>>Hey, welcome back everyone. Day three of eight of us Reinvent 2022. I'm John Farmer with Dave Volante, co-host the q Dave. 10 years for us, the leader in high tech coverage is our slogan. Now 10 years of reinvent day. We've been to every single one except with the original, which we would've come to if Amazon actually marketed the event, but they didn't. It's more of a customer event. This is day three. Is the machine learning ai keynote sws up there. A lot of announcements. We're gonna break this down. We got, we got Andy Thra here, vice President, prince Constellation Research. Andy, great to see you've been on the cube before one of our analysts bringing the, bringing the, the analysis, commentary to the keynote. This is your wheelhouse. Ai. What do you think about Swami up there? I mean, he's awesome. We love him. Big fan Oh yeah. Of of the Cuban we're fans of him, but he got 13 announcements. >>A lot. A lot, >>A lot. >>So, well some of them are, first of all, thanks for having me here and I'm glad to have both of you on the same show attacking me. I'm just kidding. But some of the announcement really sort of like a game changer announcements and some of them are like, meh, you know, just to plug in the holes what they have and a lot of golf claps. Yeah. Meeting today. And you could have also noticed that by, when he was making the announcements, you know, the, the, the clapping volume difference, you could say, which is better, right? But some of the announcements are, are really, really good. You know, particularly we talked about, one of that was Microsoft took that out of, you know, having the open AI in there, doing the large language models. And then they were going after that, you know, having the transformer available to them. And Amazon was a little bit weak in the area, so they couldn't, they don't have a large language model. So, you know, they, they are taking a different route saying that, you know what, I'll help you train the large language model by yourself, customized models. So I can provide the necessary instance. I can provide the instant volume, memory, the whole thing. Yeah. So you can train the model by yourself without depending on them kind >>Of thing. So Dave and Andy, I wanna get your thoughts cuz first of all, we've been following Amazon's deep bench on the, on the infrastructure pass. They've been doing a lot of machine learning and ai, a lot of data. It just seems that the sentiment is that there's other competitors doing a good job too. Like Google, Dave. And I've heard folks in the hallway, even here, ex Amazonians saying, Hey, they're train their models on Google than they bring up the SageMaker cuz it's better interface. So you got, Google's making a play for being that data cloud. Microsoft's obviously putting in a, a great kind of package to kind of make it turnkey. How do they really stand versus the competition guys? >>Good question. So they, you know, each have their own uniqueness and the we variation that take it to the field, right? So for example, if you were to look at it, Microsoft is known for as industry or later things that they are been going after, you know, industry verticals and whatnot. So that's one of the things I looked here, you know, they, they had this omic announcement, particularly towards that healthcare genomics space. That's a huge space for hpz related AIML applications. And they have put a lot of things in together in here in the SageMaker and in the, in their models saying that, you know, how do you, how do you use this transmit to do things like that? Like for example, drug discovery, for genomics analysis, for cancer treatment, the whole, right? That's a few volumes of data do. So they're going in that healthcare area. Google has taken a different route. I mean they want to make everything simple. All I have to do is I gotta call an api, give what I need and then get it done. But Amazon wants to go at a much deeper level saying that, you know what? I wanna provide everything you need. You can customize the whole thing for what you need. >>So to me, the big picture here is, and and Swami references, Hey, we are a data company. We started, he talked about books and how that informed them as to, you know, what books to place front and center. Here's the, here's the big picture. In my view, companies need to put data at the core of their business and they haven't, they've generally put humans at the core of their business and data. And now machine learning are at the, at the outside and the periphery. Amazon, Google, Microsoft, Facebook have put data at their core. So the question is how do incumbent companies, and you mentioned some Toyota Capital One, Bristol Myers Squibb, I don't know, are those data companies, you know, we'll see, but the challenge is most companies don't have the resources as you well know, Andy, to actually implement what Google and Facebook and others have. >>So how are they gonna do that? Well, they're gonna buy it, right? So are they gonna build it with tools that's kind of like you said the Amazon approach or are they gonna buy it from Microsoft and Google, I pulled some ETR data to say, okay, who are the top companies that are showing up in terms of spending? Who's spending with whom? AWS number one, Microsoft number two, Google number three, data bricks. Number four, just in terms of, you know, presence. And then it falls down DataRobot, Anaconda data icu, Oracle popped up actually cuz they're embedding a lot of AI into their products and, and of course IBM and then a lot of smaller companies. But do companies generally customers have the resources to do what it takes to implement AI into applications and into workflows? >>So a couple of things on that. One is when it comes to, I mean it's, it's no surprise that the, the top three or the hyperscalers, because they all want to bring their business to them to run the specific workloads on the next biggest workload. As you was saying, his keynote are two things. One is the A AIML workloads and the other one is the, the heavy unstructured workloads that he was talking about. 80%, 90% of the data that's coming off is unstructured. So how do you analyze that? Such as the geospatial data. He was talking about the volumes of data you need to analyze the, the neural deep neural net drug you ought to use, only hyperscale can do it, right? So that's no wonder all of them on top for the data, one of the things they announced, which not many people paid attention, there was a zero eight L that that they talked about. >>What that does is a little bit of a game changing moment in a sense that you don't have to, for example, if you were to train the data, data, if the data is distributed everywhere, if you have to bring them all together to integrate it, to do that, it's a lot of work to doing the dl. So by taking Amazon, Aurora, and then Rich combine them as zero or no ETL and then have Apaches Apaches Spark applications run on top of analytical applications, ML workloads. That's huge. So you don't have to move around the data, use the data where it is, >>I, I think you said it, they're basically filling holes, right? Yeah. They created this, you know, suite of tools, let's call it. You might say it's a mess. It's not a mess because it's, they're really powerful but they're not well integrated and now they're starting to take the seams as I say. >>Well yeah, it's a great point. And I would double down and say, look it, I think that boring is good. You know, we had that phase in Kubernetes hype cycle where it got boring and that was kind of like, boring is good. Boring means we're getting better, we're invisible. That's infrastructure that's in the weeds, that's in between the toes details. It's the stuff that, you know, people we have to get done. So, you know, you look at their 40 new data sources with data Wrangler 50, new app flow connectors, Redshift Auto Cog, this is boring. Good important shit Dave. The governance, you gotta get it and the governance is gonna be key. So, so to me, this may not jump off the page. Adam's keynote also felt a little bit of, we gotta get these gaps done in a good way. So I think that's a very positive sign. >>Now going back to the bigger picture, I think the real question is can there be another independent cloud data cloud? And that's the, to me, what I try to get at my story and you're breaking analysis kind of hit a home run on this, is there's interesting opportunity for an independent data cloud. Meaning something that isn't aws, that isn't, Google isn't one of the big three that could sit in. And so let me give you an example. I had a conversation last night with a bunch of ex Amazonian engineering teams that left the conversation was interesting, Dave. They were like talking, well data bricks and Snowflake are basically batch, okay, not transactional. And you look at Aerospike, I can see their booth here. Transactional data bases are hot right now. Streaming data is different. Confluence different than data bricks. Is data bricks good at hosting? >>No, Amazon's better. So you start to see these kinds of questions come up where, you know, data bricks is great, but maybe not good for this, that and the other thing. So you start to see the formation of swim lanes or visibility into where people might sit in the ecosystem, but what came out was transactional. Yep. And batch the relationship there and streaming real time and versus you know, the transactional data. So you're starting to see these new things emerge. Andy, what do you, what's your take on this? You're following this closely. This seems to be the alpha nerd conversation and it all points to who's gonna have the best data cloud, say data, super clouds, I call it. What's your take? >>Yes, data cloud is important as well. But also the computational that goes on top of it too, right? Because when, when the data is like unstructured data, it's that much of a huge data, it's going to be hard to do that with a low model, you know, compute power. But going back to your data point, the training of the AIML models required the batch data, right? That's when you need all the, the historical data to train your models. And then after that, when you do inference of it, that's where you need the streaming real time data that's available to you too. You can make an inference. One of the things, what, what they also announced, which is somewhat interesting, is you saw that they have like 700 different instances geared towards every single workload. And there are some of them very specifically run on the Amazon's new chip. The, the inference in two and theran tr one chips that basically not only has a specific instances but also is run on a high powered chip. And then if you have that data to support that, both the training as well as towards the inference, the efficiency, again, those numbers have to be proven. They claim that it could be anywhere between 40 to 60% faster. >>Well, so a couple things. You're definitely right. I mean Snowflake started out as a data warehouse that was simpler and it's not architected, you know, in and it's first wave to do real time inference, which is not now how, how could they, the other second point is snowflake's two or three years ahead when it comes to governance, data sharing. I mean, Amazon's doing what always does. It's copying, you know, it's customer driven. Cuz they probably walk into an account and they say, Hey look, what's Snowflake's doing for us? This stuff's kicking ass. And they go, oh, that's a good idea, let's do that too. You saw that with separating compute from storage, which is their tiering. You saw it today with extending data, sharing Redshift, data sharing. So how does Snowflake and data bricks approach this? They deal with ecosystem. They bring in ecosystem partners, they bring in open source tooling and that's how they compete. I think there's unquestionably an opportunity for a data cloud. >>Yeah, I think, I think the super cloud conversation and then, you know, sky Cloud with Berkeley Paper and other folks talking about this kind of pre, multi-cloud era. I mean that's what I would call us right now. We are, we're kind of in the pre era of multi-cloud, which by the way is not even yet defined. I think people use that term, Dave, to say, you know, some sort of magical thing that's happening. Yeah. People have multiple clouds. They got, they, they end up by default, not by design as Dell likes to say. Right? And they gotta deal with it. So it's more of they're inheriting multiple cloud environments. It's not necessarily what they want in the situation. So to me that is a big, big issue. >>Yeah, I mean, again, going back to your snowflake and data breaks announcements, they're a data company. So they, that's how they made their mark in the market saying that, you know, I do all those things, therefore you have, I had to have your data because it's a seamless data. And, and Amazon is catching up with that with a lot of that announcements they made, how far it's gonna get traction, you know, to change when I to say, >>Yeah, I mean to me, to me there's no doubt about Dave. I think, I think what Swamee is doing, if Amazon can get corner the market on out of the box ML and AI capabilities so that people can make it easier, that's gonna be the end of the day tell sign can they fill in the gaps. Again, boring is good competition. I don't know mean, mean I'm not following the competition. Andy, this is a real question mark for me. I don't know where they stand. Are they more comprehensive? Are they more deeper? Are they have deeper services? I mean, obviously shows to all the, the different, you know, capabilities. Where, where, where does Amazon stand? What's the process? >>So what, particularly when it comes to the models. So they're going at, at a different angle that, you know, I will help you create the models we talked about the zero and the whole data. We'll get the data sources in, we'll create the model. We'll move the, the whole model. We are talking about the ML ops teams here, right? And they have the whole functionality that, that they built ind over the year. So essentially they want to become the platform that I, when you come in, I'm the only platform you would use from the model training to deployment to inference, to model versioning to management, the old s and that's angle they're trying to take. So it's, it's a one source platform. >>What about this idea of technical debt? Adrian Carro was on yesterday. John, I know you talked to him as well. He said, look, Amazon's Legos, you wanna buy a toy for Christmas, you can go out and buy a toy or do you wanna build a, to, if you buy a toy in a couple years, you could break and what are you gonna do? You're gonna throw it out. But if you, if you, if part of your Lego needs to be extended, you extend it. So, you know, George Gilbert was saying, well, there's a lot of technical debt. Adrian was countering that. Does Amazon have technical debt or is that Lego blocks analogy the right one? >>Well, I talked to him about the debt and one of the things we talked about was what do you optimize for E two APIs or Kubernetes APIs? It depends on what team you're on. If you're on the runtime gene, you're gonna optimize for Kubernetes, but E two is the resources you want to use. So I think the idea of the 15 years of technical debt, I, I don't believe that. I think the APIs are still hardened. The issue that he brings up that I think is relevant is it's an end situation, not an or. You can have the bag of Legos, which is the primitives and build a durable application platform, monitor it, customize it, work with it, build it. It's harder, but the outcome is durability and sustainability. Building a toy, having a toy with those Legos glued together for you, you can get the play with, but it'll break over time. Then you gotta replace it. So there's gonna be a toy business and there's gonna be a Legos business. Make your own. >>So who, who are the toys in ai? >>Well, out of >>The box and who's outta Legos? >>The, so you asking about what what toys Amazon building >>Or, yeah, I mean Amazon clearly is Lego blocks. >>If people gonna have out the box, >>What about Google? What about Microsoft? Are they basically more, more building toys, more solutions? >>So Google is more of, you know, building solutions angle like, you know, I give you an API kind of thing. But, but if it comes to vertical industry solutions, Microsoft is, is is ahead, right? Because they have, they have had years of indu industry experience. I mean there are other smaller cloud are trying to do that too. IBM being an example, but you know, the, now they are starting to go after the specific industry use cases. They think that through, for example, you know the medical one we talked about, right? So they want to build the, the health lake, security health lake that they're trying to build, which will HIPPA and it'll provide all the, the European regulations, the whole line yard, and it'll help you, you know, personalize things as you need as well. For example, you know, if you go for a certain treatment, it could analyze you based on your genome profile saying that, you know, the treatment for this particular person has to be individualized this way, but doing that requires a anomalous power, right? So if you do applications like that, you could bring in a lot of the, whether healthcare, finance or what have you, and then easy for them to use. >>What's the biggest mistake customers make when it comes to machine intelligence, ai, machine learning, >>So many things, right? I could start out with even the, the model. Basically when you build a model, you, you should be able to figure out how long that model is effective. Because as good as creating a model and, and going to the business and doing things the right way, there are people that they leave the model much longer than it's needed. It's hurting your business more than it is, you know, it could be things like that. Or you are, you are not building a responsibly or later things. You are, you are having a bias and you model and are so many issues. I, I don't know if I can pinpoint one, but there are many, many issues. Responsible ai, ethical ai. All >>Right, well, we'll leave it there. You're watching the cube, the leader in high tech coverage here at J three at reinvent. I'm Jeff, Dave Ante. Andy joining us here for the critical analysis and breaking down the commentary. We'll be right back with more coverage after this short break.

