say, Hey, is David getting a great expedience? No, it's poor, and I can die right in. I can find out the root cause I can fix the issue, and I could do that automatically. >> KP eyes can come out of that right? Absolutely serviceability. >> Absolutely. And I think you know, I've talked to many Cee Io's and we you know, we drive works based one and they for awhile sort of told me, Hey, this is all good. But >> I don't know how I'm doing all my >> doing with respect to, you know, your best best customer. Um, I ahead and behind and far behind. So this really helps them. >> Here. Let me ask the questions. That's a good point I want because this gets down to the heart of the issue. What is the top requests that you're getting from your customers or top two or three features? That pattern that continued comes back from your customer base when it comes to end user computing. These the experience, >> it spends all three things, right? So the first thing is, they're saying, Listen, I want to be able to deliver a great employee experience some, you know, help me do that. Helping measure and make sure I know what journey, Eman That's one right. Second is I've got to set virginity. I've got this complexity of God. You know, I always phones. I've got android tablets. I've got a you know, Dell laptop. I've got a Mac book. I've got you know a rugged device. I've got some work space I ot devices like printers and ex sector X factor. I've got this head virginity. Just help me manage this complexity in a sort of a unified, seamless, uniform way. Right? And third is help me secure my enterprise. So there's a whole model emerging called zero Trust. Where in the old world, what you do is you just build a huge wall around the enterprise, right? A pedometer, and say I'm inside the wall. I need to be domain joined on that inside the fire world. Therefore, I'm good. I mean, you got to throw that out of the window anymore. >> Doesn't exist in your model, because if a millennial or workaround working at home, that means every single i p device on my network potentially a compromise point. >> Correct. So you have You have to start with that device never ought to be trusted. And every network is hostile, right? If you start out for that reminds, then you build trust over time, right? And how do you build trust? You first say you leverage user identity, You say Okay, Davis who he is, right? And so that becomes an identity. You say this device is trusted or partially trusted. So one of the things we're announcing its part of innovations today is what we call workspace to risk analytic, which means we're able to provide a risk or write for the device. And we can say, Hey, this device is a risk on a score of 1 to 10 of eight, which means I can mostly trust it. Maybe you don't trust the sensitive apse. So therefore, a block access to the most sensitive apse, right? So use a combination of different things. They use things like NSX micro segmentation to your point about how we build on the Via Mary Stack. The carbon black acquisition is phenomenal because it gives us that intelligence. So collectively, we're able to sort of implement the zero trust model. Right. So >> those are the >> three main topics, right? Is employed expedience, unified management and zero trust security are really, really >> important. I want to ask you about your tenure, gm, where coincided with the air watch expedition. And prior to that event theme, we're struggled in this space. Ana Citrix dominated your pre Gerald. You know, your former company kind of fumbling around in air watch now. Air watch, if I recall correctly from wrong was not like the number one player. Just like people are saying carbon blacks, not the number one player. Absolutely. And then you get into the VM where flywheel effect or Sanjay Putin came in and it was great leader. But I wonder if you could sort of describe the ascendancy of the end user computing business at at VM wear. And I'm curious you mentioned carbon black and you kind of replicate that with our end point cloud security, peace. There's obviously a security use case. You clearly just described it, but take us back to >> great, great, great question. So actually, I joined right when literally, maybe a month before the air watch acquisition. Right then. So a Sandy and I and the rest of the team sort of worked this. We said, Hey, listen, a watch is a phenomenal sort of mobile management and security player. We had a very good product and horizon VD I, but it was a little bit isolated, and there were others, like, say, tricks that are sort of motor head in that space. So what? The first thing we did is we have three assets. Actually, the third I said what we had a Fed rated identity asset that we had purchase, but not leverage. So we said he know what the identity really has to get coupled with. You know, the death star pulled the mobile world, so we actually took these three piece parts and started integrating it as he started integrating it. We said, You know, this actually forms a very interesting work space, and we said It's a digital work space to be sort of coined that term and started to really tight together. The experience is a user would have, whether they were in a mobile device, a physical desktop or a virtual desktop right and made that seamless. So that's when the work's based one app was