>> Narrator: Live from Las Vegas, it's theCUBE. Covering Dell EMC World 2017, brought to you by Dell EMC. >> Okay, welcome back everyone, we are live in Las Vegas for Dell EMC World 2017. This is theCUBE's eighth year of coverage of what was once EMC World, now it's Dell EMC World 2017. I'm John Furrier at SiliconANGLE, and also my cohost from SiliconANGLE, Paul Gillin. Our next guest is Vikram Bhambri, who is the Vice President of Product Management at Dell EMC. Formally with Microsoft Azure, knows cloud, knows VIPRE, knows the management, knows storage up and down, the Emerging Technologies Group, formerly of EMC. Good to see you on theCUBE again. >> Good to see you guys again. >> Okay, so Elastic Compute, this is going to be the game changer. We're so excited about one of our favorite interviews was your colleague we had on earlier. Unstructured data, object store, is becoming super valuable. And it was once the throwaway, "Yeah, store, later late ". Now with absent data driven enterprises having access to data is the value proposition that they're all driving towards. >> Absolutely. >> Where are you guys with making that happen and bringing that data to life? >> So, when I think about object storage in general, people talk about it's the S3 protocol, or it's the object protocol versus the file protocol. I think the conversation is not about that. The conversation is about data of the universe is increasing and it's increasing tremendously. We're talking about 44 zettabytes of data by 2020. You need an easier way to consume, store, that data in a meaningful way, and not only just that but being able to derive meaningful insights out of that either when the data is coming in or when the data is stored on a periodic basis being able to drive value. So having access to the data at any point of time, anywhere, is the most important aspect of it. And with ECS we've been able to actually attack the market from both sides. Whether it's talking about moving data from higher cost storage arrays or higher performance tiers down to a more accessible, more cheap storage that is available geographically, that's one market. And then also you have tons of data that's available on the tape drive but that data is so difficult to access, so not available. And if you want to go put that tape back on a actual active system the turnaround time is so long. So being able to turn all of that storage into an active storage system that's accessible all the time is the real value proposition that we have to talk about. >> Well now help me understand this because we have all these different ways to make sense of unstructured data now. We have NoSQL databases, we have JSON, we have HDFS, and we've got object storage. Where does it fit into the hierarchy of making sense of unstructured data? >> The simplest way to think about it is we talk about a data ocean, with the amount of data that's growing. Having the capability to store data that is in a global content repository. That is accessible-- >> Meaning one massive repository. >> One massive repository. And not necessarily in one data center, right? It's spread across multiple data centers, it's accessible, available with a single, global namespace, regardless of whether you're trying to access data from location A or location B. But having that data be available through a single global namespace is the key value proposition that object storage brings to bear. The other part is the economics that we're able to provide consistently better than what the public clouds are able to offer. You're talking about anywhere between 30 to 48% cheaper TCO than what public clouds are able to offer, in your own data center with all the constraints that you want to like upload to it, whether it's regular environments. Whether you're talking about country specific clouds and such, that's where it fits well together. But, exposing that same data out whether through HDFS or a file is where ECS differentiated itself from other cloud platforms. Yes, you can go to a Hadoop cluster and do a separate data processing but then you're creating more copies of the same data that you have in your primary storage. So things like that essentially help position object as the global content repository where you can just dump and forget about, about the storage needs. >> Vikram I want to ask you about the elastic cloud storage, as you mentioned, ECS, it's been around for a couple of years. You just announced a ECS lesser cloud storage, dedicated cloud. Can you tell me what that is and more about that because some people think of elastic they think Amazon, "I'll just throw it in object storage in the cloud." What are you guys doing specifically 'cause you have this hybrid offering. >> Absolutely. >> What is this about, can you explain that? >> Yeah, so if you look at, there are two extremes, or two paradigms that people are attracted by. On one side you have public clouds which give you the ease of use, you just swipe your credit card and you're in business. You don't have to worry about the infrastructure, you don't have to worry about, like, "Where my data is going to be stored?" It's just there. And then on the other side you have regular environments or you just have environments where you cannot move to public clouds so customers end up put in ECS, or other object storage for that matter, though ECS is the best. >> John: Biased, but that's okay. >> Yeah, now we are starting to see customers they're saying, "Can I have the best of both worlds? "Can I have a situation where I like the ease of use "of the public cloud but I don't want to "be in a shared bathtub environment. "I don't want to be in a public cloud environment. "I like the privacy that you are able to provide me "with this ECS in my own data center "but I don't want to take on the infrastructure management." So for those customers we have launched ECS dedicated cloud service. And this is specifically targeted for scenarios where customers have maybe one data center, two data centers, but they want to use the full strength and the capabilities of ECS. So what we're telling them we will actually put their bought ECS in our data centers, ECS team will operate and manage that environment for the customer but they're the only dedicated customer on that cloud. So that means they have their own