>>text, you know, consumer opens up their iphone and says, oh my gosh, I love the technology behind my eyes. What's it been like being on the shark tank? You know, filming is fun, hang out, just fun and it's fun to be a celebrity at first your head gets really big and you get a good tables at restaurants who says texas has got a little possess more skin in the game today in charge of his destiny robert Hirschbeck, No stars. Here is CUBA alumni. Yeah, okay. >>Hi. I'm john Ferry, the co founder of silicon angle Media and co host of the cube. I've been in the tech business since I was 19 1st programming on many computers in a large enterprise and then worked at IBM and Hewlett Packard total of nine years in the enterprise brian's jobs from programming, Training, consulting and ultimately as an executive salesperson and then started my first company with 1997 and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology and I love covering you know, emerging technology as trained as a software developer and love business and I love the impact of software and technology to business to me creating technology that starts the company and creates value and jobs is probably the most rewarding things I've ever been involved in. And I bring that energy to the queue because the Cubans were all the ideas are and what the experts are, where the people are and I think what's most exciting about the cube is that we get to talk to people who are making things happen, entrepreneur ceo of companies, venture capitalists, people who are really on a day in and day out basis, building great companies and the technology business is just not a lot of real time live tv coverage and, and the cube is a non linear tv operation. We do everything that the T. V guys on cable don't do. We do longer interviews. We asked tougher questions, we ask sometimes some light questions. We talked about the person and what they feel about. It's not prompted and scripted. It's a conversation authentic And for shows that have the Cube coverage and makes the show buzz. That creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the cube and that is the result. Great content, great conversations and I'm so proud to be part of you with great team. Hi, I'm john ferrier. Thanks for watching the cube. >>Hello and welcome to the cube. We are here live on the ground in the expo floor of a live event. The AWS public sector summit. I'm john for your host of the cube. We're here for the next two days. Wall to wall coverage. I'm here with Sandy carter to kick off the event. Vice president partner as partners on AWS public sector. Great to see you Sandy, >>so great to see you john live and in person, right? >>I'm excited. I'm jumping out of my chair because I did a, I did a twitter periscope yesterday and said a live event and all the comments are, oh my God, an expo floor a real events. Congratulations. >>True. Yeah. We're so excited yesterday. We had our partner day and we sold out the event. It was rock them and pack them and we had to turn people away. So what a great experience. Right, >>Well, I'm excited. People are actually happy. We tried, we tried covering mobile world congress in Barcelona. Still, people were there, people felt good here at same vibe. People are excited to be in person. You get all your partners here. You guys have had had an amazing year. Congratulations. We did a couple awards show with you guys. But I think the big story is the amazon services for the partners. Public sector has been a real game changer. I mean we talked about it before, but again, it continues to happen. What's the update? >>Yeah, well we had, so there's lots of announcements. So let me start out with some really cool growth things because I know you're a big growth guy. So we announced here at the conference yesterday that our government competency program for partners is now the number one industry in AWS for are the competency. That's a huge deal. Government is growing so fast. We saw that during the pandemic, everybody was moving to the cloud and it's just affirmation with the government competency now taking that number one position across AWS. So not across public sector across AWS and then one of our fastest growing areas as well as health care. So we now have an A. T. O. Authority to operate for HIPPA and Hi trust and that's now our fastest growing area with 85% growth. So I love that new news about the growth that we're seeing in public sector and all the energy that's going into the cloud and beyond. >>You know, one of the things that we talked about before and another Cuban of you. But I want to get your reaction now current state of the art now in the moment the pandemic has highlighted the antiquated outdated systems and highlighted help inadequate. They are cloud. You guys have done an amazing job to stand up value quickly now we're in a hybrid world. So you've got hybrid automation ai driving a complete change and it's happening pretty quick. What's the new things that you guys are seeing that's emerging? Obviously a steady state of more growth. But what's the big success programs that you're seeing right now? >>Well, there's a few new programs that we're seeing that have really taken off. So one is called proserve ready. We announced yesterday that it's now G. A. And the U. S. And a media and why that's so important is that our proserve team a lot of times when they're doing contracts, they run out of resources and so they need to tap on the shoulder some partners to come and help them. And the customers told us that they wanted them to be pro served ready so to have that badge of honor if you would that they're using the same template, the same best practices that we use as well. And so we're seeing that as a big value creator for our partners, but also for our customers because now those partners are being trained by us and really helping to be mentored on the job training as they go. Very powerful program. >>Well, one of the things that really impressed by and I've talked to some of your MSP partners on the floor here as they walk by, they see the cube, they're all doing well. They're all happy. They got a spring in their step. And the thing is that this public private partnerships is a real trend we've been talking about for a while. More people in the public sector saying, hey, I want I need a commercial relationship, not the old school, you know, we're public. We have all these rules. There's more collaboration. Can you share your thoughts on how you see that evolving? Because now the partners in the public sector are partnering closer than ever before. >>Yeah, it's really um, I think it's really fascinating because a lot of our new partners are actually commercial partners that are now choosing to add a public sector practice with them. And I think a lot of that is because of these public and private partnerships. So let me give you an example space. So we were at the space symposium our first time ever for a W. S at the space symposium and what we found was there were partners, they're like orbital insight who's bringing data from satellites, There are public sector partner, but that data is being used for insurance companies being used for agriculture being used to impact environment. So I think a lot of those public private partnerships are strengthening as we go through Covid or have like getting alec of it. And we do see a lot of push in that area. >>Talk about health care because health care is again changing radically. We talked to customers all the time. They're like, they have a lot of legacy systems but they can't just throw them away. So cloud native aligns well with health care. >>It does. And in fact, you know, if you think about health care, most health care, they don't build solutions themselves, they depend on partners to build them. So they do the customer doesn't buy and the partner does the build. So it's a great and exciting area for our partners. We just launched a new program called the mission accelerator program. It's in beta and that program is really fascinating because our healthcare partners, our government partners and more now can use these accelerators that maybe isolate a common area like um digital analytics for health care and they can reuse those. So it's pretty, I think it's really exciting today as we think about the potential health care and beyond. >>You know, one of the challenge that I always thought you had that you guys do a good job on, I'd love to get your reaction to now is there's more and more people who want to partner with you than ever before. And sometimes it hasn't always been easy in the old days like to get fed ramp certified or even deal with public sector. If you were a commercial vendor, you guys have done a lot with accelerating certifications. Where are you on that spectrum now, what's next? What's the next wave of partner onboarding or what's the partner trends around the opportunities in public sector? >>Well, one of the new things that we announced, we have tested out in the U. S. You know, that's the amazon way, right, Andy's way, you tested your experiment. If it works, you roll it out, we have a concierge program now to help a lot of those new partners get inundated into public sector. And so it's basically, I'm gonna hold your hand just like at a hotel. I would go up and say, hey, can you direct me to the right restaurant or to the right museum, we do the same thing, we hand hold people through that process. Um, if you don't want to do that, we also have a new program called navigate which is built for brand new partners. And what that enables our partners to do is to kind of be guided through that process. So you are right. We have so many partners now who want to come and grow with us that it's really essential that we provide a great partner, experienced a how to on board. >>Yeah. And the A. P. M. Was the amazon partner network also has a lot of crossover. You see a lot a lot of that going on because the cloud, it's you can do both. >>Absolutely. And I think it's really, you know, we leverage all of the ap in programs that exist today. So for example, there was just a new program that was put out for a growth rebate and that was driven by the A. P. N. And we're leveraging and using that in public sector too. So there's a lot of prosecutes going on to make it easier for our partners to do business with us. >>So I have to ask you on a personal note, I know we've talked about before, your very comfortable the virtual now hybrid space. How's your team doing? How's the structure looks like, what are your goals, what are you excited about? >>Well, I think I have the greatest team ever. So of course I'm excited about our team and we are working in this new hybrid world. So it is a change for everybody uh the other day we had some people in the office and some people calling in virtually so how to manage that, right was really quite interesting. Our goals that we align our whole team around and we talked a little bit about this yesterday are around mission which are the solution areas migration, so getting everything to the cloud and then in the cloud, we talk about modernization, are you gonna use Ai Ml or I O T? And we actually just announced a new program around that to to help out IOT partners to really build and understand that data that's coming in from I O T I D C says that that idea that IOT data has increased by four times uh in the, during the covid period. So there's so many more partners who need help. >>There's a huge shift going on and you know, we always try to explain on the cube. Dave and I talked about a lot and it's re platform with the cloud, which is not just lift and shift you kind of move and then re platform then re factoring your business and there's a nuance there between re platform in which is great. Take advantage of cloud scale. But the re factoring allows for this unique advantage of these high level services. >>That's right >>and this is where people are winning. What's your reaction to that? >>Oh, I completely agree. I think this whole area of modernizing your application, like we have a lot of folks who are doing mainframe migrations and to your point if they just lift what they had in COBOL and they move it to a W S, there's really not a lot of value there, but when they rewrite the code, when they re factor the code, that's where we're seeing tremendous breakthrough momentum with our partner community, you know, Deloitte is one of our top partners with our mainframe migration. They have both our technology and our consulting um, mainframe migration competency there to one of the other things I think you would be interested in is in our session yesterday we just completed some research with r C T O s and we talked about the next mega trends that are coming around Web three dato. And I'm sure you've been hearing a lot about web www dot right? Yeah, >>0.04.0, it's all moving too fast. I mean it's moving >>fast. And so some of the things we talked to our partners about yesterday are like the metaverse that's coming. So you talked about health care yesterday electronic caregiver announced an entire application for virtual caregivers in the metaverse. We talked about Blockchain, you know, and the rise of Blockchain yesterday, we had a whole set of meetings, everybody was talking about Blockchain because now you've got El Salvador Panama Ukraine who have all adopted Bitcoin which is built on the Blockchain. So there are some really exciting things going on in technology and public sector. >>It's a societal shift and I think the confluence of tech user experience data, new, decentralized ways of changing society. You're in the middle of it. >>We are and our partners are in the middle of it and data data, data data, that's what I would say. Everybody is using data. You and I even talked about how you guys are using data. Data is really a hot topic and we we're really trying to help our partners figure out just how to migrate the data to the cloud but also to use that analytics and machine learning on it too. Well, >>thanks for sharing the data here on our opening segment. The insights we will be getting out of the Great Sandy. Great to see you got a couple more interviews with you. Thanks for coming on. I appreciate you And thanks for all your support. You guys are doing great. Your partners are happy you're on a great wave. Congratulations. Thank you, john appreciate more coverage from the queue here. Neither is public sector summit. We'll be right back. Mhm Yeah. >>Mhm. Mhm robert Herjavec. People obviously know you from shark tank

(upbeat music) >> Hello and welcome to this CUBE conversation here presenting the "AWS Startup Showcase: "New Breakthroughs in DevOps, Data Analytics "and Cloud Management Tools" featuring Opsani for the cloud management and migration track here today, I'm your host John Furrier. Today, we're joined by Patrick Conte, Chief Commercial Officer, Opsani. Thanks for coming on. Appreciate you coming on. Future of AI operations. >> Thanks, John. Great to be here. Appreciate being with you. >> So congratulations on all your success being showcased here as part of the Startups Showcase, future of AI operations. You've got the cloud scale happening. A lot of new transitions in this quote digital transformation as cloud scales goes next generation. DevOps revolution as Emily Freeman pointed out in her keynote. What's the problem statement that you guys are focused on? Obviously, AI involves a lot of automation. I can imagine there's a data problem in there somewhere. What's the core problem that you guys are focused on? >> Yeah, it's interesting because there are a lot of companies that focus on trying to help other companies optimize what they're doing in the cloud, whether it's cost or whether it's performance or something else. We felt very strongly that AI was the way to do that. I've got a slide prepared, and maybe we can take a quick look at that, and that'll talk about the three elements or dimensions of the problem. So we think about cloud services and the challenge of delivering cloud services. You've really got three things that customers are trying to solve for. They're trying to solve for performance, they're trying to solve for the best performance, and, ultimately, scalability. I mean, applications are growing really quickly especially in this current timeframe with cloud services and whatnot. They're trying to keep costs under control because certainly, it can get way out of control in the cloud since you don't own the infrastructure, and more importantly than anything else which is why it's at the bottom sort of at the foundation of all this, is they want their applications to be a really a good experience for their customers. So our customer's customer is actually who we're trying to solve this problem for. So what we've done is we've built a platform that uses AI and machine learning to optimize, meaning tune, all of the key parameters of a cloud application. So those are things like the CPU usage, the memory usage, the number of replicas in a Kubernetes or container environment, those kinds of things. It seems like it would be simple just to grab some values and plug 'em in, but it's not. It's actually the combination of them has to be right. Otherwise, you get delays or faults or other problems with the application. >> Andrew, if you can bring that slide back up for a second. I want to just ask one quick question on the problem statement. You got expenditures, performance, customer experience kind of on the sides there. Do you see this tip a certain way depending upon use cases? I mean, is there one thing that jumps out at you, Patrick, from your customer's customer's standpoint? Obviously, customer experience is the outcome. That's the app, whatever. That's whatever we got going on there. >> Sure. >> But is there patterns 'cause you can have good performance, but then budget overruns. Or all of them could be failing. Talk about this dynamic with this triangle. >> Well, without AI, without machine learning, you can solve for one of these, only one, right? So if you want to solve for performance like you said, your costs may overrun, and you're probably not going to have control of the customer experience. If you want to solve for one of the others, you're going to have to sacrifice the other two. With machine learning though, we can actually balance that, and it isn't a perfect balance, and the question you asked is really a great one. Sometimes, you want to over-correct on something. Sometimes, scalability is more important than cost, but what we're going to do because of our machine learning capability, we're going to always make sure that you're never spending more than you should spend, so we're always going to make sure that you have the best cost for whatever the performance and reliability factors that you you want to have are. >> Yeah, I can imagine. Some people leave services on. Happened to us one time. An intern left one of the services on, and like where did that bill come from? So kind of looked back, we had to kind of fix that. There's a ton of action, but I got to ask you, what are customers looking for with you guys? I mean, as they look at Opsani, what you guys are offering, what's different than what other people might be proposing with optimization solutions? >> Sure. Well, why don't we bring up the second slide, and this'll illustrate some of the differences, and we can talk through some of this stuff as well. So really, the area that we play in is called AIOps, and that's sort of a new area, if you will, over the last few years, and really what it means is applying intelligence to your cloud operations, and those cloud operations could be development operations, or they could be production operations. And what this slide is really representing is in the upper slide, that's sort of the way customers experience their DevOps model today. Somebody says we need an application or we need a feature, the developers pull down something from get. They hack an early version of it. They run through some tests. They size it whatever way they know that it won't fail, and then they throw it over to the SREs to try to tune it before they shove it out into production, but nobody really sizes it properly. It's not optimized, and so it's not tuned either. When it goes into production, it's just the first combination of settings that work. So what happens is undoubtedly, there's some type of a problem, a fault or a delay, or you push new code, or there's a change in traffic. Something happens, and then, you've got to figure out what the heck. So what happens then is you use your tools. First thing you do is you over-provision everything. That's what everybody does, they over-provision and try to soak up the problem. But that doesn't solve it because now, your costs are going crazy. You've got to go back and find out and try as best you can to get root cause. You go back to the tests, and you're trying to find something in the test phase that might be an indicator. Eventually your developers have to hack a hot fix, and the conveyor belt sort of keeps on going. We've tested this model on every single customer that we've spoken to, and they've all said this is what they experience on a day-to-day basis. Now, if we can go back to the side, let's talk about the second part which is what we do and what makes us different. So on the bottom of this slide, you'll see it's really a shift-left model. What we do is we plug in in the production phase, and as I mentioned earlier, what we're doing is we're tuning all those cloud parameters. We're tuning the CPU, the memory, the Replicas, all those kinds of things. We're tuning them all in concert, and we're doing it at machine speed, so that's how the customer gets the best performance, the best reliability at the best cost. That's the way we're able to achieve that is because we're iterating this thing in machine speed, but there's one other place where we plug in and we help the whole concept of AIOps and DevOps, and that is we can plug in in the test phase as well. And so if you think about it, the DevOps guy can actually not have to over-provision before he throws it over to the SREs. He can actually optimize and find the right size of the application before he sends it through to the SREs, and what this does is collapses the timeframe because it means the SREs don't have to hunt for a working set of parameters. They get one from the DevOps guys when they send it over, and this is how the future of AIOps is being really affected by optimization and what we call autonomous optimization which means that it's happening without humans having to press a button on it. >> John: Andrew, bring that slide back up. I want to just ask another question. Tuning in concert thing is very interesting to me. So how does that work? Are you telegraphing information to the developer from the autonomous workload tuning engine piece? I mean, how does the developer know the right knobs or where does it get that provisioning information? I see the performance lag. I see where you're solving that problem. >> Sure. >> How does that work? >> Yeah, so actually, if we go to the next slide, I'll show you exactly how it works. Okay, so this slide represents the architecture of a typical application environment that we would find ourselves in, and inside the dotted line is the customer's application namespace. That's where the app is. And so, it's got a bunch of pods. It's got a horizontal pod. It's got something for replication, probably an HPA. And so, what we do is we install inside that namespace two small instances. One is a tuning pod which some people call a canary, and that tuning pod joins the rest of the pods, but it's not part of the application. It's actually separate, but