born and this was probably around the 2015 time frame. So we started releasing it, and then we started to stitching together basically all the back and integrations, right, So out >> of >> this out of that was born a workspace. And so, in 2016 with the momentum of the workspace, desktop business came back because now it had it been on. We've done a lot of work on the desktop businesses. Well, we made it very competitive with Citrix. We bought volumes. We integrated that we made it actually the best media solution. The markets, with a tremendous traction by itself in the horizon space and then integrating it works with people, said You know what, I need to get that workspace. And why am I dealing with Citrix this horizon solution within workspace in a more than salts my problem. In fact, it's better in certain areas. So that sort of got momentum going around that we really built that workspace momentum. And that was, I would say, till about 2016 or so. And then we saw these three things coming up. One is Hey, employees, experience matters. We really started pouring effort into the employees experience from, you know, day one day two and beyond. And then recently, including this show, we added divided sort of Day zero and then the off boarding pieces. Well, so employees experience became sort of the lightning rod for why somebody would adopt this workspace one platform which were built by then, right, and then we added on this ability to do modern management, especially on Windows and Mac, which was really starting to take off last year completely. Darden rounded out that portfolio and handsome capability, and then we added Now zero trust model, which is which is now sort of bolstered by the acquisition of carbon black. So you can see this a set off cascading talk, full moves. But we did it in a way where, you know, it was really truly integrated. So when as we come out with carbon black now, one of the most interesting things is right when carbon black comes into the fold, we've already done the integration. We're actually going to show it on my keynote right after this, right? We're actually showing the integration between workspace one intelligence and carbon backs You There you have it. You already have an asset that's completely integrated. >> So the risk or is interesting to me as well, so as endpoint security, because much, much more importantly, no fishing is you know, the big way that people get give up credentials. Does >> any of >> this seep into machines and I ot and edge? >> Yeah, and fabulous question. >> Wonder if you could come. >> Absolutely. I think listen, be if you think about risk oars and if >> you think about >> risks at large and devices they've been largely and Windows devices and not to and blame it on Windows, I think they might accept in a fabulous job of sort of progressing windows. But by far it's the most used operating system in the enterprise, right? But Mobile is getting used there. There, you know, it's starting to make a huge starting take a large part of the real estate of the enterprise. So I think we have a unique opportunity now through the data we collect on mobile devices with workspace one using the underlying air watch technology coupled with some of the, um, you know, data that, you know, data analytics tools we have in the carbon black cloud and the way they do sort of threat analysis and, uh, and determine potential attack vectors. We have an opportunity to leverage that intelligence. And that day, the lake and that technology, coupled with the data, we have to really now build a broader sort of threat surface understanding across multiple devices, and eventually that goes into a I ot. Right. So we're actually going to be working with some of the other technologies we have in Wimmer called Paul's Right. Pulse is very interesting because they have the ability to speak multiple device protocols that nobody does. Okay, so we're gonna take advantage of them potentially to sort of be able to start to poke into devices that are attached to the office, but not quite attached to the office. In the sense they're not mainstream devices you and I would use. But indirectly, you may use it, right? So be able to sort of get a much broader view off a visibility of devices. Second is how to manage them through a combination of workspace, one impulse and third, to get the data so that we can feed it into this federated cloud of workspace one intelligence and carbon black to understand the risk. And that way you have this three prom thing, right? I >> wanna ask you a personal question. Pat gal singer was very prolific this week again. Props of in social Media, Mojo doing a selfie on stage with Craig. Job ate up. Yeah, um, doing a little morning thing, telling people how he prepares for his keynote. Yeah. So how do you prepare for your keynote. Do you like, give it for a M and hit the gym and get a job coming up right after this interview? >> I do. I I I'm not fat. That's incredibly disciplined, I think. I think it's been waking up at 4 a.m. for a long time, so I'm not that much of an early bird. But I prepare because, you know, I've been involved in the construction of the keynote. So for me, it's, um, be started work on this, probably about three months ago, because the story came together. It's very natural to me. Just like you asked me the question. You