environment-- >> It's completely secure for their data. >> Vikram: Exactly. >> No multi tenant issues at all. >> No, and you can have either partial capabilities in our data center, or you can fully host in our data center. So you can do various permutation and combinations thus giving customers a lot of flexibility of starting with one point and moving to the other. Let's them start with a private cloud, they want to move to a hybrid version they can move that, or if they start from the hybrid and they want to go back to their own data centers they can do that as well. >> Let's change gears and talk about IoT. You guys had launched Project Nautilus, we also heard that from your boss earlier, two days ago. What is that about? Explain, specifically, what is Project Nautilus? >> So as I was mentioning earlier there is a whole universe of data that is now being generated by these IoT devices. Whether you're talking about connected cars, you're talking about wind sensors, you're talking about anything that collects a piece of data that needs to be not only stored but people want to do realtime analysis on that dataset. And today people end up using a combination of 10 different things. They're using Kafka, Speak, HDFS, Cassandra, DASH storage to build together a makeshift solution, that sort of works but doesn't really. Or you end up, like, if you're in the public cloud you'll end up using some implementation of Lambda Architecture. But the challenge there is you're storing same amount of data in a few different places, and not only that there is no consistent way of managing data, processing data that effectively. So what Project Nautilus is our attempt to essentially streamline all of that. Allow stream of data that's coming from these IoT devices to be processed realtime, or for batch, in the same solution. And then once you've done that processing you essentially push that data down to a tier, whether it's Isilon or ECS, depending on the use case that you are trying to do. So it simplifies the whole story on realtime analytics and you don't want to do it in a closed source way. What we've done is we've created this new paradigm, or new primitive called streaming storage, and we are open sourcing it, we are Project Pravega, which is in the Apache Foundation. We want the whole community, just like there is a common sense of awareness for object file we want to that same thing for streaming storage-- >> So you guys are active in open source. Explain quickly, many might not know that. Talk about that. >> So, yeah, as I mentioned Project Prevega is something we announced at Flink Forward Conference. It's a streaming storage layer which is completely open source in the Apache Foundation and we just open sourced it today. And giving customers the capability to contribute code to it, take their version, or they can do whatever they want to do, like build additional innovation on top. And the goal is to make streaming storage just like a common paradigm like everything else. And in addition we're partnering with another open source component. There is a company called data Artisans based out of Berlin, Germany, and they have a project called Flink, and we're working with them pretty closely to bring Nautilus to fruition. >> theCUBE was there by the way, we covered Flink Forward again, one of the-- >> Paul: True streaming engine. >> Very good, very big open source project. >> Yeah, we we're talking with Jeff Woodrow earlier about software defined storage, self driving storage as he calls it. >> Where does ECS fit in the self driving storage? Is this an important part of what you're doing right now or is it a different use? >> Yeah, our vision right from the beginning itself was when we built this next generation of object storage system it has to be software first. Not only software first where a customer can choose their commodity hardware to bring to bear or we an supply the commodity hardware but over time build intelligence in that layer of software so that you can pull data off smartly to other, from SSDs to more SATA based drives. Or you can bring in smarts around metadata search capabilities that we've introduced recently. Because you have now billions of billions of records that are being stored on ECS. You want ease of search of what specifically you're looking for, so we introduced metadata search capability. So making the storage system and all of the data services that were usually outside of the platform, making them be part of the code platform itself. >> Are you working with Elasticsearch? >> Yes, we are using Elasticsearch more to enable customers who want to get insights about ECS itself. And Nautilus, of course, is also going to integrate with Elasticsearch as well. >> Vikram let's wrap this up. Thank you for coming on theCUBE. Bottom line, what's the bottom line message, quickly, summarize the value proposition, why customers should be using ECS, what's the big aha moment, what's the proposition? >> I would say the value proposition is very simple. Sometimes it can be like, people talk about lots of complex terms, it's very simple. Sustainably, low cost storage, for storing a wide variety of content in a global content repository is the key value proposition. >> And used for application developers to tap into? The whole dev ops, data as code, infrastructure as code movement. >> Yeah, you start, what we have seen in the majority of the used cases customers start with one used case of archiving. And then they very quickly realize that there's, it's like a Swiss Army knife. You start with archiving then you move on to application development, more modern applications, or in the cloud native applications development. And now with IoT and Nautilus being able to leverage data from these IoT devices onto these-- >> As I said two days ago, I think this is a huge, important area for agile developers. Having access to data in less than a hundred milliseconds, from any place in the world, is going to be table steaks. >> ECS has to be, or in general, object storage, has to be part of every important conversation that is happening about digital IT transformation. >> It sounds like eventually most of the data's going to end up there. >> Absolutely. >> Okay, so I'll put ya on the spot. When are we going to be seeing data in less than a hundred milliseconds