it gets the same traffic. We also install somebody we call Servo which is basically an action engine. What Servo does is Servo takes the metrics from whatever the metric system is is collecting all those different settings and whatnot from the working application. It could be something like Prometheus. It could be an Envoy Sidecar, or more likely, it's something like AppDynamics, or we can even collect metrics off of Nginx which is at the front of the service. We can plug into anywhere where those metrics are. We can pull the metrics forward. Once we see the metrics, we send them to our backend. The Opsani SaaS service is our machine learning backend. That's where all the magic happens, and what happens then is that service sees the settings, sends a recommendation to Servo, Servo sends it to the tuning pod, and we tune until we find optimal. And so, that iteration typically takes about 20 steps. It depends on how big the application is and whatnot, how fast those steps take. It could be anywhere from seconds to minutes to 10 to 20 minutes per step, but typically within about 20 steps, we can find optimal, and then we'll come back and we'll say, "Here's optimal, and do you want to "promote this to production," and the customer says, "Yes, I want to promote it to production "because I'm saving a lot of money or because I've gotten "better performance or better reliability." Then, all he has to do is press a button, and all that stuff gets sent right to the production pods, and all of those settings get put into production, and now he's now he's actually saving the money. So that's basically how it works. >> It's kind of like when I want to go to the beach, I look at the weather.com, I check the forecast, and I decide whether I want to go or not. You're getting the data, so you're getting a good look at the information, and then putting that into a policy standpoint. I get that, makes total sense. Can I ask you, if you don't mind, expanding on the performance and reliability and the cost advantage? You mentioned cost. How is that impacting? Give us an example of some performance impact, reliability, and cost impacts. >> Well, let's talk about what those things mean because like a lot of people might have different ideas about what they think those mean. So from a cost standpoint, we're talking about cloud spend ultimately, but it's represented by the settings themselves, so I'm not talking about what deal you cut with AWS or Azure or Google. I'm talking about whatever deal you cut, we're going to save you 30, 50, 70% off of that. So it doesn't really matter what cost you negotiated. What we're talking about is right-sizing the settings for CPU and memory, Replica. Could be Java. It could be garbage collection, time ratios, or heap sizes or things like that. Those are all the kinds of things that we can tune. The thing is most of those settings have an unlimited number of values, and this is why machine learning is important because, if you think about it, even if they only had eight settings or eight values per setting, now you're talking about literally billions of combinations. So to find optimal, you've got to have machine speed to be able to do it, and you have to iterate very, very quickly to make it happen. So that's basically the thing, and that's really one of the things that makes us different from anybody else, and if you put that last slide back up, the architecture slide, for just a second, there's a couple of key words at the bottom of it that I want to want to focus on, continuous. So continuous really means that we're on all the time. We're not plug us in one time, make a change, and then walk away. We're actually always measuring and adjusting, and the reason why this is important is in the modern DevOps world, your traffic level is going to change. You're going to push new code. Things are going to happen that are going to change the basic nature of the software, and you have to be able to tune for those changes. So continuous is very important. Second thing is autonomous. This is designed to take pressure off of the SREs. It's not designed to replace them, but to take the pressure off of them having to check pager all the time and run in and make adjustments, or try to divine or find an adjustment that might be very, very difficult for them to do so. So we're doing it for them, and that scale means that we can solve this for, let's say, one big monolithic application, or we can solve it for literally hundreds of applications and thousands of microservices that make up those applications and tune them all at the same time. So the same platform can be used for all of those. You originally asked about the parameters and the settings. Did I answer the question there? >> You totally did. I mean, the tuning in concert. You mentioned early as a key point. I mean, you're basically tuning the engine. It's not so much negotiating a purchase SaaS discount. It's essentially cost overruns by the engine, either over burning or heating or whatever you want to call it. I mean, basically inefficiency. You're tuning the core engine. >> Exactly so. So the cost thing is I mentioned is due to right-sizing the settings and the number of Replicas. The performance is typically measured via latency, and the reliability is typically measured via error rates. And there's some other measures as well. We have a whole list of them that are in the application itself, but those are the kinds of things that we look for as results. When we do our tuning, we look for reducing error rates, or we look for holding error rates at zero, for example, even if we improve the performance or we improve the cost. So we're looking for the best result, the best combination result, and then a customer can decide if they want to do so to actually over-correct on something. We have the whole concept of guard rail, so if performance is the most important thing, or maybe some customers, cost is the most important thing, they can actually say, "Well, give us the best cost, "and give us the best performance and the best reliability, "but at this cost," and we can then use that as a service-level objective and tune around it. >> Yeah, it reminds me back in the old days when you had filtering white lists of black lists of addresses that can go through, say, a firewall or a device. You have billions of combinations now with machine learning. It's essentially scaling the same concept to unbelievable. These guardrails are now in place, and that's super cool and I think really relevant call-out point, Patrick, to kind of highlight that. At this kind of scale, you need machine learning, you need the AI to essentially identify quickly the patterns or combinations that are actually happening so a human doesn't have to waste their time that can be filled by basically a bot at that point. >> So John, there's just one other thing I want to mention around this, and that is one of the things that makes us different from other companies that do optimization. Basically, every other company in the optimization space creates a static recommendation, basically their recommendation engines, and what you get out of that is, let's say it's a manifest of changes, and you hand that to the SREs, and they put it into effect. Well, the fact of the matter is is that the traffic could have changed then. It could have spiked up, or it could have dropped below normal. You could have introduced a new feature or some other code change, and at that point in time, you've already instituted these changes. They may be completely out of date. That's why the continuous nature of what we do is important and different. >> It's funny, even the language that we're using here: network, garbage collection. I mean, you're talking about tuning an engine, am operating system. You're talking about stuff that's moving up the stack to the application layer, hence this new kind of eliminating of these kind of siloed waterfall, as you pointed out in your second slide, is kind of one integrated kind of operating environment. So when you have that or think about the data coming in, and you have to think about the automation just like self-correcting, error-correcting, tuning, garbage collection. These are words that we've kind of kicking around, but at the end of the day, it's an operating system. >> Well in the old days of automobiles, which I remember cause I'm I'm an old guy, if you wanted to tune your engine, you would probably rebuild your carburetor and turn some dials to get the air-oxygen-gas mix right. You'd re-gap your spark plugs. You'd probably make sure your points were right. There'd be four or five key things that you would do. You couldn't do them at the same time unless you had a magic wand. So we're the magic wand that basically, or in modern world, we're sort of that thing you plug in that tunes everything at once within that engine which is all now electronically controlled. So that's the big differences as you think about what we used to do manually, and now, can be done with automation. It can be done much, much faster without humans having to get their fingernails greasy, let's say. >> And I think the dynamic versus static is an interesting point. I want to bring up the SRE which has become a role that's becoming very prominent in the DevOps kind of plus world that's happening. You're seeing this new revolution. The role of the SRE is not just to be there to hold down and do the manual configuration. They had a scale. They're a developer, too. So I think this notion of offloading the SRE from doing manual tasks is another big, important point. Can you just react to that and share more about why the SRE role is so important and why automating that away through when you guys have is important? >> The SRE role is becoming more and more important, just as you said, and the reason is because somebody has to get that application ready for production. The DevOps guys don't do it. That's not their job. Their job is to get the code finished and send it through, and the SREs then have to make sure that that code will work, so they have to find a set of settings that will actually work in production. Once they find that set of settings, the first one they find that works, they'll push it through. It's not optimized at that point in time because they don't have time to try to find optimal, and if you think about it, the difference between a machine learning backend and an army of SREs that work 24-by-seven, we're talking about being able to do the work of many, many SREs that never get tired, that never need to go play video games, to unstress or whatever. We're working all the time. We're always measuring, adjusting. A lot of the companies we talked to do a once-a-month adjustment on their software. So they put an application out, and then they send in their SREs once a month to try to tune the application, and maybe they're using some of these other tools, or maybe they're using just their smarts, but they'll do that once a month. Well, gosh, they've pushed code probably four times during the month, and they probably had a bunch of different spikes and drops in traffic and other things that have happened. So we just want to help them spend their time on making sure that the application is ready for production. Want to make sure that all the other parts of the application are where they should be, and let us worry about tuning CPU, memory, Replica, job instances, and things like that so that they can work on making sure that application gets out and that it can scale, which is really important for them, for their companies to make money is for the apps to scale. >> Well, that's a great insight, Patrick. You mentioned you have a lot of great customers, and certainly if you have your customer base are early adopters, pioneers, and grow big companies because they have DevOps. They know that they're seeing a DevOps engineer and an SRE. Some of the other enterprises that are transforming think the DevOps engineer is the SRE person 'cause they're having to get transformed. So you guys are at the high end and getting now the new enterprises as they come on board to cloud scale. You have a huge uptake in Kubernetes, starting to see the standardization of microservices. People are getting it, so I got to ask you can you give us some examples of your customers, how they're organized, some case studies, who uses you guys, and why they love you? >> Sure. Well, let's bring up the next slide. We've got some customer examples here, and your viewers, our viewers, can probably figure out who these guys are. I can't tell them, but if they go on our website, they can sort of put two and two together, but the first one there is a major financial application SaaS provider, and in this particular case, they were having problems that they couldn't diagnose within the stack. Ultimately, they had to apply automation to it, and what we were able to do for them was give them a huge jump in reliability which was actually the biggest problem that they were having. We gave them 5,000 hours back a month in terms of the application. They were they're having pager duty alerts going off all the time. We actually gave them better performance. We gave them a 10% performance boost, and we dropped their cloud spend for that application by 72%. So in fact, it was an 80-plus % price performance or cost performance improvement that we gave them, and essentially, we helped them tune the entire stack. This was a hybrid environment, so this included VMs as well as more modern architecture. Today, I would say the overwhelming majority of our customers have moved off of the VMs and are in a containerized environment, and even more to the point, Kubernetes which we find just a very, very high percentage of our customers have moved to. So most of the work we're doing today with new customers is around that, and if we look at the second and third examples here, those are examples of that. In the second example, that's a company that develops websites. It's one of the big ones out in the marketplace that, let's say, if you were starting a new business and you wanted a website, they would develop that website for you. So their internal infrastructure is all brand new stuff. It's all Kubernetes, and what we were able to do for them is they were actually getting decent performance. We held their performance at their SLO. We achieved a 100% error-free scenario for them at runtime, and we dropped their cost by 80%. So for them, they needed us to hold-serve, if you will, on performance and reliability and get their costs under control because everything in that, that's a cloud native company. Everything there is cloud cost. So the interesting thing is it took us nine steps because nine of our iterations to actually get to optimal. So it was very, very quick, and there was no integration required. In the first case, we actually had to do a custom integration for an underlying platform that was used for CICD, but with the- >> John: Because of the hybrid, right? >> Patrick: Sorry? >> John: Because it was hybrid, right? >> Patrick: Yes, because it was hybrid, exactly. But within the second one, we just plugged right in, and we were able to tune the Kubernetes environment just as I showed in that architecture slide, and then the third one is one of the leading application performance monitoring companies on the market. They have a bunch of their own internal applications and those use a lot of cloud spend. They're actually running Kubernetes on top of VMs, but we don't have to worry about the VM layer. We just worry about the Kubernetes layer for them, and what we did for them was we gave them a 48% performance improvement in terms of latency and throughput. We dropped their error rates by 90% which is pretty substantial to say the least, and we gave them a 50% cost delta from where they had been. So this is the perfect example of actually being able to deliver on all three things which you can't always do. It has to be, sort of all applications are not created equal. This was one where we were able to actually deliver on all three of the key objectives. We were able to set them up in about 25 minutes from the time we got started, no extra integration, and needless to say, it was a big, happy moment for the developers to be able to go back to their bosses and say, "Hey, we have better performance, "better reliability. "Oh, by the way, we saved you half." >> So depending on the stack situation, you got VMs and Kubernetes on the one side, cloud-native, all Kubernetes, that's dream scenario obviously. Not many people like that. All the new stuff's going cloud-native, so that's ideal, and then the mixed ones, Kubernetes, but no VMs, right? >> Yeah, exactly. So Kubernetes with no VMs, no problem. Kubernetes on top of VMs, no problem, but we don't manage the VMs. We don't manage the underlay at all, in fact. And the other thing is we don't have to go back to the slide, but I think everybody will remember the slide that had the architecture, and on one side was our cloud instance. The only data that's going between the application and our cloud instance are the settings, so there's never any data. There's never any customer data, nothing for PCI, nothing for HIPPA, nothing for GDPR or any of those things. So no personal data, no health data. Nothing is passing back and forth. Just the settings of the containers. >> Patrick, while I got you here 'cause you're such a great, insightful guest, thank you for coming on and showcasing your company. Kubernetes real quick. How prevalent is this mainstream trend is because you're seeing such great examples of performance improvements. SLAs being met, SLOs being met. How real is Kubernetes for the mainstream enterprise as they're starting to use containers to tip their legacy and get into the cloud-native and certainly hybrid and soon to be multi-cloud environment? >> Yeah, I would not say it's dominant yet. Of container environments, I would say it's dominant now, but for all environments, it's not. I think the larger legacy companies are still going through that digital transformation, and so what we do is we catch them at that transformation point, and we can help them develop because as we remember from the AIOps slide, we can plug in at that test level and help them sort of pre-optimize as they're coming through. So we can actually help them be more efficient as they're transforming. The other side of it is the cloud-native companies. So you've got the legacy companies, brick and mortar, who are desperately trying to move to digitization. Then, you've got the ones that are born in the cloud. Most of them aren't on VMs at all. Most of them are on containers right from the get-go, but you do have some in the middle who have started to make a transition, and what they've done is they've taken their native VM environment and they've put Kubernetes on top of it so that way, they don't have to scuttle everything underneath it. >> Great. >> So I would say it's mixed at this point. >> Great business model, helping customers today, and being a bridge to the future. Real quick, what licensing models, how to buy, promotions you have for Amazon Web Services customers? How do people get involved? How do you guys charge? >> The product is licensed as a service, and the typical service is an annual. We license it by application, so let's just say you have an application, and it has 10 microservices. That would be a standard application. We'd have an annual cost for optimizing that application over the course of the year. We have a large application pack, if you will, for let's say applications of 20 services, something like that, and then we also have a platform, what we call Opsani platform, and that is for environments where the customer might have hundreds of applications and-or thousands of services, and we can plug into their deployment platform, something like a harness or Spinnaker or Jenkins or something like that, or we can plug into their their cloud Kubernetes orchestrator, and then we can actually discover the apps and optimize them. So we've got environments for both single apps and for many, many apps, and with the same platform. And yes, thanks for reminding me. We do have a promotion for for our AWS viewers. If you reference this presentation, and you look at the URL there which is opsani.com/awsstartupshowcase, can't forget that, you will, number one, get a free trial of our software. If you optimize one of your own applications, we're going to give you an Oculus set of goggles, the augmented reality goggles. And we have one other promotion for your viewers and for our joint customers here, and that is if you buy an annual license, you're going to get actually 15 months. So that's what we're putting on the table. It's actually a pretty good deal. The Oculus isn't contingent. That's a promotion. It's contingent on you actually optimizing one of your own services. So it's not a synthetic app. It's got to be one of your own apps, but that's what we've got on the table here, and I think it's a pretty good deal, and I hope your guys take us up on it. >> All right, great. Get Oculus Rift for optimizing one of your apps and 15 months for the price of 12. Patrick, thank you for coming on and sharing the future of AIOps with you guys. Great product, bridge to the future, solving a lot of problems. A lot of use cases there. Congratulations on your success. Thanks for coming on. >> Thank you so much. This has been excellent, and I really appreciate it. >> Hey, thanks for sharing. I'm John Furrier, your host with theCUBE. Thanks for watching. (upbeat music)