know, tell me about the evolution. It's just a very natural thing because, like telling you >> on relevant story, not just beady eye. Yeah, it's so much more now. >> It's so much more And, you know, and I've lived through this and I participated in most of the decision making, so, you know, when my head of product marketing company said, Hey, what should we do with the keynote? I said, You know, I have the storyline in mind, right? And sit on the same three or four pillars I'm talking to you about, right? How do we tell the story to the audience about what is the platform? Why should they sort of bet on it? How did they sort of deploy it, show them some real world examples and then basically sprinkling all the innovations? That sounds exciting. So? So because of that story lines always being in my head. So it's not that hard. It's just sometimes you just need to sort of a CZ. You're unstable. >> You're preparing Saul, you're part of Yeah, I was handing it to you. Nobody related it. So >> for me, I think it's just sometimes just rehearsing some of the key parts. And then, of course, the visual cues. And they >> want to slam home the big point. They go. You know, I've been looking at your career. You have to check your technologies, but also, you're pretty much been a product leader. Yeah, and your career definite. So I gotta ask you around from the big movements in the innocent. Like your perspective as a participant. This was a product leaders Well, executive in there and done that. Amazon introduced their first conference around cloud security called reinforces. Here we get Cube coverage there. It was interesting because it wasn't like a typical security conference like black hat. Definitely on our say wasn't so much I t is really about cloud security. And so Dave and I were speculating like, this is the first cloud security show. I mean, dedicated to kind of cloud security didn't say cloud security, but essentially, cloud security. >> What is >> your take on the cloud security? Because a >> little bit >> of a different view, little bit architectural change. If you gotta have the on premises, you're gonna have the cloud if things any working together, some things you're doing and security quite frankly, around isolation to, you know, working in in any environment. You're that year in the middle of it all. >> Yeah. >> What is cloud >> security and why I have a conference isn't relevant with your thoughts. >> That's a >> great question. I think you know, you see many of these trends, I think, you know, listen, many of these conferences, they provoke their thought provoking, so it forces you to think right? So when I think about cloud security now, traditionally when you think about cloud security, you would think about technologies like Cass be light cloud access service broker. You would think about encryption to means much more than I do >> all the usual stuff in the back. If he's there, other people are there. But no. >> Yeah, I mean more than my coffee. I think you know you. It's sort of you think of the the the NL unlocked to cloud securities Data center security where you think of the sort of Amazon cloud living in Amazon Data Center. And, you know, how can we protect the, you know, the data and the egress access into those cloud and in the same technology sort of apply, but to your point that you sort of just touched upon its That cloud is not living in isolation. First of all, that Amazon Cloud is connected to a whole bunch of, you know, applications that are still sitting in the data center. Right. So they were not there. Potentially not moving the Oracle database today isn't there moving some workloads to the cloud, right? That's what most most companies are. Hey, guess what? There's all these end points of the connecting the connecting both the data center in the cloud. You're not gonna proxy to the cloud to get to the data center. So there's gateways. So do me. Cloud security can't be an isolated, you know, sort of technology that companies have to sort of think about now is there Is there opportunity to leverage the cloud to manage security better and get visibility in the security environment to do security? Analytics? Absolutely. So I think to me, that's where it's going. Because security, I think, has been proven, is no longer. You know, the one sing single thing. It's just you have to do multiple things. Every time I go talk to CSO's, they tell me they got this technology. I said, Hey, wait a minute. You you have 20. Did you cut down any yet? We've got down a few, but you know, they're just nervous about cutting down too much. Because of that one piece of software >> insurance policy. They're insecure. >> They cut to the added four, >> another tool. Bullshit. I think I think the architecture will get simpler because it's way too complex, but the same time I think you have to. There's no sustenance, cloud security and network security or endpoint security, and >> maybe there's a whole new group emerging within VM where that you could add to your repertoire en Pointe computing group your end user computing. Why don't have endpoint computing? That's >> what you're holding >> is you know is all about what do we need to do for the user? Both as I t and the end user? Okay. And now he now folks like hr and so on, the securities has to be built into it, right? So much