from any database anywhere in the fabric of a company for a developer to call a data ocean and give me data back from any database, from any transaction in less than a hundred milliseconds? Can we do that today? >> We can do that today, it's available today. The challenge is how quickly enterprises are adopting the technology. >> John: So they got to architect it? >> Yeah. >> They have to architect it. >> Paul: If it's all of Isilon. >> They can pull it, they can cloud pull it down from Isilon to ECS. >> True. >> Yeah. >> Speed, low latency, is the key to success. Congratulations. >> Thank you so much. >> And I love this new object store, love this tier two value proposition. It's so much more compelling for developers, certainly in cloud native. >> Vikram: Absolutely. >> Vikram, here on theCUBE, bringing you more action from Las Vegas. We'll be right back as day three coverage continues here at Dell EMC World 2017. I'm John Furrier with Paul Gillan, we'll be right back.

okay welcome back to IBM's information on demand live in Las Vegas this is the cube SiliconANGLE movie bonds flagship program we go out to the events it's check the student from the noise talk to the thought leaders get all the data share that with you and you go to SiliconANGLE com or Wikibon or to get all the footage and we're if you want to participate with us we're rolling out our new innovative crowd activated innovation application called crowd chat go to crouch at net / IBM iod just login with your twitter handle or your linkedin and participate and share your voice is going to be on the record transcript of the cube conversations I'm John furrier with silicon items with my co-host hi buddy I'm Dave vellante Wikibon dork thanks for watching aren't you Oh bhambri is here she's the vice president of big data and analytics at IBM many time cube guests as you welcome back good to see you again thank you so we were both down at New York City last week for the hadoop world really amazing to see how that industry has evolved I mean you guys I've said the number of times today and I said this to you before you superglued your your big data or your analytics business to the Big Data meme and really created a new category I don't know if that was by design or you know or not but it certainly happened suddenly by design well congratulations then because because I think that you know again even a year a year and a half ago those two terms big data and analytics were sort of separate now it's really considered as one right yeah yeah I think because initially as people our businesses started getting really flooded with big data right dealing with the large volumes dealing with structured semi-structured or unstructured data they were looking at that you know how do you store and manage this data in a cost-effective manner but you know if you're just only storing this data that's useless and now obviously it's people realize that they need and there is insights from this data that has to be gleaned and there's technology that is available to do that so so customers are moving very quickly to that it's not just about cost savings in terms of handling this data but getting insights from it so so big data and analytics you know is becoming it's it's becoming synonymous heroes interesting to me on Jules is you know just following this business it's all it's like there's a zillion different nails out there and and and everybody has a hammer and they're hitting the nail with their unique camera but I've it's like IBM as a lot of different hammers so we could talk about that a little bit you've got a very diverse portfolio you don't try to force one particular solution on the client you it sort of an it's the Pens sort of answer we could talk about that a little bit yeah sure so in the context of big data when we look at just let's start with transactional data right that continues to be the number one source where there is very valuable insights to be gleaned from it so the volumes are growing that you know we have retailers that are handling now 2.5 million transactions per hour a telco industry handling 10 billion call data detailed records every day so when you look at that level that volume of transactions obviously you need to be you need engines that can handle that that can process analyze and gain insights from this that you can get you can do ad hoc analytics on this run queries and get information out of this at the same speed at which this data is getting generated so you know we we announced the blu acceleration rate witches are in memory columnstore which gives you the power to handle these kinds of volumes and be able to really query and get value out of this very quickly so but now when you look at you know you go beyond the structured data or beyond transactional data there is semi structured unstructured data that's where which is still data at rest is where you know we have big insights which leverages Apache Hadoop open source but we've built lots of capabilities on top of that where we get we give the customers the best of open source plus at the same time the ability to analyze this data so you know we have text analytics capabilities we provide machine learning algorithms we have provided integration with that that customers can do predictive modeling on this data using SPSS using open source languages like our and in terms of visualization they can visualize this data using cognos they can visualize this data using MicroStrategy so we are giving customers like you said it's not just you know there's one hammer and they have to use that for every nail the other aspect has been around real time and we heard that a lot at strada right in the like I've been going to start us since the beginning and those that time even though we were talking about real time but nobody else true nobody was talking nobody was back in the hadoop world days ago one big bats job yeah so in real time is now the hotbed of the conversation a journalist storm he's new technologies coming out with him with yarn has done it's been interesting yeah you seen the same thing yeah so so and and of course you know we have a very mature technology in that space you know InfoSphere streams for a real-time analytics has been around for a long time it was you know developed initially for the US government