>>Yeah, yeah. Welcome back to the third session in our building, A vibrant data ecosystem track. This session is unleash the power of your cloud data warehouse. So what comes after you've moved your data to the cloud in this session will explore White Enterprise Analytics is finally ready for the cloud, and we'll discuss how you can consume Enterprise Analytics in the very same way he would cloud services. We'll also explore where analytics meets cloud and see firsthand how thought spot is open for everyone. Let's get going. I'm happy to say we'll be hearing from two folks from thought spot today, Michael said Cassie, VP of strategic partnerships, and Vika Valentina, senior product marketing manager. And I'm very excited to welcome from our partner at AWS Gal Bar MIA, product engineering manager with Red Shift. We'll also be sharing a live demo of thought spot for BTC Marketing Analytics directly on Red Shift data. Gal, please kick us off. >>Thank you, Military. And thanks. The talks about team and everyone attending today for joining us. When we talk about data driven organizations, we hear that 85% of businesses want to be data driven. However, on Lee. 37% have been successful in We ask ourselves, Why is that and believe it or not, Ah, lot of customers tell us that they struggled with live in defining what being data driven it even means, and in particular aligning that definition between the business and the technology stakeholders. Let's talk a little bit. Let's look at our own definition. A data driven organization is an organization that harnesses data is an asset. The drive sustained innovation and create actionable insights. The super charge, the experience of their customers so they demand more. Let's focus on a few things here. One is data is an asset. Data is very much like a product needs to evolve sustained innovation. It's not just innovation innovation, it's sustained. We need to continuously innovate when it comes to data actionable insights. It's not just interesting insights these air actionable that the business can take and act upon, and obviously the actual experience we. Whether whether the customers are internal or external, we want them to request Mawr insights and as such, drive mawr innovation, and we call this the for the flywheel. We use the flywheel metaphor here where we created that data set. Okay, Our first product. Any focused on a specific use case? We build an initial NDP around that we provided with that with our customers, internal or external. They provide feedback, the request, more features. They want mawr insights that enables us to learn bringing more data and reach that actual data. And again we create MAWR insights. And as the flywheel spins faster, we improve on operational efficiencies, supporting greater data richness, and we reduce the cost of experimentation and legacy environments were never built for this kind of agility. In many cases, customers have struggled to keep momentum in their fleet, flywheel in particular around operational efficiency and experimentation. This is where Richie fits in and helps customer make the transition to a true data driven organization. Red Shift is the most widely used data warehouse with tens of thousands of customers. It allows you to analyze all your data. It is the only cloud data warehouse that sits, allows you to analyze data that sits in your data lake on Amazon, a street with no loading duplication or CTL required. It is also allows you to scale with the business with its hybrid architectures it also accelerates performance. It's a shared storage that provides the ability to scale toe unlimited concurrency. While the UN instant storage provides low late and say access to data it also provides three. Key asks that customers consistently tell us that matter the most when it comes to cost. One is usage based pricing Instead of license based pricing. Great value as you scale your data warehouse using, for example, reserved instances they can save up to 75% compared to on the mind demand prices. And as your data grows, infrequently accessed data can be stored. Cost effectively in S three encouraged through Amazon spectrum, and the third aspect is predictable. Month to month spend with no hitting charges and surprises. Unlike and unlike other cloud data warehouses, where you need premium versions for additional enterprise capabilities. Wretched spicing include building security compression and data transfer. >>Great Thanks. Scout um, eso. As you can see, everybody wins with the cloud data warehouses. Um, there's this evolution of movement of users and data and organizations to get value with these cloud data warehouses. And the key is the data has to be accessible by the users, and this data and the ability to make business decisions on the data. It ranges from users on the front line all the way up to the boardroom. So while we've seen this evolution to the Cloud Data Warehouse, as you can see from the statistic from Forrester, we're still struggling with how much of that data actually gets used for analytics. And so what is holding us back? One of the main reasons is old technology really trying to work with today's modern cloud data warehouses? They weren't built for it. So you run into issues of trying to do data replication, getting the data out of the cloud data warehouse. You can do analysis and then maintaining these middle layers of data so that you can access it quickly and get the answers you need. Another issue that's holding us back is this idea that you have to have your data in perfect shape with the perfect pipeline based on the exact dashboard unique. Um, this isn't true. Now, with Cloud data warehouse and the speed of important business data getting into those cloud data warehouses, you need a solution that allows you to access it right away without having everything to be perfect from the start, and I think this is a great opportunity for GAL and I have a little further discussion on what we're seeing in the marketplace. Um, one of the primary ones is like, What are the limiting factors, your Siegel of legacy technologies in the market when it comes to this cloud transformation we're talking about >>here? It's a great question, Michael and the variety of aspect when it comes to legacy, the other warehouses that are slowing down innovation for companies and businesses. I'll focus on 21 is performance right? We want faster insights. Companies want the ability to analyze MAWR data faster. And when it comes to on prem or legacy data warehouses, that's hard to achieve because the second aspect comes into display, which is the lack of flexibility, right. If you want to increase your capacity of your warehouse, you need to ensure request someone needs to go and bring an actual machine and install it and expand your data warehouse. When it comes to the cloud, it's literally a click of a button, which allows you to increase the capacity of your data warehouse and enable your internal and external users to perform analytics at scale and much faster. >>It falls right into the explanation you provided there, right as the speed of the data warehouses and the data gets faster and faster as it scales, older solutions aren't built toe leverage that, um, you know, they're either they're having to make technical, you know, technical cuts there, either looking at smaller amounts of data so that they can get to the data quicker. Um, or it's taking longer to get to the data when the data warehouse is ready, when it could just be live career to get the answers you need. And that's definitely an issue that we're seeing in the marketplace. I think the other one that you're looking at is things like governance, lineage, regulatory requirements. How is the cloud you know, making it easier? >>That's That's again an area where I think the cloud shines. Because AWS AWS scale allows significantly more investment in securing security policies and compliance, it allows customers. So, for example, Amazon redshift comes by default with suck 1 to 3 p. C. I. Aiso fared rampant HIPPA compliance, all of them out of the box and at our scale. We have the capacity to implement those by default for all of our customers and allow them to focus. Their very expensive, valuable ICTY resource is on actual applications that differentiate their business and transform the customer experience. >>That's a great point, gal. So we've talked about the, you know, limiting factors. Technology wise, we've mentioned things like governance. But what about the cultural aspect? Right? So what do you see? What do you see in team struggling in meeting? You know, their cloud data warehouse strategy today. >>And and that's true. One of the biggest challenges for large large organizations when they moved to the cloud is not about the technology. It's about people, process and culture, and we see differences between organizations that talk about moving to the cloud and ones that actually do it. And first of all, you wanna have senior leadership, drive and be aligned and committed to making the move to the cloud. But it's not just that you want. We see organizations sometimes Carol get paralyzed. If they can't figure out how to move each and every last work clothes, there's no need to boil the ocean, so we often work with organizations to find that iterative motion that relative process off identifying the use cases are date identifying workloads in migrating them one at a time and and through that allowed organization to grow its knowledge from a cloud perspective as well as adopt its tooling and learn about the new capabilities. >>And from an analytics perspective, we see the same right. You don't need a pixel perfect dashboard every single time to get value from your data. You don't need to wait until the data warehouse is perfect or the pipeline to the data warehouse is perfect. With today's technology, you should be able to look at the data in your cloud data warehouse immediately and get value from it. And that's the you know, that's that change that we're pushing and starting to see today. Thanks. God, that was That was really interesting. Um, you know, as we look through that, you know, this transformation we're seeing in analytics, um, isn't really that old? 20 years ago, data warehouses were primarily on Prem and the applications the B I tools used for analytics around them were on premise well, and so you saw things like applications like Salesforce. That live in the cloud. You start having to pull data from the cloud on Prem in order to do analytics with it. Um, you know, then we saw the shift about 10 years ago in the explosion of Cloud Data Warehouse Because of their scale, cost reduced, reduce shin reduction and speed. You know, we're seeing cloud data. Warehouses like Amazon Red Shift really take place, take hold of the marketplace and are the predominant ways of storing data moving forward. What we haven't seen is the B I tools catch up. And so when you have this new cloud data warehouse technology, you really need tools that were custom built for it to take advantage of it, to be able to query the cloud data warehouse directly and get results very quickly without having to worry about creating, you know, a middle layer of data or pipelines in order to manage it. And, you know, one company captures that really Well, um, chick fil A. I'm sure everybody has heard of is one of the largest food chains in America. And, you know, they made a huge investment in red shift and one of the purposes of that investment is they wanted to get access to the data mawr quickly, and they really wanted to give their business users, um, the ability to do some ad hoc analysis on the data that they were capturing. They found that with their older tools, the problems that they were finding was that all the data when they're trying to do this analysis was staying at the analyst level. So somebody needed to create a dashboard in order to share that data with a user. And if the user's requirements changed, the analysts were starting to become burdened with requests for changes and the time it took to reflect those changes. So they wanted to move to fought spot with embrace to connect to Red Shift so they could start giving business users that capability. Query the database right away. And with this, um, they were able to find, you know, very common things in in the supply chain analysis around the ability to figure out what store should get, what product that was selling better. The other part was they didn't have to wait for the data to get settled into some sort of repository or second level database. They were able to query it quickly. And then with that, they're able to make changes right in the red shift database that were then reflected to customers and the business users right away. So what they found from this is by adopting thought spot, they were actually able to arm business users with the ability to make decisions very quickly. And they cleared up the backlog that they were having and the delay with their analysts. And they're also putting their analysts toe work on different projects where they could get better value from. So when you look at the way we work with a cloud data warehouse, um, you have to think of thoughts about embrace as the tool that access that layer. The perfect analytic partner for the Cloud Data Warehouse. We will do the live query for the business user. You don't need to know how to script and sequel, um Thio access, you know, red shift. You can type the question that you want the answer to and thought spot will take care of that query. We will do the indexing so that the results come back faster for you and we will also do the analysis on. This is one of the things I wanted to cover, which is our spot i. Q. This is new for our ability to use this with embrace and our partners at Red Shift is now. We can give you the ability to do auto analysis to look at things like leading indicators, trends and anomalies. So to put this in perspective amount imagine somebody was doing forecasting for you know Q three in the western region. And they looked at how their stores were doing. And they saw that, you know, one store was performing well, Spot like, you might be able to look at that analysis and see if there's a leading product that is underperforming based on perhaps the last few quarters of data. And bring that up to the business user for analysis right away. They don't need to have to figure that out. And, um, you know, slice and dice to find that issue on their own. And then finally, all the work you do in data management and governance in your cloud data warehouse gets reflected in the results in embrace right away. So I've done a lot of talking about embrace, and I could do more, but I think it would be far better toe. Have Vika actually show you how the product works, Vika. >>Thanks, Michael. We learned a lot today about the power of leveraging your red shift data and thought spot. But now let me show you how it works. The coronavirus pandemic has presented extraordinary challenges for many businesses, and some industries have fared better than others. One industry that seems to weather the storm pretty well actually is streaming media. So companies like Netflix and who Lou. And in this demo, we're going to be looking at data from B to C marketing efforts. First streaming media company in 2020 lately, we've been running campaigns for comedy, drama, kids and family and reality content. Each of our campaigns last four weeks, and they're staggered on a weekly basis. Therefore, we always have four campaigns running, and we can focus on one campaign launch per >>week, >>and today we'll be digging into how our campaigns are performing. We'll be looking at things like impressions, conversions and users demographic data. So let's go ahead and look at that data. We'll see what we can learn from what's happened this year so far, and how we can apply those learnings to future decision making. As you can already see on the thoughts about homepage, I've created a few pin boards that I use for reporting purposes. The homepage also includes what others on my team and I have been looking at most recently. Now, before we dive into a search, will first take a look at how to make a direct connection to the customer database and red shift to save time. I've already pre built the connection Red Shift, but I'll show you how easy it is to make that connection in just three steps. So first we give the connection name and we select our connection type and was on red Shift. Then we enter our red shift credentials, and finally, we select the tables that we want to use Great now ready to start searching. So let's start in this data to get a better idea of how our marketing efforts have been affected either positively or negatively by this really challenging situation. When we think of ad based online marketing campaigns, we think of impressions, clicks and conversions. Let's >>look at those >>on a daily basis for our purposes. So all this data is available to us in Thought spot, and we can easily you search to create a nice line chart like this that shows US trends over the last few months and based on experience. We understand that we're going to have more clicks than impressions and more impressions and conversions. If we started the chart for a minute, we could see that while impressions appear to be pretty steady over the course of the year, clicks and especially conversions both get a nice boost in mid to late March, right around the time that pandemic related policies were being implemented. So right off the bat, we found something interesting, and we can come back to this now. There are few metrics that we're gonna focus on as we analyze our marketing data. Our overall goal is obviously to drive conversions, meaning that we bring new users into our streaming service. And in order to get a visitor to sign up in the first place, we need them to get into our sign up page. A compelling campaign is going to generate clicks, so if someone is interested in our ad, they're more likely to click on it, so we'll search for Click through Rape 5% and we'll look this up by campaign name. Now even compare all the campaigns that we've launched this year to see which have been most effective and bring visitors star site. And I mentioned earlier that we have four different types of campaign content, each one aligned with one of our most popular genres. So by adding campaign content, yeah, >>and I >>just want to see the top 10. I could limit my church. Just these top 10 campaigns automatically sorted by click through rate and assigned a color for each category so we could see right away that comedy and drama each of three of the top 10 campaigns by click through rate reality is, too, including the top spot and kids and family makes one appearance as well. Without spot. We know that any non technical user can ask a question and get an answer. They can explore the answer and ask another question. When you get an answer that you want to share, keep an eye on moving forward, you pin the answer to pin board. So the BBC Marketing Campaign Statistics PIN board gives us a solid overview of our campaign related activities and metrics throughout 2020. The visuals here keep us up to date on click through rate and cost per click, but also another really important metrics that conversions or cost proposition. Now it's important to our business that we evaluate the effectiveness of our spending. Let's do another search. We're going to look at how many new customers were getting so conversions and the price cost per acquisition that we're spending to get each of these by the campaign contact category. So >>this is a >>really telling chart. We can basically see how much each new users costing us, based on the content that they see prior to signing up to the service. Drama and reality users are actually relatively expensive compared to those who joined based on comedy and kids and family content that they saw. And if all the genres kids and family is actually giving us the best bang for our marketing >>buck. >>And that's good news because the genres providing the best value are also providing the most customers. We mentioned earlier that we actually saw a sizable uptick in conversions as stay at home policies were implemented across much of the country. So we're gonna remove cost per acquisition, and we're gonna take a daily look how our campaign content has trended over the years so far. Eso By doing this now, we can see a comparison of the different genres daily. Some campaigns have been more successful than others. Obviously, for example, kids and family contact has always fared pretty well Azaz comedy. But as we moved into the stay at home area of the line chart, we really saw these two genres begin to separate from the rest. And even here in June, as some states started to reopen, we're seeing that they're still trending up, and we're also seeing reality start to catch up around that time. And while the first pin board that we looked at included all sorts of campaign metrics, this is another PIN board that we've created so solely to focus on conversions. So not only can we see which campaigns drug significant conversions, we could also dig into the demographics of new users, like which campaigns and what content brought users from different parts of the country or from different age groups. And all this is just a quick search away without spot search directly on a red shift. Data Mhm. All right, Thank you. And back to you, Michael. >>Great. Thanks, Vika. That was excellent. Um, so as you can see, you can very quickly go from zero to search with thought Spot, um, connected to any cloud data warehouse. And I think it's important to understand that we mentioned it before. Not everything has to be perfect. In your doubt, in your cloud data warehouse, um, you can use thought spot as your initial for your initial tool. It's for investigatory purposes, A Z you can see here with star, Gento, imax and anthem. And a lot of these cases we were looking at billions of rows of data within minutes. And as you as your data warehouse maturity grows, you can start to add more and more thoughts about users to leverage the data and get better analysis from it. So we hope that you've enjoyed what you see today and take the step to either do one of two things. We have a free trial of thoughts about cloud. If you go to the website that you see below and register, we can get you access the thought spots so you can start searching today. Another option, by contacting our team, is to do a zero to search workshop where 90 minutes will work with you to connect your data source and start to build some insights and exactly what you're trying to find for your business. Um thanks, everybody. I would especially like to thank golf from AWS for joining us on this today. We appreciate your participation, and I hope everybody enjoyed what they saw. I think we have a few questions now. >>Thank you, Vika, Gal and Michael. It's always exciting to see a live demo. I know that I'm one of those comedy numbers. We have just a few minutes left, but I would love to ask a couple of last questions Before we go. Michael will give you the first question. Do I need to have all of my data cleaned and ready in my cloud data warehouse before I begin with thought spot? >>That's a great question, Mallory. No, you don't. You can really start using thought spot for search right away and start getting analysis and start understanding the data through the automatic search analysis and the way that we query the data and we've seen customers do that. Chick fil a example that we talked about earlier is where they were able to use thoughts bought to notice an anomaly in the Cloud Data Warehouse linking between product and store. They were able to fix that very quickly. Then that gets reflected across all of the users because our product queries the Cloud Data Warehouse directly so you can get started right away without it having to be perfect. And >>that's awesome. And gal will leave a fun one for you. What can we look forward to from Amazon Red Shift next year? >>That's a great question. And you know, the team has been innovating extremely fast. We released more than 200 features in the last year and a half, and we continue innovating. Um, one thing that stands out is aqua, which is a innovative new technology. Um, in fact, lovely stands for Advanced Square Accelerator, and it allows customers to achieve performance that up to 10 times faster, uh, than what they've seen really outstanding and and the way we've achieved that is through a shift in paradigm in the actual technological implementation section. Uh, aqua is a new distributed and hardware accelerated processing layer, which effectively allows us to push down operations analytics operations like compression, encryption, filtering and aggregations to the storage there layer and allow the aqua nodes that are built with custom. AWS designed analytics processors to perform these operations faster than traditional soup use. And we no longer need to bring, you know, scan the data and bring it all the way to the computational notes were able to apply these these predicates filtering and encourage encryption and compression and aggregations at the storage level. And likewise is going to be available for every are a three, um, customer out of the box with no changes to come. So I apologize for being getting out a little bit, but this is really exciting. >>No, that's why we invited you. Call. Thank you on. Thank you. Also to Michael and Vika. That was excellent. We really appreciate it. For all of you tuning in at home. The final session of this track is coming up shortly. You aren't gonna want to miss it. We're gonna end strong, come back and hear directly from our customer a T mobile on how T Mobile is building a data driven organization with thought spot in which >>pro, It's >>up next, see you then.