like that. I think when you go build our data centers are the public cloud and build this hybrid clouds, security is to be built into that as >> well. We'll shake our thanks for coming on and sharing your insights. A super important area. We're gonna be covering this. This is cloud to point of this end user computing. This is where the edge of the network is. That's where the people are. They are part of the edge. A thin part of the edge of a big part of the edge. You're gonna be in the middle of it will be following the attraction. Thanks for coming on. You So much for having me having played Cuba, Cuba live here in San Francisco on chopper develop the state tune from or we have two sets. Three days of wall to wall coverage, worldly in day one. Stay with us. We gotta have Michael Dell. Pat Nelson. Come on Tomorrow and a lot more guests coming onto. They stay with us. We'll be right back.

>> No. Yeah. Wait. >> Hi, everyone, and welcome to the big data. Deep Dive with the Cube on AMC TV. I'm Richard Schlessinger, and I'm here with tech industry entrepreneur and wicked bond analyst Dave Volonte and Silicon Angle CEO and editor in chief John Furrier. For this last segment in our show, we're talking about the future of big data and there aren't two better guys to talk about that you and glad that you guys were here. Let me sort of tee up the this conversation a little bit with a video that we did. Because the results of big data leveraging are only as good as the data itself. There has to be trust that the data is true and accurate and as unbiased as possible. So AMC TV addressed that issue, and we're just trying to sort of keep the dialogue going with this spot. >> We live in a world that is in a constant state of transformation, political natural transformation that has many faces, many consequences. A world overflowing with information with the potential to improve the lives of millions with prospects of nations with generations in the balance way are awakening to the power of big data way trust and together transform our future. >> So, Gentlemen Trust, without that, where are we and how big of an issue is that in the world of big data? Well, you know, the old saying garbage in garbage out in the old days, the single version of the truth was what you were after with data warehousing. And people say that we're further away from a single version of the truth. Now with all this data. But the reality is with big data and these new algorithms you, khun algorithmic Lee, weed out the false positives, get rid of the bad data and mathematically get to the good data a lot faster than you could before. Without a lot of processes around it. The machines can do it for you. So, John, while we were watching that video, you murmured something about how this is the biggest issue. This is cutting edge stuff. This is what I mean. >> Trust, trust issues and trust the trust equation. Right now it is still unknown. It's evolving fast. You see it with social networks, Stevens go viral on the internet and and we live in a system now with mobility and cloud things. Air scaling infinitely, you know, these days and so good day two scales, big and bad data scales being so whether it's a rumor on you here and this is viral or the data data, trust is the most important issue, and sometimes big data can be creepy. So a. This really, really important area. People are watching it on DH. Trust is the most important thing. >> But, you know, you have to earn trust, and we're still sort of at the beginning of this thing. So what has to happen to make sure that you know you don't get the garbage in, so you get the garbage. >> It's iterative and and we're seeing a lot of pilot projects. And then those pilot projects get reworked, and then they spawn into new projects. And so it's an evolution. And as I've said many, many times, it's very early we've talked about, were just barely scratching the surface here. >> It's evolving, too, and the nature of the data is needs to be questioned as well. So what kind of data? For instance, if you don't authorize your data to be viewed, there's all kinds of technical issues around. >> That's one side of it, But the other side of it, I mean, they're bad people out there who would try to influence, Uh, you know what? Whatever conclusions were being drawn by big data programs, >> especially when you think about big data sources. So companies start with their internal data, and they know that pretty well. They know where the warts are. They know how to manipulate. It's when they start bringing in outside data that this gets a lot fuzzier. >> Yeah, it's a problem. And security talk to a guy not long ago who thought that big data could be used to protect big data, that you could use big data techniques to detect anomalies in data that's coming into the system, which is poetic if nothing else, that guys think data has told me that that that's totally happened. It's a good solution. I want to move on because way really want to talk about how this stuff is going to be used. Assuming that these trust issues can be solved on and you know, the best minds in the world are working on this issue to try to figure out how to best, you know, leverage the data, we all produce, which has been measured at five exabytes every two days. You know, somebody made an analogy with, like something. If a