and so we've been you know in the space for more than anybody else and we have deployments in the telco space where you know these tens of billions of call detail records are being processed analyzed in real time and you know these telcos are using it to predict customer churn to prevent customer churn gaining all kinds of insights and extremely high you know very low latency so so it's good to see that you know other companies are recognizing the need for it and are you know bringing other offerings out in this space yes every time before somebody says oh I want to go you know low latency and I want to use spark you say okay no problem we could do that and streets is interesting because if I understand it you're basically acting on the data producing analytics prior to persisting the data on in memory it's all in memory and but yet at the same time is it of my question is is it evolving where you now can blend that sort of real-time yeah activity with maybe some some batch data and and talk about how that's evolving yeah absolutely so so streams is for for you know where as data is coming in it can be processed filtered patterns can be seen in streams of data by correlating connecting different streams of data and based on a certain events occurring actions can be taken now it is possible that you know all of this data doesn't need to be persisted but there may be some aspects or some attributes of this data that need to be persisted you could persist this data in a database that is use it as a way to populate your warehouse you could persist it in a Hadoop based offering like BigInsights where you can you know bring in other kinds of data and enrich the data it's it's like data loans from data and a different picture emerges Jeff Jonas's puzzle right so that's that that's very valid and so so when we look at the real time it is about taking action in real time but there is data that can be persisted from that in both the warehouse as well as on something like the insides are too I want to throw a term at you and see what what what this means to you we actually doing some crowd chats with with IBM on this topic data economy was going to SS you have no date economy what does the data economy mean to you what our customers you know doing with the data economy yes okay so so my take on this is that there are there are two aspects of this one is that the cost of storing the data and analyzing the data processing the data has gone down substantially the but the value in this data because you can now process analyze petabytes of this data you can bring in not just structured but semi-structured and unstructured data you can glean information from different types of data and a different picture emerges so the value that is in this data has gone up substantially I previously a lot of this data was probably discarded people without people knowing that there is useful information in this so to the business the value in the data has gone up what they can do with this data in terms of making business decisions in terms of you know making their customers and consumers more satisfied giving them the right products and services and how they can monetize that data has gone up but the cost of storing and analyzing and processing has gone down rich which i think is fantastic right so it's a huge win win for businesses it's a huge win win for the consumers because they are getting now products and services from you know the businesses which they were not before so that that to me is the economy of data so this is why I John I think IBM is really going to kill it in this in this business because they've got such a huge portfolio they've got if you look at where I OD has evolved data management information management data governance all the stuff on privacy these were all cost items before people looked at him on I gotta deal with all this data and now it's there's been a bit flip uh-huh IBM is just in this wonderful position to take advantage of it of course Ginny's trying to turn that you know the the battleship and try to get everybody aligned but the moons and stars are aligning and really there's a there's a tailwind yeah we have a question on domains where we have a question on Twitter from Jim Lundy analyst former Gartner analyst says own firm now shout out to Jim Jim thanks for for watching as always I know you're a cube cube alum and also avid watcher and now now a loyal member of the crowd chat community the question is blu acceleration is helps drive more data into actionable analytics and dashboards mm-hmm can I BM drive new more new deals with it I've sued so can you expound it answers yes yes yes and can you elaborate on that for Jim yeah I you know with blu acceleration you know we have had customers that have evaluated blue and against sa bihana and have found that what blue can provide is is they ahead of what SI p hana can provide so we have a number of accounts where you know people are going with the performance the throughput you know what blue provides is is very unique and it's very head of what anybody else has in the market in solving SI p including SI p and and you know it's ultimately its value to the business right and that's what we are trying to do that how do we let our customers the right technology so that they can deal with all of this data get their arms around it get value from this data quickly that's that's really of a sense here wonderful part of Jim's question is yes the driving new deals for sure a new product new deals me to drive new footprints is that maybe what he's asking right in other words you traditional IBM accounts are doing doing deals are you able to drive new footprints yeah yeah we you know there are there are customers that you know I'm not gonna take any names here but which have come to us which are new to IBM right so it's a it's that to us and that's happening that new business that's Nate new business and that's happening with us for all our big data offerings because you know the richness that is there in the portfolio it's not that we have like you were saying Dave it's not that we have one hammer and we are going to use it for every nail that is out there you know as people are looking at blue big insights for her to streams for real time and with all this comes the whole lifecycle management and