>> From around the globe, it's theCube, with digital coverage of AWS re:Invent 2020. Special coverage sponsored by, AWS Worldwide Public Sector. >> Okay, welcome back to theCube's coverage, of re:Invent 2020 virtual. It's theCube virtual, I'm John Farrow your host, we're here celebrating, the special coverage of public sector with Sandy Carter, vice president of AWS Public Sector Partners. She heads up the partner group within Public Sector, now in multiple for about a year now. Right Sandy, or so? >> Right, you got it, John. >> About a year? Congratulations, welcome back to theCube, >> Thank you. >> for reason- >> Always a pleasure to be here and what an exciting re:Invent right? >> It's been exciting, we've got wall-to-wall coverage, multiple sets, a lot of actions, virtual it's three weeks, we're not in person we have to do it remote this year. So when real life comes back, we'll bring the Cube back. But I want to take a minute to step back, take a minute to explain your role for the folks that are new to theCube virtual and what you're doing over there at Public Sector. Take a moment to introduce yourself to the new viewers. >> Well, welcome. theCube is phenomenal, and of course we love our new virtual re:Invent as well, as John said, my name is Sandy Carter and I'm vice president with our public sector partners group. So what does that mean? That means I get to work with thousands of partners globally covering exciting verticals like, space and healthcare, education, state and local government, federal government, and more. And what I get to do is, to help our partners learn more about AWS so that they can help our customers really be successful in the marketplace. >> What has been the most, exciting thing for you in the job? >> Well, you know, I love, wow, I love everything about it, but I think one of the things I love the most, is how we in Public Sector, really make technology have a meaningful impact on the world. So John, I get to work with partners like Orbis which is a non-profit they're fighting preventable blindness. They're a partner of ours. They've got something called CyberSec AI which enables us to use machine learning over 20 different machine learning algorithms to detect common eye diseases in seconds. So, you know, that purpose for me is so important. We also work with a partner called Twist Inc it's hard to say, but it just does a phenomenal job with AWS IoT and helps make water pumps, smart pumps. So they are in 7,300 remote locations around the world helping us with clean water. So for me that's probably the most exciting and meaningful part of the job that I have today. >> And it's so impactful because you guys really knew Amazon's business model has always been about enablement from startups to now up and running Public Sector; entities, agencies, education, healthcare, again, and even in spaces, this IoT in space. But you've been on the 100 partner tour over a 100 days. What did you learn, what are you hearing from partners now? What's the messages that you're hearing? >> Well, first of all, it was so exciting. I had a 100 different partner meetings in a 100 days because John, just like you, I missed going around the world and meeting in person. So I said, well, if I can't meet in person I will do a virtual tour and I talked to partners, in 68 different countries. So a couple of things I heard, one is a lot of love for our map program and that's our migration acceleration program. We now have funding available for partners as they assess migration, we can mobilize it and as they migrate it. And you may or may not know, but we have over twice the number of migration competency partners doing business in Public Sector this year, than we did last year. The second thing we heard was that, partners really love our marketing programs. We had some really nice success this year showcasing value for our customers with cyber security. And I love that because security is so important. Andy Jassy always talks about how her customers really have that as priority zeros. So we were able to work with a couple of different areas that we were very proud at and I loved that the partners were too. We did some repeatable solutions with our consulting partners. And then I think the third big takeaway that I saw was just our partners love the AWS technology. I heard a lot about AI and ML. We offered this new program called The Rapid Adoption Assistance Program. It's going global in 2021, and so we help partners brainstorm and envision what they could do with it. And then of course, 5G. 5G is ushering in, kind of a new era of new demand. And we going to to do a PartnerCast on all about 5G for partners in the first quarter. >> Okay, I'm going to put you on the spot. What are the three most talked about programs that you heard? >> Oh, wow, let's see. The three most talked about programs that I heard about, the first one was, is something I'm really excited about. It's called a Think Big for Small Business. It really focuses in on diverse partner groups and types. What it does is it provides just a little bit of extra boost to our small and medium businesses to help them get some of the benefits of our AWS partner program. So companies like MFT they're based down in South Africa it's a husband and wife team that focus on that Black Economic Empowerment rating and they use the program to get some of the go to market capability. So that's number one. Let's see, you said three. Okay, so number two would be our ProServe ready pilot. This helps to accelerate our partner activation and enablement and provides partners a way to get badged on the ProServe best practices get trained up and does opportunity matching. And I think a lot of partners were kind of buzzing about that program and wanting to know more about it. And then ,last but not least, the one that I think of probably really has impact to time to compliance it's called ATO or Authority to Operate and what we do is we help our partners, both technology partners and consulting partners get support for compliance framework. So FedRAMP, of course, we have over 129 solutions right now that are FedRAMPed but we also added John, PCI for financial HIPPA for healthcare, for public safety, IRS 1075 for international GDPR and of course for defense, aisle four, five and six, and CMMC. That program is amazing because it cuts the time to market and have cuts across and have and really steps partners through all of our best practices. I think those are the top three. >> Yeah, I've been like a broken record for the folks that don't know all my interviews I've done with Public Sector over the years. The last one is interesting and I think that's a secret sauce that you guys have done, the compliance piece, being an entrepreneur and starting companies that first three steps in a cloud of dust momentum the flywheel to get going. It's always the hardest and getting the certification if you don't have the resources, it's time consuming. I think you guys really cracked the code on that. I really want to call that out 'cause that's I think really super valuable for the folks that pay attention to and of course sales enablement through the program. So great stuff. Now, given that's all cool, (hands claps) the question I have and I hear all the time is, okay, I'm involved I got a lot of pressure pandemic has forced me to rethink I don't have a lot of IT I don't have a big budget I always complaint but not anymore. Mandate is move fast, get built out, leverage the cloud. Okay, I want to get going. What's the best ways for me to grow with Public Sector? How do I do that if I'm a customer, I really want to... I won't say take a shortcut because there's probably no shortage. How do I throttle up? Quickly, what's your take on that? >> Well, John, first I want to give one star that came to us from a Twilio. They had interviewed a ton of companies and they found that there was more digital transformation since March since when the pandemic started to now than in the last five years. So that just blew me away. And I know all of our partners are looking to see how they can really grow based on that. So if you're a consulting partner, one of the things that we say to help you grow is we've already done some integrations and if you can take advantage of those that can speed up your time to market. So I know know this one, the VMware Cloud on AWS. what a powerful integration, it provides protection of skillsets to your customer, increases your time to market because now VMware, vSphere, VSAN is all on AWS. So it's the same user interface and it really helps to reduce costs. And there's another integration that I think really helps which is Amazon connect one of our fastest growing areas because it's a ML AI, breads solution to help with call centers. It's been integrated with Salesforce but the Service Cloud and the Sales Cloud. So how powerful is that this integrated customer workflow? So I think both of those are really interesting for our consulting partners. >> That's a great point. In fact, well, that's the big part of the story here at re:Invent. These three weeks has been the integration. Salesforce as you mentioned connect has been huge and partner- >> Huge >> so just just great success again, I've seen great momentum. People are seeing their jobs being saved, they're saving lives. People are pretty excited and it's certainly a lot of work you've done in healthcare and education two big areas of activity which is really hard corporation, really, really hard. So congratulations on that and great work. Great to see you, I going to ask you one final question. What's the big message for your customers watching as they prepare for 2021 real life is coming back vaccines on the horizon. We're hearing some good news a lot of great cloud help there. What's your message to send to 2021? >> 2021, for our partners for 2021, one, there is a tremendous growth ahead and tremendous value that our partners have added. And that's both on the mission side, which both Theresa and I discussed during our sessions as well as technology. So I think first messages is, there's lots of growth ahead and a lot of ways that we can add value. Second is, all of those programs and initiatives, there's so much help out there for partners. So look for how you could really accelerate using some of those areas on your customer journey as you're going along. And then finally, I just want John, everybody to know , that we love our partners and AWS is there to help you every step of the way. And if you need anything at all obviously reach out to your PDM or your account manager or you're always welcome to reach out to me. And my final message is just, thank you, through so many different things that have happened in 2020, our partners have come through amazingly with passion with value and just with persistence, never stopping. So thank you to all of our partners out there who've really added so much value to our customers. >> And Amazon is recognizing the leadership of partners in the work you're doing. Your leadership session was awesome for the folks who missed it, check it out on demand. Thank you very much, Sandy for coming on the sharing the update. >> Thank you, John, and great to see all your partners out there. >> Okay, this is theCube virtual covering AWS re:Invent 2020 virtual three weeks, wall-to-wall coverage. A lot of videos ,check out all the videos on demand the leadership sessions, theCube videos and of course the Public Sector video on demand. Micro-site with theCube. I'm John Furrier, thanks for watching. (upbeat music)

>>from around the globe. It's the queue with digital coverage of AWS Public sector online brought to you by Amazon Web services. Everyone welcome back to the Cube's virtual coverage of Amazon Web services. Public sector Summit Online Virtual I'm John Furrier, your host of the Cube here in our Palo Alto studios were quarantined with our crew here. We're talking to all the guests, getting all the content I'm excited of. Sandy Carter Cube alumni's also the VP vice president. Worldwide public sector partners and programs. Sandy. Great to see you virtually. You look >>great virtually too. It's great to see everybody virtually. >>I love the sign behind you. Powered by AWS. I'm excited to have you on, but I really wanted to get jump right in because this is really an important conversation. Public sector is seeing a lot of activity around what's going on with covert 19 especially with all the public services that are needed. And people are now remote workers, remote consumers, public service and still needs to be delivered just like business. So it's a really had a big impact of the entire world. We're all seeing it. We're feeling it's not just tech thing. How are you seeing your community respond? Your partners are responding to covert. 19. Can you share what's happening? >>Yes, John, I have to say, I am so incredibly proud of the partners that we support and how they've stepped up in this time. That has no blueprint, right? It's brand new for everybody, whether we're talking about virtual call centers. We had so many states that said they had people waiting for hours waiting for calls to be answered about Covance for Take. For instance, West Virginia, West Virginia had collars waiting for hours 77,000 calls a day. They worked with one of our partners, Smartronix, and they got this new solution a ream or remote virtual call center, up in 72 hours. 