bite was a paper clip and you stretched five exabytes worth of paper clips, they would go to the moon or whatever. Anyway, it's a lot of bike. It's a lot of actually, I think that's a lot of fun and back way too many times one hundred thousand times I lost track of my paper. But anyway, the best minds are trying to figure out, you know, howto, you know, maximize that the value that data. And they're doing that not far from here where we sit. Uh, Emmett in a place called C Sale, which was just recently set up, See Sail stands for the computer signs, an artificial intelligence lab. So we went there not long ago. It's just, you know, down the Mass. Pike was an easy trip, and this is what we found. It's fascinating >> Everybody's obviously talking about big data all the time, and you hear it gets used to mean all different types of things. So he thinks we're trying to do in the big data. Is he? Still program is to understand what are the different types of big data that exists in the world? And how do we help people to understand what different problems or fall under the the overall umbrella of big data? She sells the largest interdepartmental laboratory and mitt, so there's about one hundred principal investigators. So that's faculty and sort of senior research scientists. About nine hundred students who are involved, >> basically with big data, almost anything to do with it has to be in a much larger scale than we're used to, and the way it changes that equation is you have to You have to have the hardware and software to do the things you're used to doing. You have to meet them of comedy's a larger size a much larger size >> of times. When people talk about big data, they, I mean, not so much the volume of the data, but that the data, for example, is too complex for their existing data. Processing system to be able to deal with it. So it's I've got information from Social network from Twitter. I've got your information from a person's mobile phone. Maybe I've got information about retail records. Transactions hole Very diverse set of things that need to be combined together. What this clear? It says this is If you added this, credit it to your query, you would remove the dots that you selected. That's part of what we're trying to do here. And big data is he sail on. Our big data effort in general at MIT is toe build a set of software tools that allow people to take all these different data sets, combine them together, asked questions and run algorithms on top of them that allowed him to extracting sight. >> I'm working with it was dragged by NASA, but the purpose of my work right now is Tio Tio. Take data sets within Davis's, and instead of carrying them for table results, you query them, get visualizations. So instead of looking at large sets of numbers and text him or not, you get a picture and gave the motivation Behind that is that humans are really good into pretty pictures. They're not so that interpreting huge tables with big data, that's a really big issue. So this will have scientists tio visualize their data sets more quickly so they can start exploring And, uh, just looking at it faster, because with big data, it's a challenge to be able to visualize an exploiter data. >> I'm here just to proclaim what you already know, which is that the hour of big data has arrived in Massachusetts, and >> it's a very, very exciting time. So Governor Patrick was here just a few weeks ago to announce the Mass Big Data Initiative. And really, I think what he recognizes and is partly what we recognize here is that there's a expertise in the state of Massachusetts in areas that are related to big data, partly because of companies like AMC, as well as a number of other companies in this sort of database analytic space, CMC is a partner in our big data detail, initiatives and big data and See Sale is industry focused initiative that brings companies together to work with Emmet T. Think about it. Big data problems help to understand what big data means for the companies and also to allow the companies to give feedback. Tow us about one of the most important problems for them to be working on and potentially expose our students and give access to these companies to our students. >> I think the future will tell us, and that's hard to say right now, because way haven't done a lot of thinking, and I was interpreting and Big Data Way haven't reached our potential yet, and I just there's just so many things that we can't see right now. >> So one of the things that people tell us that are involved in big data is they have trouble finding the skill sets the data. Science can pick capability and capacity. And so seeing videos like this one of them, it is a new breed of students coming out there. They're growing up in this big data world, and that's critical to keep the big data pipeline flowing. And Jon, you and I have spent a lot of time in the East Coast looking at some of the big data cos it's almost a renaissance for Massachusetts in Cambridge and very exciting to see. Obviously, there's a lot going on the West Coast as well. Yeah, I mean, I'll say, I'm impressed with Emmett and around M I. T. In Cambridge is exploding with young, young new guns coming out of there. The new rock stars, if you will. But in California we're headquartered in Palo Alto. You know we in a chance that we go up close to