governance right so security privacy all those things don't don't go away so all the stuff that was relevant for the relational data now we are able to bring that to big data very quickly and which is I think of huge value to customers and as people are moving very quickly in this big data space there's nobody else who can just bring all of these assets together from and and you know provide an integrated platform what use cases to Jim's point I don't you know I know you don't want to name names but can you name you how about some use cases that that these customers are using with blue like but use cases and they solving so you know I from from a use case a standpoint it is really like you know people are seeing performance which is you know 30 32 times faster than what they had seen when they were not using and in-memory columnstore you know so eight to twenty five thirty two times per men's gains is is you know something that is huge and is getting more and more people attracted to this so let's take an industry take financial services for example so the big the big ones in financial services are a risk people want to know you know are they credit risk yeah there's obviously marketing serving up serving up ads a fraud detection you would think is another one that in more real time are these these you know these will be the segments and of course you know retail where again you know there is like i was saying right that the number of transactions that are being handled is is growing phenomenally i gave one example which was around 2.5 million transactions per hour which was unheard of before and the information that has to be gleaned from it which is you know to leverage this for demand forecasting to leverage this for gaining insights in terms of giving the customers the right kind of coupons to make sure that those coupons are getting you know are being used so it was you know before the world used to be you get the coupons in your email in your mail then the world changed to that you get coupons after you've done the transaction now where we are seeing customers is that when a customer walks in the store that's where they get the coupons based on which i layer in so it's a combination of the transactional data the location data right and we are able to bring all of this together so so it's blue combined with you know what things like streams and big insights can do that makes the use cases even more powerful and unique so I like this new format of the crowd chatting emily is a one hour crowd chat where it's kind of like thought leaders just going to pounding away but this is more like reddit AMA but much better question coming in from grant case is one of the themes to you is one of the themes we've heard about in Makino was the lack of analytical talent what is going on to contribute more value for an organization skilling up the work for or implementing better software tools for knowledge workers so in terms so skills is definitely an issue that has been a been a challenge in the in the industry with and it got pretty compound with big data and the new technology is coming in from the standpoint of you know what we are doing for the data scientists which is you know the people who are leveraging data to to gain new insights to explore and and and discover what other attributes they should be adding to their predictive models to improve the accuracy of those models so there is there's a very rich set of tools which are used for exploration and discovery so we have which is both from you know Cognos has such such such capabilities we have such capabilities with our data Explorer absolutely basically tooling for the predictive on the modeling sister right now the efforts them on the modeling and for the predictive and descriptive analytics right I mean there's a lot of when you look at that Windows petabytes of data before people even get to predictive there's a lot of value to be gleaned from descriptive analytics and being able to do it at scale at petabytes of data was difficult before and and now that's possible with extra excellent visualization right so that it's it's taking things too that it the analytics is becoming interactive it's not just that you know you you you are able to do this in real time ask the questions get the right answers because the the models running on petabytes of data and the results coming from that is now possible so so interactive analytics is where this is going so another question is Jim was asking i was one of ibm's going around doing blue accelerator upgrades with all its existing clients loan origination is a no brainer upgrade I don't even know that was the kind of follow-up that I had asked is that new accounts is a new footprint or is it just sort of you it is spending existing it's it's boat it's boat what is the characteristic of a company that is successfully or characteristics of a company that is successfully leveraging data yeah so companies are thinking about now that you know their existing edw which is that enterprise data warehouse needs to be expanded so you know before if they were only dealing with warehouses which one handling just structure data they are augmenting that so this is from a technology standpoint right there augmenting that and building their logical data warehouse which takes care of not just the structure data but also semi-structured and unstructured data are bringing augmenting the warehouses with Hadoop based offerings like big insights with real-time offerings like streams so that from an IT standpoint they are ready to deal with all kinds of data and be able to analyze and gain information from all kinds of data now from the standpoint of you know how do you start the Big Data journey it the platform that at least you know we provide is a plug-and-play so there are different starting points for for businesses they may have started with warehouses they bring in a poly structured store with big inside / Hadoop they are building social profiles from social and public data which was not being done before matching that with the enterprise data which may be in CRM systems master data management systems inside the enterprise and which creates quadrants of comparisons and they are gaining more insights about the customer based on master data management based on social profiles