72 hours later, Average wait. Time was 60 seconds. Amazing job by Smartronix or one of our other partners, Elektronik Caregiver who's based out of New Mexico, where my husband's from a great partner who's been looking at, um, telemedicine, how they can help those at risk in hospitals and rehabs, even just at their homes. Or another startup that's a partner of ours called Hello, Alice, that integrated with our AI and ML to create a small business platform to help those small businesses get access to funding. Answer questions During this really hard time and the last example, I'll give you his Inter vision, one of our newest premier partners, who had a customer that came to them and said, Look, I need to get a remote work solution up workspaces identity manager help desk And they thought it would take months and Inter Vision was able to do it in week. So I am so proud and so thankful of our partners and what they've done to really impact the world, not just for their own profit, but for purpose helping out states, governments and citizens >>and congratulations. And it's well needed. People are feeling the pain. One area I want to get your thoughts on is the agencies we talked to the Department of Defense general manager earlier today. Um, all of the agencies in in public sector are shifting, and obviously, with the limitations, they got a shift to the remote workforce. They got to be faster. They got to be agile. I know they've been trying to, but they can't just wait any longer. They're forced to. How are your public sector partners helping the agencies? >>Yeah, this is another just terrific story. I cannot brag about our partners enough with our agency work. So if you looked at all of the agencies, kind of had a tight title wave of this digital transformation, things that we're gonna take them years ended up taking them weeks and months. So whether it's Kansas with the Department of Labor, they had 8800 and 77,000 calls a day. 21 staff couldn't do. It worked with our partners to get a call center up and going or in New Mexico again with Accenture, they used Amazon Connect, which is one of my new favorite products from Amazon. It's a call center that leverages machine learning and AI. They were able to work with the New Mexico Human Services and get that up and going in two days, Um, or even in Montana, a great story with Deloitte, where they built a custom chat box in seven days, custom chat box and seven days to answer questions about food and medicine and even how to get cash. If you needed to get cash, our partners really stepped up with the agencies, and they did so much compelling work so quickly. I think speed was such a great component here, John. The speed of deployment, the speed of help. You know, working 24 by seven to deliver these solutions. Our partners really did an amazing job. >>Yeah, and it's really hard with virtual. I got, I got I wish I was in person with everyone because coming to the public sector summits, one of my favorite events reinvent in public sector. Some of the two big shows, I really think encapsulate all the activity because it's virtual. People might miss some news. What else is going on in the world of public sector partners? You? Can you elaborate more on what's going on around the edges? What's on the bleeding? Cutting edge? What's the pioneer and what are some of the blocking and tackling that you're doing? Share some of the news. What else is going on? >>Yeah. Thank you, John. There's so much going on. First of all, we just introduced a new partner solution portal. So all of these code that 19 solutions are featured there. We will provide a URL for any customer looking for a great solution by our partners. We also really honed in and helped our partners during this time around. Said Ramp. And you know that fed ramp is so crucial. Security cybersecurity Incredibly essential. During this time I know you talked to my good friend Casey from Salesforce. They were able to achieve their fed ramp I and we offer a lot of help to our partners to help them to achieve not just fed ramp, but GDP are as well as HIPPA too. Some other news on migrations. We've got a competency around migrations. We've got some new funding for our partners around map and we're seeing our migration's really accelerate, you know, once these agencies, once he states see the power of the cloud, they're like, give me more, I want to put more and so we're seeing migrations accelerate. I know that you saw the Navy speak about what they're doing with s AP and as to another one of my favorite partners 72,000 users now running in his two on AWS. Six different commands pretty powerful. And I would say last but not least, is PTP our program transformation program for our partners, which really is like 100 and 10 day session to help the partners become a cloud business themselves. So they're kind of drinking their own champagne before they go out and help others. They become a cloud business. It's really powerful. This program has helped to generate twice the revenue of a typical a PM program. >>You mentioned the Navy always having interesting chat about that. Migration was less than 10 months. >>Yes, again. Speed, speed, speed, right, John. I mean, it's incredible >>years, two months, and the other thing that you probably find interesting and this is something that's kind of not talked about. But it's felt just the basic stuff, like getting paperwork in some of these processes, like you mentioned Fed Ramp. There's a lot of things that go on around public sector. You just got to get done. You got a slog through it, if you will. You guys have have responded well there, and this is the benefit of the cloud. Having the streamlined processes elaborate more on that, because I think that's important. Benefit not only just started in the critical infrastructure, like call centers and things of that nature, but getting business done. That's a big thing. >>Yeah, And I would say, you know, if you look at it, we helped over 20 states with their insurance processes. I mean, it seems like a minor thing, but a lot of these things were manual before, Um, we've helped many states with unemployment, you know, very critical at this time, taking a manual process and getting it into the cloud. There's so many of these that we can go on and on about How do you get medical supplies? One of our partners cohesive down in Latin America has been helping around some of the supply chain issues that that we deal with there some of the things that we take for granted when you're in person now that your virtual, you really need to think them through in the cloud. So again, you know, our partners responded with speed. They responded with heart to John one of the other things, you know, hashtag tech for good. They responded with heart as well as they were looking at these projects and ensuring that states and agencies and governments around the world could take care of their citizens, which is all of us. >>You know, existing. We've talked in the past. We've talked on camera and off camera around our shared passion around tech for good. I've been a big proponent of as well as us of right of other folks. But with the crisis, the word impact means something. And social impact is actually social impact. Getting your unemployment check or, you know, this this is highlights the critical nature of why these services exist. I think it's a real testament. I think people should step back and saying why we should never go back to the old antiquated ways because this is now the new reality. These services can be agile, they can be faster. It takes a crisis, unfortunately, and I guess that could be the silver lining in all this. So props to you guys on giving the partnership there with the partners >>and to the governments and states, John, who have now, like they moved rapidly, right? All these states, all these agencies, all these governments move quickly to digital transformation. Now they've gotten a taste of it, and they're like, give me more. And so the great thing to me is that this wasn't a one time event or one time crisis driven movement. Now that they see the power of it much like what you're saying with your business, they're doing more and and that's what I really applaud for all of them. And the way that they're transforming the business is now longer term. >>I'm optimistic, and I hope when we come out of this when everyone gets settled and they re imagine and reinvent, there's a growth strategy and expansion could be for positive change. So you've >>got >>stuff. We're all for that, and we'll be watching that reporting on it. I >>want to >>ask you something. I've heard that you guys will be soon expanding your public safety and disaster response partner. Competency. Can you tell me more about that? >>Yeah, So we announced the This is a hard one is disaster response in public safety competency at re invent for our consulting partners? And that went over amazingly well. I mean, take, for instance, Max are who is probably the best at believing delivering data both pre and post data to a disaster. They helped Noah, for instance, where data was taking 100 minutes to get that data down. Not good enough in a disaster. They were able to achieve a 58% faster download of data so you can do something with that Use that data to make good decisions. So these consulting partners have really embraced are our disaster recovery and public safety response competency. And now what we want to do is introduce this for our technology partners. So we're announcing the coming of this program for our technology partners. Now who is a technology partner? Well, think about an AI is the or a SAS provider these type of partners who have great solutions that target this particular area, think about public safety right now and how important that is, or even disaster response. You know, we have cove it, but right after that, we have all these hurricanes and earthquakes and other things that are happening around the world. Killer hornets. Um and so we've got some great technology partners that have solutions here, and we'll be welcoming them into this confidence. He fold as well. >>Well, this brings up something I've been commenting on. I want to get your reaction is because you know, when you have that flywheel pattern, infrastructures of service platforms of service and sass that build cloud when we've seen the benefits over a decade. Plus, when you bring the business model, you start to see the same thing. Some foundational things like infrastructure as service would be like compliance. Instant auditing that the Navy seeing, for instance, I heard earlier and then that platform pieces to allow these new workloads. So these new applications are going to be coming on. Creative surge of application developers, new kinds of workloads, new kinds of workforces and and work work flows. So you're gonna start to see these new APS. That means you guys will probably be inundated with new things. How do people get involved? Do they join a PN? What are some of the benefits? What should someone do? I want to be a partner of AWS because I see a solution. I create something that may be unique and specialize in niche. But it solves a really important problem. I want to bring it to Amazon. How do I do that? >>And we want you as a partner to John. Um, so yes. I mean, if you're a partner, the very first place to start is to join our A p m r Amazon Partner Network. If you're a startup or an I s d a distributor or reseller consulting partner, any of those that would be the first place to start, And then based on what you're interested in, you would then select the types of help that you might get. So, for example, if you're a start up, we helped start ups with credits because a lot of startups need free credits as they're starting their businesses or even technologies. So if you think about Hello, Alice, uh, you know, really using tagging for her small business site during Cove it we were able to provide some technology expertise to get her moving and grooving. Um, other great programs that we have out there are things like 80 0 the authority to operate. And this is really important, John, because a lot of our our customers require fed ramp and fed ramp is very costly and not only costly, but takes a lot of time so we can dramatically reduce your time to market with fed ramp really help you through with all those best practices. In fact, today we have 110 fed ramp solution that have gone through our 80 or authority to hire authority to operate process. And that's four X. Our top two competitors combined four x the number of partners that have gotten through because of the amount of time that is reduced through this process as well as the best practices that we bring. We've done a slim down version, so if you're a start up and you're interested in it like we partner with the Joshua down at Capital Factory and they've got the Army future command, we got a lot of startups. You want it? We've also got a slim down version for for them as well. >>It's been a >>very powerful program, >>and being in the cloud you can fast track and learn from others. This >>is the >>whole point of cloud. >>Absolutely, And learning from others is, you know, one of the great things that we love to do. In fact, until I we're going to do a big partner meeting, you know, here at the summit we'll have partners that participate in the virtual online summit. We're going to do a separate meeting just for our partners in July as well to share with them some of the things that are important to them around programs and some of these AP and benefits and some of the changes that we've made to help support them during the Cove it crisis. >>And I think you know the partners or the channel or how you look at it. They're adding value and a great partner for Amazon. For you guys, It's a great city. >>Yeah, I mean, are we could not. We at Amazon could not do the business We do without our partners. They bring their expertise, their best practices, the skills and the relationships they have, the contracts they bring to the table. So we're so grateful for the partners that we have in our public sector partner program. It's one of the reasons I loved my job. Every day I get to talk to a new partner on a new technology area that they're working on. It could be, you know, spatial computing, or AI, and they're helping not just move for a business, but they're helping on a purposeful mission project usually which are so powerful in today's world, especially with all the different crisis, is that we've seen, >>you know, One thing I want to get just share with you is that I talk to a lot of partners, certainly on the Cube and in person. One of the things that resonates with partners is not only the optimism of Amazon and programs you run, but it's enablement. You guys really enable the partners to be successful on your behalf and you on their behalf. But ultimately the customer and I think, and there's money to be made so lucrative and profitable, and they could impact change. So this enabling capability is really the magic. And so I want to ask you on your final question. Here in the talk is what's the vibe now? Because also, we know it's pretty depressing with Cove it, um and we're gonna get through this, but so there will be a day we get through. This will be growth and strategies around. It will never be the same. Certainly, I believe the hybrid world. What's >>the >>vibe inside the Amazon Web services public sector partner team, the community, the ecosystem? Could you just give some insight into how people are doing? And what's the vibe? >>Yeah, I would say the vibe is hopeful um, we all see the difference and the impact that we're making on a daily basis. And because of that, um, we continue to stretch forward and really move mountains for our customers to help them deliver better services. Um, you know, our partners are jumping in and all kinds of areas. First of all, for example, they are jumping in on doing hackathons to help with covet 19. So, John, you know, girls and tech. We've got our partners and us as AWS jumping into happy on different solutions for some of these challenges that are facing there. That's all about hope. I hope that we can make a difference. We are jumping in and assisting on remote work and unemployment, um, to provide hope to the teams and the community. So I would say, you know, it's tough for all. In fact, one of my friends describes, this is a crisis cake, not one level of a crisis, but multiple levels of the crisis. And I have never been with a with a more optimistic and positive team in my whole life, one who's willing to do what it takes. And when I see team, I mean not just my AWS partner team, which is the best of the world, but our world class partner team as well, who is willing to jump in there and do what it takes to help our customers. Even this weekend, I had a part of my partner team and my partners working to solve a problem for an agency that was, you know, um, critical. And they jumped in on the weekend to make that happen. So I would say, if I could say one word, I would say My partner's are hopeful they are. They're learning. They're curious. They're stepping out into new areas like connect and remote work and remote learning. And they're doing things that they never thought was possible based on what's happening today. >>Critical infrastructure, critical software, services and processes gotta be maintained and this opportunity. So I think it's, you know, heads down with hope and growth, always great to chat with you. And of course, we'll be following and covering your event next month. So looking forward to it, exciting times. Sandy Carter, Thank you for joining me today for coverage. >>Thank you, John. It's always a pleasure to be here on the Cube Thank you guys for watching as well. >>Sandy Carter, vice president, worldwide public sector partners in program. Distinguished Cube Alumni. A tough job, great job at same time. A lot of opportunities and hope. I'm John Furrow, your host of the Cube. You're watching our coverage. Cube Virtual of Amazon public sector Online summit. Thanks for watching. Yeah, yeah, yeah.