Google Facebook and Jeff Hammer backer, who will show a video in a second that I interview with him and had dupe some. But he was the first guy a date at Facebook to build the data platform, which now has completely changed Facebook and made it what it is. He's also the co founder of Cloudera The Leader and Had Duke, which we've talked about, and he's the poster child, in my opinion of a data scientist. He's a math geek, but he understands the world problems. It's not just a tech thing. It's a bigger picture. I think that's key. I mean, he knows. He knows that you have to apply this stuff so and the passion that he has. This video from Jeff Hammer Bacher, cofounder of Cloud Ear, Watches Video. But and then the thing walk away is that big data is for everyone, and it's about having the passion. >> Wait. Wait. >> Palmer Bacher Data scientists from Cloudera Cofounder Hacking data Twitter handle Welcome to the Cube. >> Thank you. >> So you're known in the industry? I'LL see. Everyone knows you on Twitter. Young Cora heavily follow you there at Facebook. You built the data platform for Facebook. One of the guys mean guys. They're hacking the data over Facebook. Look what happened, right? I mean, the tsunami that Facebook has this amazing co founder Cloudera. You saw the vision on Rommedahl always quotes on the Cube. We've seen the future. No one knows it yet. That was a year and a half ago. Now everyone knows it. So do you feel about that? Is the co founder Cloudera forty million thousand? Funding validation again? More validation. How do you feel? >> Yeah, sure, it's exciting. I think of you as data volumes have grown and as the complexity of data that is collected, collected and analyzed as increase your novel software architectures have emerged on. I think what I'm most excited about is the fact that that software is open source and we're playing a key role in driving where that software is going. And, you know, I think what I'm most excited about. On top of that is the commodification of that software. You know, I'm tired of talking about the container in which you put your data. I think a lot of the creativity is happening in the data collection integration on preparation stage. Esso, I think. You know, there was ah tremendous focus over the past several decades on the modeling aspect of data way really increase the sophistication of our understanding, you know, classification and regression and optimization. And all off the hard court model and it gets done. And now we're seeing Okay, we've got these great tools to use at the end of the pipe. Eso Now, how do we get more data pushed through those those modeling algorithm? So there's a lot of innovative work. So we're thinking at the time how you make money at this or did you just say, Well, let's just go solve the problem and good things will happen. It was it was a lot more the ladder. You know, I didn't leave Facebook to start a company. I just left Facebook because I was ready to do something new. And I knew this was a huge movement and I felt that, you know, it was very gnashing and unfinished a software infrastructure. So when the opportunity Cloudera came along, I really jumped on it. And I've been absolutely blown away by the commercial success we've had s o. I didn't I certainly didn't set out with a master plan about how to extract value from this. My master plan has always been to really drive her duped into the background of enterprise infrastructure. I really wanted to be as obvious of a choice as Lennox and you See you, you're We've talked a lot at this conference and others about, you know, do moving from with fringe to the mainstream commercial enterprises. And all those guys are looking at night J. P. Morgan Chase. Today we're building competitive advantage. We're saving money, those guys, to have a master plan to make money. Does that change the dynamic of what you do on a day to day basis, or is that really exciting to you? Is an entrepreneur? Oh, yeah, for sure. It's exciting. And what we're trying to do is facilitate their master plan, right? Like we wanted way. Want to identify the commonalities and everyone's master plan and then commoditize it so they can avoid the undifferentiated heavy lifting that Jeff Bezos points out. You know where you know? No one should be required, Teo to invest tremendous amounts of money in their container anymore, right? They should really be identifying novel data sources, new algorithms to manipulate that data, the smartest people for using that data. And that's where they should be building their competitive advantage on. We really feel that, you know, we know where the market's going on. We're very confident, our product strategy. And I think over the next few years, you know, you guys are gonna be pretty excited about the stuff we're building, because I know that I'm personally very excited. And yet we're very excited about the competition because number one more people building open source software has never made me angry. >> Yeah, so So, you know, that's kind of market place. So, you know, we're talking about data science building and data science teams. So first tell us Gerald feeling today to science about that. What you're doing that, Todd here, around data science on your team and your goals. And what is a data scientist? I mean, this is not, You know, it's a D B A