that they are building so so this is one big trend that we are seeing you know to take this journey they have to you know take smaller smaller bites digests that get value out of it and you know eat it in chunks rather than try to you know eat the whole pie in one chunk so a lot of companies starting with exploration proof of concepts implementing certain use cases in four to six weeks getting value and then continuing to add more and more data sources and more and more applications so there are those who would say those existing edw so many people man some people would say they should be retired you would disagree with that no no I yeah I I think we very much need that experience and expertise businesses need that experience and expertise because it's not an either/or it's not that that goes away and there comes a different kind of a warehouse it's an evolution right but there's a tension there though wouldn't you say there's an organizational tension between the sort of newbies and the existing you know edw crowd i would say that maybe you know three years ago that was there was a little bit of that but there is i mean i talked to a lot of customers and there is i don't see that anymore so people are people are you know they they understand they know what's happening they are moving with the times and they know that this evolution is where the market is going where the business is going and where the technology you know they're going to be made obsolete if they don't embrace it right yeah yeah so so as we get on time I want to ask you a personal question what's going on with you these days with within IBM asli you're in a hot area you are at just in New York last week tell us what's going on in your life these days I mean things going well I mean what things you're looking at what are you paying attention to what's on your radar when you wake up and get to work before you get to work what's what are you thinking about what's the big picture so so obviously you know big data has been really fascinating right lots of lots of different kinds of applications in different industries so working with the customers in telco and healthcare banking financial sector has been very educational right so a lot of learning and that's very exciting and what's on my radar is we are obviously now seeing that we've done a lot of work in terms of helping customers develop and their Big Data Platform on-premise now we are seeing more and more a trend where people want to put this on the cloud so that's something that we have now a lot of I mean it's not like we haven't paid attention to the cloud but you know in the in the coming months you are going to see more from us are where you know how do we build cus how do we help customers build both private and and and public cloud offerings are and and you know where they can provide analytics as a service two different lines of business by setting up the clouds soso cloud is certainly on my mind software acquisition that was a hole in the portfolio and that filled it you guys got to drive that so so both software and then of course OpenStack right from an infrastructure standpoint for what's happening in the open source so we are you know leveraging both of those and like I said you'll hear more about that OpenStack is key as I say for you guys because you have you have street cred when it comes to open source I mean what you did in Linux and made a you know great business out of that so everybody will point it you know whether it's Oracle or IBM and HP say oh they just want to sell us our stack you've got to demonstrate and that you're open and OpenStack it's great way to do that and other initiatives as well so like I say that's a V excited about that yeah yeah okay I sure well thanks very much for coming on the cube it's always a pleasure to thank you see you yeah same here great having you back thank you very much okay we'll be right back live here inside the cube here and IV IBM information on demand hashtag IBM iod go to crouch at net / IBM iod and join the conversation where we're going to have a on the record crowd chat conversation with the folks out the who aren't here on-site or on-site Worth's we're here alive in Las Vegas I'm Java with Dave on to write back the q

>> Live from Las Vegas. It's the queue covering Adobe Summit twenty nineteen brought to you by Adobe. >> Hey, welcome back, everyone. Cube live coverage here in Las Vegas for Adobe sum of twenty nineteen. I'm John for which have Frick. Where he with a cube alumni that had job for three years. And you'LL Bhambri, Vice president of Platform Engineering at Adobe. Great to see you. Thanks for coming by. >> Thank you. >> Let's talk. Engineering. That was your line on the keynote. Great Kino today, by the way, super impressed with content. I'm washing that slides you're presenting, like were to cloud company. I'm failing my Amazon reinvent here. You guys built a really cool platform. Take us through. This was your mission. That's true. So take us through your journey. So how'd we get here? How did you get this beautiful platform? >> So, you know, we've been at it for a few years, and as you know, we've seen CEOs and see emos late. That their focus is to really deliver, you know, delightful experiences to their customers. And not just once, but throughout the journey off the customer. Right? Delight your customer. Every step of the way is what you'LL hear from Adobe from our customers. And we are really helping them to do that. And obviously, in order to do that, there is on, as you well know, that data is behind everything to do with experiences as well. There is a lot ofthe interaction of data and bringing it all together to really understand that holistic view of the customer is super important. And, you know, as you've been this realist, you know, the holistic view of the customer. It's not that you just ended once, and you forget about it, right? You have to build this in real time because the interactions that customers are having with brands are to wear through mobile devices to the apse that they're using off the those brands. And the businesses have to understand that whole journey off the customers and understand what their preferences are. Write what? You know what they like, what they don't like and be able to keeping like that context really