>> 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.