>> Narrator: From around the globe, it's theCUBE! Covering HPE Discover Virtual Experience. Brought to you by HPE. >> Welcome back, I'm Stu Miniman, and this is theCUBE's coverage of HPE Discover 2020 the Virtual Experience, really happy to welcome to the program, we have a returning guest. Tarkan Maner is the Chief Commercial Officer at Nutanix, in a new role since the last time we had him on the program, and joining him, we have Herman Brown, who's the CIO for the City of San Francisco's District Attorney. Gentlemen, thank you so much for joining us. >> Thank you for having me. >> So, Tarkan, help set the stage for us. As I mentioned, we know you, our network knows you, but, new to Nutanix in the last year, and talk to us a little bit about this HPE Nutanix partnership. >> Yeah, if you noticed, first of all, thank you for hosting us, great to be here. This is probably, who knows, my fiftieth CUBE I guess, over the past two decades, especially the last twenty years have been crazy for us, obviously in the industry, lots of movement, lots of change. So let me go into the context, that led to Nutanix joining the company, about six months ago in the capacity of Chief Commercial Officer, a hybrid role with some product aspects, business development global market, our cloud infrastructure digitalizes and some of the Corp Dev we're working on. In that context, obviously, HPE is a very, very important strategic partner to us. As you know, the companies, the two companies have been working together for a long time, but especially the last, I would say, six to twelve months, we have this phenomenal relationship around what I call "three focused areas" of our business. Around our digital infrastructure, upward converge infrastructure, a business on top of that, our solutions from data center to DevOps and to this stuff, services, it's three specific segments, we built this really interesting really strong relationship with HPE with some of our philosophies and HPE's platform, now obviously, working through a multicloud channel, who are our own Nutanix cloud, our own hosted cloud, in addition to it, our Telco and SSP partners using their cloud infrastructure as well as some of the hyperscaling work we're doing, with Azure, AWS in addition to our direct SalesForce and private cloud approach, HPE and Nutanix are working hand in hand in this multicloud so to speak operating model. So it's a new relationship in some ways, from a multicloud perspective, but if it has to grow in segments, we had a phenomenal quarter in the last three months, we just released our results, and HPE is growing for us. And we're given definitely a great suite of solutions to our customers with the typical usual, simple to deploy, simple to use mechanics customer delight on the HPE platform. So I'm sure we've got a whole lot more, but glad to be here also with Herman Brown, from the DA's office in San Francisco, my favorite city in the world, so glad to be here. Thank you, Stu, again, for hosting us. >> Great, thanks so much, Tarkan. You know, Herman, we're going to get into a lot of the technology pieces, you with your CIO hat on, you know, want to understand how cloud, how modern infrastructure, your applications are changing, but, give us a little bit about your personal background and really the purview that you cover in the city of San Francisco District Attorney's office. >> Yes, well, you know, I've been with the DA's office for just over 3 years, it'll be 4 years I guess in August of this year, and I come from twenty plus years of private sector experience, some government experience. And, you know, the city and county, the government is really no different than any other organization other than we're known to be a little bit slower to adopt the technologies, which is why I'm here. I want to help government become more efficient, more productive through the use of technology, and so I'm excited to be here and thank you, first and foremost, for having me on the show. I appreciate it. >> I love that you brought that up, because we've been doing theCUBE for just over a decade now, and in the early parts of that, it's like, right, okay, I'm talking to a local government, we understand, your budgets are tight, you're using older technology, you've got duct tape and baling wire to keep things going. The last few years, some of my favorite conversations have been in the public sector, because you talk about some of the tools that are out there, and don't need a huge capital investment to get started, I can modernize, so Herman, digital transformation, is that a term that you've brought from the private sector over to the public sector, or what kind of transformations are you going through and what is it that's I guess driving the need for transformation in your world? >> So yeah, I've been with the city and county of San Francisco for nine years, so I'd love to say that I brought digital transformation or at least the term with me, but I was actually here in the DA's office or in the city and county's employment when that terminology came out. Being the CIO for the San Francisco District Attorney's office, I mean, we're essentially a law firm. And law firms are historically just paper intensive organizations, right, you have court filings and rap sheets, all these physical documents that have to be physically ink signed and transferred from one attorney to another to the courts, and between police departments and sheriffs and so forth and so on. And we just looked at, what are we doing, how can we work more efficient, you know? As a lot of organizations, we're always finding ourselves to be understaffed for the amount of work that we have going on, the city and county of San Francisco, the DA's office, we see roughly 26,000 cases a year, we try about half of those cases per year. And we're a staff of 320 people. That includes everyone, the attorneys, the paralegals, finance folks, IT, investigators. And so it was like, we need to really embrace technology and be able to help transform this paper intensive processes into automated, digital forms and documents that can minimize the physical transferring of data, especially now, during Covid-19. >> Yeah, Herman, that transformation process is often multi-step, there's a lot of people, there's technology, and then there's the applications. It was at a Nutanix show that the comment I made is, well, let's modernize the platform, then you can modernize the applications on top of it. Tarkan, maybe, I'd love to hear just a little commentary from you, you've got a great perspective on this. That modernization effort, where your customers are, some of the levers that Nutanix is helping them along that journey. >> Yeah, so everything Herman said is very interesting, and obviously, a delight to my ears, because as a technologist in the industry for the past three decades, we're dealing with this, what I call, transformational waves, and you know, in the last ten years, the cloud transformation from the server to transition transformation now, increasingly, we're seeing this very fast migrations from the old school legacy data centers with legacy infrastructure and apps, basically are lifting and shifting these applications to a new cloud, so to speak, opened the model. The cloud to us, in a sense, it's not a destination, it's an open model, so if we see the customer's needs at the end of the day, just like Herman outlined, Herman is not trying to do cloud or digitization for the digital cloud's sake, he's trying to lead his team and the DA's office, with the most DAs by the way, in the nation, making sure that they can process data faster. They can achieve their goals, working especially in this post-pandemic world, and the entire change that are happening in our country, in a big way over the past few weeks, the events, and how our country is going to change for the future. So there's going to be a lot of work going to be happening in the government, this transformation or digitization, migration to the cloud, is going to be a big deal, so as a company, it very quickly we've seen this as a huge opportunity for our customers, as we're partnering with them in a multi cloud way. We still believe our server partners are super important in this context with HPE, but the cloud services around HPE Greenlake, the things we are doing with them, at the same time, working with HPE and some of our partners delivering our own Nutanix cloud services as well as some of the things we have been doing with some of Telco's and service providers, to give choice to our customers to consume the services we provide on-prem, through our old cloud services through a third party telephone service provider, or the choice of hyper scale into the U.S. As your Google, unlimited oracle. So in this context this partnership is hugely important, so there's a lot going on with HPE with Antonio, with our CEO, with Tarak, our CFO, with Tom Black, it's Sonali. The entire executive team are working very closely with them, and with Hyko in the fuel organization, our fuel organization, and we really cherish customers like the DA's office who are doing the transformation, who are leading the transformation, during this pandemic and during this massive change in our country and hopefully it's going to make a transformative change to our world in terms of obviously not only technology, but social change, so you see this as a transformative time frame for companies like us and HPE and partners like Herman and DA's office. >> Herman, please! >> Yeah, I was just going to say, and absolutely I agree with Tarkan, and the way that we're able to react so quickly to this pandemic is the fact that we've already have started this digital transformation, that we've already been looking at these cloud services, we've already started down this path, and so it's made the transition with this surging overnight change of the office nine to five, five days a week to you know, everyone is remote every day now, we couldn't do that without having these cloud services such as Nutanix and HPE partnership, to make that possible. >> Yeah, is there something specific you talk, the work from home initiative, did you have to scale something out, did you have to, you know, bring us inside that change that helped enable your workforce that you wouldn't have been able to do without this technology. >> Yeah, we absolutely had to scale out the workforce. I would say that before the beginning of this pandemic, we had roughly 15 people that probably had VPN access from outside the office, now you have to also understand that the DA's office is very unique in the form of the types of data that we handle and deal with, so I have HIPPA data, I have CJIS, which is criminal justice information, that's managed by DOJ, so there are certain systems that we normally would not be able to access from outside the office that we had to be able to access now remotely. And so it's taken some time to get us there to that point, but you know, having this environment that allowed us to scale up easily, start looking at digitizing this process and being able to have the storage and compute and processing power to be able to support that initiative is really what we're talking about, and that's what we've been doing. We've been quickly scaling, adding in additional storage but popping in drives and making this all possible in a very quickly and seamlessly process. >> Excellent. Maybe we've talked a little bit about the results and how you can move faster, you know, digital information all about leveraging your data and be able to react more quickly, so you know, the pandemic definitely has put services to the test and it sounds like they're doing well. Maybe step us back a little bit as to what led you to HPE and Nutanix, how you made that decision. >> Well, you know, we went through a trial, a period, proof of concept, we looked at Dell, we looked at HPE and Nutanix, we looked at a few different solutions, and it really boiled down to cost, and what we were getting, bang for the dollar. I think there are some other great solutions out there or good solutions out there but none of them came to the value that the partnership with HPE and Nutanix actually have to offer to us. You know, one of the things is that with this partnership is when there's a support issue, I call Nutanix, I'm not calling HPE, I'm not calling this, the other third party vendor, I'm not getting the runaround of "oh, that's not our problem, that's someone else's problem, you need to call the software team, you need to call the hardware team," no. It's one person that you know, we call, as I like to say, "one throat to choke." And fortunately, we haven't had to go that route, Nutanix has been an excellent partner for ours and they have been great to work with, and on the ball, and that's what I always talk about, success is not just the success of the organization, but the success of the individuals and the success of the partnership between organizations. And that's what I looked for is a business partner that wants to help me at my role at my organization be successful. >> Great. Herman, we talked about modernizing the environment, bring us inside the applications, if you would, what applications you're using, you know, are there new initiatives that you're doing from an application standpoint? >> Yeah, so we're running the same standard applications that most organizations are running, with DHCPISS, you know, I have some other systems that we run just because of the CIO, CICA hat that I also wear within the organization, I'm very security conscious about talking about those applications. But we run pretty much the same basic applications as most organizations do. Those specialized applications that we also operate on, we do see an improvement in performance, we do see the speediness of the access, the more stability and reliability of the solutions, and so we're very pleased with the performance that we're getting. >> Excellent. You also, you talked about the efficiency of what you're doing. I mentioned earlier that, public sector, you can get started, you know, for smaller chunks using things like Nutanix, but budget, obviously, still a concern, I'm sure, anything you're doing with the verbalization in the infrastructure that is helping you keep budget under control? >> Absolutely, I mean, the Nutanix environment is scalable, it allows us to be able to look at other solutions such as CDI, which we're talking about and looking at, potentially doing for staff members that don't have laptops that may need laptops or need remote access into the system. We also have that ability to scale up with just another leg, more storage, it makes it very easy to go with where you're looking at cost-saving measures, currently running BMWare on the back end, but looking to convert that over AHV, yes, in the future, that can also help us reduce those costs in the future as well. Especially at this point in time, where city and county is looking for department budget savings. >> Excellent. Tarkan, I guess this would be a good point for you to chime in on, you know, generally, AHV and any other commentary you've got regarding-- >> I was just trying to hold my words back, because the things that Herman is doing are so exciting in a way, you know, techies like myself still get really excited. Like Herman talked about we're not doing infrastructure for infrastructure's sake. At the end of the day, Herman and his office like many government offices both in the fed as in state or local, have to do more with less. Obviously in this post-pandemic world, you get even more efficient, more innovative, and get most output from our input. In that context, bringing storage, compute, networking, all integrated in a converged way, it's smart, it's not just adding them up, one plus one plus one equals three, but one plus one plus one equals less than one, in terms of cost, making it make sure it's infrastructures are simplified, easy to deploy, easy to use, that's why we keep an NPS score of 90, by the way, part of the reason, a little bit of shameless plug there for you. I don't know many companies who have an NPS 90 because we make infrastructure simple. So if you settle this, to Herman's point, all those applications he's managing and building and then obviously digitizing, and in some way, lifting a shifting and creating a new cloud digitized model, he want to make sure Herman and companies and organizations like the DA's office under leadership, with innovative CIOs like Herman, making sure they have choice. They can choose the prem model they want, on-prem, off-prem, hybrid, or multicloud, or in a government cloud fashion, and deliver these services. To give you an example, we talked about home as the extended enterprise. Our home office is now part of the office. I have to secure my home the way I secure my Nutanix headquarters because I'm now running my business from home. So in the past, there was a delineation between home and office. Now home is part of the extended office. The way I manage my trash, the way I manage my peer flows, applications, the network, latency, everything has to be dealt with in a very smart way. But even our paper trash in our office, we manage it carefully because of the IP, you know, people steal IP. Guess what, now at home, I have to have the same vigor. Guess what, you know, DA's office, the things that Herman is dealing with, they have to be so careful, not only in the office, but at home. So in that sense, that's the better service, your two desktops, all these new technologies I'm going to deal with in this simple way. Our new solution, all requires a browser, that's it, and no deliver a browser-based application, integration, to home, in a secure way, the things that we've been praying for for a long, long time. So this post-pandemic world is going to make us more agile, is going to make us more efficient, and hopefully we're going to do much more with less. >> Excellent, well, Herman, I have one more question for you, if you can, give us a little bit of a look forward. We always love to hear from a CIO just, number one, what's on your plate, and as you look at this solution, what you'll be using it for and going, and secondly, if you've got anything, if you could have something more that the ecosystem, maybe HPE and Nutanix, or maybe just in general from the ecosystem out there, that would make your life and your staff's easier. >> Well, you know, that's a great question. We have over 30 projects on our project list right now that are active projects that's going on. I have a staff of 9 IT professionals with three open positions, so I should say, 9, I have six, actual staff members with three open positions, currently, and we're on a hiring freeze. So one of the great things about the Nutanix HP solution has been that I've been able to downsize from the two systems engineer to the one system engineer without necessarily losing any bandwidth or knowledge or experience because the environment is so easy to manage, which has been great. We will continue to move forward with the digitization of our records and utilizing the cloud services that are available, through the various channels, and it's just an unprecedented time. I see that this is going to be the new norm. >> Excellent, so Tarkan, we'll let you put the exclamation point on it, give us the final takeaway for HPE and Nutanix. >> So, look, at the end of the day, we are in this new software defined growth and multicloud fashion having a partnership within two companies which covers data center services, DevOps services, as well as end user services, end to end, both in private clouds, also in a multicloud fashion, through telco as well as hyperscalers and Azure, deliver the service, with the open end model the customer chooses. Again, end to end, from data center, to DevOps, to end user, is the perfect marriage that HPE and Nutanix's relationship delivers. So we are really looking forward to working with customers like Herman, to deliver on that dream, on that journey, making sure that cloud migration and cloud consolidation happens efficiently end to end. Again, from the data center, to DevOps, to end user, all the way in a fashion that we do more with less in this post-pandemic world, and we're looking forward to that partnership as we move forward, and thank you Stu and thank you, Herman, for the time today. >> Excellent, well, Tarkan Maner, always a pleasure to catch up with you, really great to get all the update from you and really appreciate HPE and Nutanix bringing us Herman Brown, CIO, Herman, thank you so much for joining us, really appreciate you sharing your story, hopefully, you'll be able to open up and hire those three people that you're looking to hire in your future. Thank you both so much for joining us. >> Thank you, thank you very much for having me, Tarkan, it's always a pleasure, thanks Nutanix and HPE for just making a solid, great solution that can help in the success of the DA's office. Really do appreciate it. >> Thank you so much, Herman, again, I really appreciate it. >> We'll be back with more coverage from HPE Discover 2020, the Virtual Experience. I'm Stu Miniman, thank you, as always, for watching theCUBE. (gentle music)