during the journey. Whether they're coming to their Web site for the first time are they are repeat, customers be able to give them the right experience at every touch point. And that's where you need all of this data, which is a lot of data. So so you know, We've been on this big data journey on me personally, even, you know, for a long time. But the scale that I've seen here I had not seen before >> our IBM conscious when you weren't IBM prior from Hadoop World, you had your eye on this big data trend. Now, at Adobe, when you have really data coming in with apple cases out in the market place to put a platform together. Hard task. But I want to ask you specific question around that. Looking at the architecture slide you have and analytics cloud and add Cloud a marketing cloud in the commerce cloud. They all have Marcus that they have to address and be highly effective as almost appear placed in alone. But now, integrating across each other now with the journey that you guys were put together is difficult. I know that from a computer science background. How does how did you guys look at that? Architecturally, what were some of the guiding principles around building that? Because you don't want to compromise the capabilities of those functional elements. So you decompose and I get that. How did you put it all together? What was the key guiding principle around. >> Yeah, so that's a really good question, because I mean, Adobe has bean delivering applications, right? Like you said, whether it's around analytics, our marketing cloud or advertising. And now we obviously just acquired the commerce cloud on DH. When you look at the common stuff around all of this, it's data, right? Data being captured, two different channels, data that needs to be curated, you know, having a common data dictionary so that, you know, things mean the same on DH, even though they're captured two different channels. So gathering this data curating this data, organizing it for that holistic view of the customer organizing it so that you can do B I, and reporting on that data is all something that we pull together in the platform there. Now it becomes that whether it is you're doing analytics on this right, which could be a B I and the putting all your doing I and Melander is to do your next best action. All your targeting these customers with personalized content. You're doing it on that single version of the truth, which is the real time customer profile that powers all of these different clouds. So that it's not like when you do reporting you have one view ofthe a customer. But when you're trying to show them personalized content, half the view is lost because the data was siloed. So we've gone past all of that. There's no data silos now, right? >> Real time customer profile is literally being updated all the time. That's the key in great, exciting part about it is a curious >> kind of philosophically. And execution is like you've been in this space for a long time, and one of the jokes I left shares, you know, we used to make decisions based on a sampling of something that happened in the past. Now you know, we can make decisions based on all of the data that's happening now, but at the same time, your challenges, that source's heir changing all the time. The speed of the input is changing all the time, and the expected return on your reaction is shortening all the time. So from from just a date, a professional and I'm sure it's super exciting and super scary to move that paradigm shift to you got to deliver the right thing right now >> and you know, one of the key things field is that as all of this data was being gathered, right, obviously this data has to be gathered with these events are occurring. So if you look at glands, their customers are global. They are transacting browsing, whether it's on where mobile devices with that land globally around the world. That means data has to be collected from these globally distributed edges. And it has to be brought in processed in real time pending that profile. And as the data keeps coming, the profile is updated right? And and you can't have stained a dying, they're right, because otherwise, you know you are action ing based on something that happened five minutes ago. You know how we've seen that you buy something and you're still getting ads off that same product that you buy even a day or two days late? >> Already bought ten anymore. Ten. >> So that's because that bland has a stale profile off you, right? But if they had the real time customer profile, then there's no way that they would be delivering our action ing based on that stale information. So just like the data was being gathered from edges even when we have to deliver the experiences right. This is where edge computing comes into the picture, right? So we are also taking. So when you look at the whole architecture of the platform, yes, it's based on the cloud and you know it's a big data stack. It's completely assassin offering. But there is also a big edge computing part of the platform, which is where all the hard data is collected. Process and action and to your point, trade, like as we build, say, predictive models on Ex Best action on the data that's on the cloud. The scoring off the models has to happen on the edges where the events are crying. So this is a complicated engineering problem. But that's why I guess we love it. >> Big smile. So the data is critical. So about how adobes changed over the past few years because you guys did clown. I heard the nuance. I heard that keynote, you know, reading through the names of the lines. Is that it? It's hard to get data right at the beginning. Yeah, get cloud right now. You got data rights. Take us through that point because this is where I think the key to success is how to make that data work. Because if you're gonna have open AP eyes and open data integrity, that data right database, it's a time Siri's aircraft dated. A lot of different applications might choose certain technology. Yes, you have to deal with that. How, how important is the texture on that? >> So So that's why that's a great question that, you know, from a platform standpoint, our goal is that we have to be able to answer the questions with the right laden see or speed as well as relevancy, right? So when we talk really time, it's about it's