for her. Do you know what you know, sheriff? Sure. So what's going on? >> Yeah, So, you know, to kind of reflect on the genesis of the term. You know, when we were building out the data team at Facebook, we kind of two classes of analysts. We had data analysts who are more traditional business intelligence. You know, building can reports, performing data, retrieval, queries, doing, you know, lightweight analytics. And then we had research scientists who are often phds and things like sociology or economics or psychology. And they were doing much more of the deep dive, longitudinal, complex modeling exercises. And I really wanted to combine those two things I didn't want to have. Those two folks be separate in the same way that we combined engineering and operations on our date infrastructure group. So I literally just took data analyst and research scientists and put them together and called it data scientist s O. So that's kind of the the origin of the title on then how that's translating what we do at Clyde era. So I've recently hired to folks into a a burgeoning data science group Cloudera. So the way we see the market evolving is that you know the infrastructure is going to be commoditized. Yes, mindset >> to really be a data scientists, and you know what is way should be thinking about it. And there's no real manual. Most people aboard that math skills, economic kinds of disciplines you mentioned. What should someone prepared themselves? How did they? How does someone wanna hire data scientist had, I think form? Yeah, kinds of things. >> Well, I tend to, you know, I played a lot of sports growing up, and there's this phrase of being a gym rat, which is someone who's always in the gym just practicing. Whatever support is that they love. And I find that most data scientists or sort of data rats, they're always there, always going out for having any data. So you're there's a genuine curiosity about seeing what's happening and data that you really can't teach. But in terms of the skills that are required, I didn't really find anyone background to be perfect. Eso actually put together a course at University California, Berkeley, and taught it this spring called Introduction to Data Science, and I'm teaching and teaching it again this coming spring, and they're actually gonna put it into the core curriculum. Uh, in the fall of next year for computer science. >> Right, Jack Harmer. Bakar. Thanks so much for that insight. Great epic talk here on the Cube. Another another epic conversations share with the world Live. Congratulations on the funding. Another forty months. It's great validation. Been congratulations for essentially being part of data science and finding that whole movement Facebook. And and now, with Amaar Awadallah and the team that cloud there, you contend a great job. So congratulations present on all the competition keeping you keeping a fast capitalism, right? Right. Thank >> you. But it's >> okay. It's great, isn't it? That with all these great minds working in this industry, they still can't. We're so early in this that they still can't really define what a data scientist is. Well, what does talk about an industry and its infancy? That's what's so exciting. Everyone has a different definition of what it is, and that that what that means is is that it's everyone I think. Data science represents the new everybody. It could be a housewife. It could be a homemaker to on eighth grader. It doesn't matter if you see an insight and you see something that could be solved. Date is out there, and I think that's the future. And Jeff Hamel could talked about spending all this time and technology with undifferentiated heavy lifting. And I'm excited that we are moving beyond that into essentially the human part of Big Data. And it's going to have a huge impact, as we talked about before on the productivity of organizations and potentially productivity of lives. I mean, look at what we've talked about this this afternoon. We've talked about predicting volcanoes. We've talked about, you know, the medical issues. We've talked about pretty much every aspect of life, and I guess that's really the message of this industry now is that the folks who were managing big data are looking too change pretty much every aspect of life. This is the biggest inflexion point in history of technology that I've ever seen in the sense that it truly affects everything and the data that's generated in the data that machine's generate the data that humans generate, data that forest generate things like everything is generating data. So this's a time where we can actually instrument it. So this is why this massive disruption, this area and disruption We should say the uninitiated is a good thing in this business. Well, creation, entrepreneurship, copies of being found it It's got a great opportunity. Well, I appreciate your time, I unfortunately I think that's going to wrap it up for our big date. A deep dive. John and Dave the Cube guys have been great. I really appreciate you showing up here and, you know, just lending your insights and expertise and all that on DH. I want to thank you the audience for joining us. So you should stay tuned for the ongoing conversation on the Cube and to emcee TV to be informed, inspired and hopefully engaged. I'm Richard Schlessinger. Thank you very much for joining us.