Leighton sees. You know, when you talk to engineers, they only talk agency. But it's not that right. It's needn't see and relevancy. So in order to depending on. Like if it's more like B I r. Reporting kind off questions or queries, you need to organize the data certainly for, you know, single lookups off customers, right? You have to organize the data differently, and that's where our I'd be comes into the picture that how do we partition and organize this data to meet the needs ofthe both operational as well as the more, you know, like analytical kind ofthe workloads. So we support both and to your point, also that, you know, then we need a sequel database where there's no sequel database are a graft database. I mean, those are choices we make, but on top, they're providing FBI's. So we're abstracting all of that from the user. And you know how where we direct question, that's all R ight, but their applications are not going to break because they're writing to the FBI's. So as technologies advance underneath, we make those choices, but again so that they're getting the right agency and relevancy. >> So in the cloud game, we used to talk about this when you when you're on the Cuban way, an IBM the devil's movement was full tilt and they use the term infrastructure is code. Uh, so you're kind of getting out. I want to get your reaction to this Is that if applications and workloads are the use, cases are gonna determine the date of structures, data architecture and Leighton see relevance equation isn't. Then there's a new kind of infrastructures code emerging. Is that data as code? So, or maybe it's this should that workloads dictate what type of data diversity and Leighton see relevance is needed Or is that come from the network again? The question is, workloads are kind of in charge, I guess. What? I'm trying to get out. So >> I Yeah, I would say that, you know, as a platform, you have to support all of these workloads, right? So which means that from an architecture standpoint, we have to make sure that whether it's analytical, kindof a question or workload like B. I reporting whether it is, you know, more like an operational kind ofthe question around, You know that you want to just do a quick question around. You know, what did this customer by or what John's action happened? The underneath data structures and databases we have to pick the right ones so that way are able to support both >> the expectations, the expected yes, the expectations of the workload. >> It is. >> You're running commerce. Leighton Seon Relevance. Low latent. She's going to be in the milliseconds or >> gut ache >> and relevance. Gus, have a high bar there, too. Analytics query for a B. I tool might be, if every second so again, this is a huge Delta in terms of capabilities, and I think that will happen on the flies hard. Yes. How do you guys do that was sauce. >> Yeah, so that's That's the, you know, underlying technology that you know the way we are bending, that is, so that you can support both of those and wait with the customers were sticking to that. They wants equal access to the data they're getting. That's equal access now, depending on the kind ofthe queries, whether they, Paula's B I and reporting are more like transactional kind of things in nature. That's the that. Those are the right technical choices that we're making behind the scenes so that the user, those on our lab print right, because they can really focus on the insights that they're getting and really making decisions based on that inside and not get caught into how to bend all of these different pieces so that they can support both of these work clothes. The other thing is that you know a lot off the time that has Bean spent an I T. Has Bean to figure out all of this so that the CEO can support the line of business like the CMO now by, you know, Adobe taking. Get off this all this. It's heavy lifting. That idea had to do. I think that, you know it will be able to meet the requirements of the line of business much faster. And there's going to be, you know, the agility that is needed to support the business. I think that's really our goal in how we support the CEOs so that they don't worry about all this technology, all the data management, how to collect all this data from globally distributed edges. I mean, that's the partnership that we are, you know, bending with the CEOs so that we help them in their journey off, really helping their line of business deliver the best experiences >> on Jewel. Great to see you having so much fun, Toby. Thank you. What's it like there? Tell us, what's it like working in a job? You got a platform? Certainly. There's a lot of hard problems to solve. So you got that on the engineering side, tell us what the cultures like they're >> doing is a fantastic company. I mean, I just love every bit every every minute that I spend here is fantastic. It's, you know, great people open culture open to new ideas on DH. You know, I guess, uh, >> all the >> creative cloud you know has got the straight of it. Eve itches in fused in people. So it's just it's it's just being a blast and and, you know, people recognize them. Barton's off how data is so critical to delivering those delightful experiences, and it's very rewarding to just see how focused everybody is in the company to really help businesses delight their customers. So it's zygo >> system is great, but the developer ecosystem What's your reaction to that of the >> I mean Adobe Io is I don't know. I feel, you know, Yeah, So that's so if you think of all the creators that work with Adobe products and build their applications, I mean, the ecosystem is very rich. So combined creatives on the data and I t I mean >> so we should call the marketing native like cloud native accomplice of developers, developers. It's coming together >> on DH because >> cats living together I mean, this is >> called wait. Call them that experience maker's late. So we are really bringing experience makers, developers, data, scientists all together >> It's a whole new level for a >> whole new level. It's thanks >> for coming on. Sharing the insights. Cube coverage live here, and it will be some in Las Vegas. I'm John for your jefe. Rick, Stay with us. We're here for two days. We're in day one of wall to wall coverage at Adobe Summit. We write back.