>>from around the globe. It's the Cube with digital coverage of enterprise data automation, an event Siri's brought to you by Iot. Tahoe Welcome everybody to Enterprise Data Automation. Ah co created digital program on the Cube with support from my hotel. So my name is Dave Volante. And today we're using the hashtag data automated. You know, organizations. They really struggle to get more value out of their data, time to data driven insights that drive cost savings or new revenue opportunities. They simply take too long. So today we're gonna talk about how organizations can streamline their data operations through automation, machine intelligence and really simplifying data migrations to the cloud. We'll be talking to technologists, visionaries, hands on practitioners and experts that are not just talking about streamlining their data pipelines. They're actually doing it. So keep it right there. We'll be back shortly with a J ahora who's the CEO of Iot Tahoe to kick off the program. You're watching the Cube, the leader in digital global coverage. We're right back right after this short break. Innovation impact influence. Welcome to the Cube disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader. High tech digital coverage from around the globe. It's the Cube with digital coverage of enterprise, data, automation and event. Siri's brought to you by Iot. Tahoe. Okay, we're back. Welcome back to Data Automated. A J ahora is CEO of I O ta ho, JJ. Good to see how things in London >>Thanks doing well. Things in, well, customers that I speak to on day in, day out that we partner with, um, they're busy adapting their businesses to serve their customers. It's very much a game of ensuring the week and serve our customers to help their customers. Um, you know, the adaptation that's happening here is, um, trying to be more agile. Got to be more flexible. Um, a lot of pressure on data, a lot of demand on data and to deliver more value to the business, too. So that customers, >>as I said, we've been talking about data ops a lot. The idea being Dev Ops applied to the data pipeline, But talk about enterprise data automation. What is it to you. And how is it different from data off >>Dev Ops, you know, has been great for breaking down those silos between different roles functions and bring people together to collaborate. Andi, you know, we definitely see that those tools, those methodologies, those processes, that kind of thinking, um, lending itself to data with data is exciting. We look to do is build on top of that when data automation, it's the it's the nuts and bolts of the the algorithms, the models behind machine learning that the functions. That's where we investors, our r and d on bringing that in to build on top of the the methods, the ways of thinking that break down those silos on injecting that automation into the business processes that are going to drive a business to serve its customers. It's, um, a layer beyond Dev ops data ops. They can get to that point where well, I think about it is is the automation behind new dimension. We've come a long way in the last few years. Boy is, we started out with automating some of those simple, um, to codify, um, I have a high impact on organization across the data a cost effective way house. There's data related tasks that classify data on and a lot of our original pattern certain people value that were built up is is very much around that >>love to get into the tech a little bit in terms of how it works. And I think we have a graphic here that gets into that a little bit. So, guys, if you bring that up, >>sure. I mean right there in the middle that the heart of what we do it is, you know, the intellectual property now that we've built up over time that takes from Hacha genius data sources. Your Oracle Relational database. Short your mainframe. It's a lay and increasingly AP eyes and devices that produce data and that creates the ability to automatically discover that data. Classify that data after it's classified. Them have the ability to form relationships across those different source systems, silos, different lines of business. And once we've automated that that we can start to do some cool things that just puts of contact and meaning around that data. So it's moving it now from bringing data driven on increasingly where we have really smile, right people in our customer organizations you want I do some of those advanced knowledge tasks data scientists and ah, yeah, quants in some of the banks that we work with, the the onus is on, then, putting everything we've done there with automation, pacifying it, relationship, understanding that equality, the policies that you can apply to that data. I'm putting it in context once you've got the ability to power. Okay, a professional is using data, um, to be able to put that data and contacts and search across the entire enterprise estate. Then then they can start to do some exciting things and piece together the the tapestry that fabric across that different system could be crm air P system such as s AP and some of the newer brown databases that we work with. Snowflake is a great well, if I look back maybe five years ago, we had prevalence of daily technologies at the cutting edge. Those are converging to some of the cloud platforms that we work with Google and AWS and I think very much is, as you said it, those manual attempts to try and grasp. But it is such a complex challenges scale quickly runs out of steam because once, once you've got your hat, once you've got your fingers on the details Oh, um, what's what's in your data state? It's changed, You know, you've onboard a new customer. You signed up a new partner. Um, customer has, you know, adopted a new product that you just Lawrence and there that that slew of data keeps coming. So it's keeping pace with that. The only answer really is is some form of automation >>you're working with AWS. You're working with Google, You got red hat. IBM is as partners. What is attracting those folks to your ecosystem and give us your thoughts on the importance of ecosystem? >>That's fundamental. So, I mean, when I caimans where you tell here is the CEO of one of the, um, trends that I wanted us CIO to be part of was being open, having an open architecture allowed one thing that was close to my heart, which is as a CEO, um, a c i o where you go, a budget vision on and you've already made investments into your organization, and some of those are pretty long term bets. They should be going out 5 10 years, sometimes with the CRM system training up your people, getting everybody working together around a common business platform. What I wanted to ensure is that we could openly like it using AP eyes that were available, the love that some investment on the cost that has already gone into managing in organizations I t. But business users to before. So part of the reason why we've been able to be successful with, um, the partners like Google AWS and increasingly, a number of technology players. That red hat mongo DB is another one where we're doing a lot of good work with, um and snowflake here is, um Is those investments have been made by the organizations that are our customers, and we want to make sure we're adding to that. And they're leveraging the value that they've already committed to. >>Yeah, and maybe you could give us some examples of the r A y and the business impact. >>Yeah, I mean, the r a y David is is built upon on three things that I mentioned is a combination off. You're leveraging the existing investment with the existing estate, whether that's on Microsoft Azure or AWS or Google, IBM, and I'm putting that to work because, yeah, the customers that we work with have had made those choices. On top of that, it's, um, is ensuring that we have got the automation that is working right down to the level off data, a column level or the file level we don't do with meta data. It is being very specific to be at the most granular level. So as we've grown our processes and on the automation, gasification tagging, applying policies from across different compliance and regulatory needs that an organization has to the data, everything that then happens downstream from that is ready to serve a business outcome now without hoping out which run those processes within hours of getting started And, um, Bill that picture, visualize that picture and bring it to life. You know, the PR Oh, I that's off the bat with finding data that should have been deleted data that was copies off on and being able to allow the architect whether it's we're working on GCB or a migration to any other clouds such as AWS or a multi cloud landscape right off the map. >>A. J. Thanks so much for coming on the Cube and sharing your insights and your experience is great to have you. >>Thank you, David. Look who is smoking in >>now. We want to bring in the customer perspective. We have a great conversation with Paul Damico, senior vice president data architecture, Webster Bank. So keep it right there. >>Utah Data automated Improve efficiency, Drive down costs and make your enterprise data work for you. Yeah, we're on a mission to enable our customers to automate the management of data to realise maximum strategic and operational benefits. We envisage a world where data users consume accurate, up to date unified data distilled from many silos to deliver transformational outcomes, activate your data and avoid manual processing. Accelerate data projects by enabling non I t resources and data experts to consolidate categorize and master data. Automate your data operations Power digital transformations by automating a significant portion of data management through human guided machine learning. Yeah, get value from the start. Increase the velocity of business outcomes with complete accurate data curated automatically for data, visualization tours and analytic insights. Improve the security and quality of your data. Data automation improves security by reducing the number of individuals who have access to sensitive data, and it can improve quality. Many companies report double digit era reduction in data entry and other repetitive tasks. Trust the way data works for you. Data automation by our Tahoe learns as it works and can ornament business user behavior. It learns from exception handling and scales up or down is needed to prevent system or application overloads or crashes. It also allows for innate knowledge to be socialized rather than individualized. No longer will your companies struggle when the employee who knows how this report is done, retires or takes another job, the work continues on without the need for detailed information transfer. Continue supporting the digital shift. Perhaps most importantly, data automation allows companies to begin making moves towards a broader, more aspirational transformation, but on a small scale but is easy to implement and manage and delivers quick wins. Digital is the buzzword of the day, but many companies recognized that it is a complex strategy requires time and investment. Once you get started with data automation, the digital transformation initiated and leaders and employees alike become more eager to invest time and effort in a broader digital transformational agenda. Yeah, >>everybody, we're back. And this is Dave Volante, and we're covering the whole notion of automating data in the Enterprise. And I'm really excited to have Paul Damico here. She's a senior vice president of enterprise Data Architecture at Webster Bank. Good to see you. Thanks for coming on. >>Nice to see you too. Yes. >>So let's let's start with Let's start with Webster Bank. You guys are kind of a regional. I think New York, New England, uh, leave headquartered out of Connecticut, but tell us a little bit about the >>bank. Yeah, Webster Bank is regional, Boston. And that again in New York, Um, very focused on in Westchester and Fairfield County. Um, they're a really highly rated bank regional bank for this area. They, um, hold, um, quite a few awards for the area for being supportive for the community. And, um, are really moving forward. Technology lives. Currently, today we have, ah, a small group that is just working toward moving into a more futuristic, more data driven data warehouse. That's our first item. And then the other item is to drive new revenue by anticipating what customers do when they go to the bank or when they log into there to be able to give them the best offer. The only way to do that is you have timely, accurate, complete data on the customer and what's really a great value on off something to offer that >>at the top level, what were some of what are some of the key business drivers there catalyzing your desire for change >>the ability to give the customer what they need at the time when they need it? And what I mean by that is that we have, um, customer interactions and multiple weights, right? And I want to be able for the customer, too. Walk into a bank, um, or online and see the same the same format and being able to have the same feel, the same look and also to be able to offer them the next best offer for them. >>Part of it is really the cycle time, the end end cycle, time that you're pressing. And then there's if I understand it, residual benefits that are pretty substantial from a revenue opportunity >>exactly. It's drive new customers, Teoh new opportunities. It's enhanced the risk, and it's to optimize the banking process and then obviously, to create new business. Um, and the only way we're going to be able to do that is that we have the ability to look at the data right when the customer walks in the door or right when they open up their app. >>Do you see the potential to increase the data sources and hence the quality of the data? Or is that sort of premature? >>Oh, no. Um, exactly. Right. So right now we ingest a lot of flat files and from our mainframe type of runnin system that we've had for quite a few years. But now that we're moving to the cloud and off Prem and on France, you know, moving off Prem into, like, an s three bucket Where that data king, we can process that data and get that data faster by using real time tools to move that data into a place where, like, snowflake Good, um, utilize that data or we can give it out to our market. The data scientists are out in the lines of business right now, which is great, cause I think that's where data science belongs. We should give them on, and that's what we're working towards now is giving them more self service, giving them the ability to access the data in a more robust way. And it's a single source of truth. So they're not pulling the data down into their own like tableau dashboards and then pushing the data back out. I have eight engineers, data architects, they database administrators, right, um, and then data traditional data forwarding people, Um, and because some customers that I have that our business customers lines of business, they want to just subscribe to a report. They don't want to go out and do any data science work. Um, and we still have to provide that. So we still want to provide them some kind of read regiment that they wake up in the morning and they open up their email. And there's the report that they just drive, um, which is great. And it works out really well. And one of the things. This is why we purchase I o waas. I would have the ability to give the lines of business the ability to do search within the data, and we read the data flows and data redundancy and things like that and help me cleanup the data and also, um, to give it to the data. Analysts who say All right, they just asked me. They want this certain report and it used to take Okay, well, we're gonna four weeks, we're going to go. We're gonna look at the data, and then we'll come back and tell you what we dio. But now with Iot Tahoe, they're able to look at the data and then, in one or two days of being able to go back and say, Yes, we have data. This is where it is. This is where we found that this is the data flows that we've found also, which is what I call it is the birth of a column. It's where the calm was created and where it went live as a teenager. And then it went to, you know, die very archive. >>In researching Iot Tahoe, it seems like one of the strengths of their platform is the ability to visualize data the data structure, and actually dig into it. But also see it, um, and that speeds things up and gives everybody additional confidence. And then the other pieces essentially infusing ai or machine intelligence into the data pipeline is really how you're attacking automation, right? >>Exactly. So you're able to let's say that I have I have seven cause lines of business that are asking me questions. And one of the questions I'll ask me is, um, we want to know if this customer is okay to contact, right? And you know, there's different avenues so you can go online to go. Do not contact me. You can go to the bank And you could say, I don't want, um, email, but I'll take tests and I want, you know, phone calls. Um, all that information. So seven different lines of business asked me that question in different ways once said Okay to contact the other one says, You know, just for one to pray all these, you know, um, and each project before I got there used to be siloed. So one customer would be 100 hours for them to do that and analytical work, and then another cut. Another of analysts would do another 100 hours on the other project. Well, now I can do that all at once, and I can do those type of searches and say yes we already have that documentation. Here it is. And this is where you can find where the customer has said, You know, you don't want I don't want to get access from you by email, or I've subscribed to get emails from you. I'm using Iot typos eight automation right now to bring in the data and to start analyzing the data close to make sure that I'm not missing anything and that I'm not bringing over redundant data. Um, the data warehouse that I'm working off is not, um a It's an on prem. It's an oracle database. Um, and it's 15 years old, so it has extra data in it. It has, um, things that we don't need anymore. And Iot. Tahoe's helping me shake out that, um, extra data that does not need to be moved into my S three. So it's saving me money when I'm moving from offering on Prem. >>What's your vision or your your data driven organization? >>Um, I want for the bankers to be able to walk around with on iPad in their hands and be able to access data for that customer really fast and be able to give them the best deal that they can get. I want Webster to be right there on top, with being able to add new customers and to be able to serve our existing customers who had bank accounts. Since you were 12 years old there and now our, you know, multi. Whatever. Um, I want them to be able to have the best experience with our our bankers. >>That's really what I want is a banking customer. I want my bank to know who I am, anticipate my needs and create a great experience for me. And then let me go on with my life. And so that's a great story. Love your experience, your background and your knowledge. Can't thank you enough for coming on the Cube. >>No, thank you very much. And you guys have a great day. >>Next, we'll talk with Lester Waters, who's the CTO of Iot Toe cluster takes us through the key considerations of moving to the cloud. >>Yeah, right. The entire platform Automated data Discovery data Discovery is the first step to knowing your data auto discover data across any application on any infrastructure and identify all unknown data relationships across the entire siloed data landscape. smart data catalog. Know how everything is connected? Understand everything in context, regained ownership and trust in your data and maintain a single source of truth across cloud platforms, SAS applications, reference data and legacy systems and power business users to quickly discover and understand the data that matters to them with a smart data catalog continuously updated ensuring business teams always have access to the most trusted data available. Automated data mapping and linking automate the identification of unknown relationships within and across data silos throughout the organization. Build your business glossary automatically using in house common business terms, vocabulary and definitions. Discovered relationships appears connections or dependencies between data entities such as customer account, address invoice and these data entities have many discovery properties. At a granular level, data signals dashboards. Get up to date feeds on the health of your data for faster improved data management. See trends, view for history. Compare versions and get accurate and timely visual insights from across the organization. Automated data flows automatically captured every data flow to locate all the dependencies across systems. Visualize how they work together collectively and know who within your organization has access to data. Understand the source and destination for all your business data with comprehensive data lineage constructed automatically during with data discovery phase and continuously load results into the smart Data catalog. Active, geeky automated data quality assessments Powered by active geek You ensure data is fit for consumption that meets the needs of enterprise data users. Keep information about the current data quality state readily available faster Improved decision making Data policy. Governor Automate data governance End to end over the entire data lifecycle with automation, instant transparency and control Automate data policy assessments with glossaries, metadata and policies for sensitive data discovery that automatically tag link and annotate with metadata to provide enterprise wide search for all lines of business self service knowledge graph Digitize and search your enterprise knowledge. Turn multiple siloed data sources into machine Understandable knowledge from a single data canvas searching Explore data content across systems including GRP CRM billing systems, social media to fuel data pipelines >>Yeah, yeah, focusing on enterprise data automation. We're gonna talk about the journey to the cloud Remember, the hashtag is data automate and we're here with Leicester Waters. Who's the CTO of Iot Tahoe? Give us a little background CTO, You've got a deep, deep expertise in a lot of different areas. But what do we need to know? >>Well, David, I started my career basically at Microsoft, uh, where I started the information Security Cryptography group. They're the very 1st 1 that the company had, and that led to a career in information, security. And and, of course, as easy as you go along with information security data is the key element to be protected. Eso I always had my hands and data not naturally progressed into a roll out Iot talk was their CTO. >>What's the prescription for that automation journey and simplifying that migration to the cloud? >>Well, I think the first thing is understanding what you've got. So discover and cataloging your data and your applications. You know, I don't know what I have. I can't move it. I can't. I can't improve it. I can't build upon it. And I have to understand there's dependence. And so building that data catalog is the very first step What I got. Okay, >>so So we've done the audit. We know we've got what's what's next? Where do we go >>next? So the next thing is remediating that data you know, where do I have duplicate data? I may have often times in an organization. Uh, data will get duplicated. So somebody will take a snapshot of the data, you know, and then end up building a new application, which suddenly becomes dependent on that data. So it's not uncommon for an organization of 20 master instances of a customer, and you can see where that will go. And trying to keep all that stuff in sync becomes a nightmare all by itself. So you want to sort of understand where all your redundant data is? So when you go to the cloud, maybe you have an opportunity here to do you consolidate that that data, >>then what? You figure out what to get rid of our actually get rid of it. What's what's next? >>Yes, yes, that would be the next step. So figure out what you need. What, you don't need you Often times I've found that there's obsolete columns of data in your databases that you just don't need. Or maybe it's been superseded by another. You've got tables have been superseded by other tables in your database, so you got to kind of understand what's being used and what's not. And then from that, you can decide. I'm gonna leave this stuff behind or I'm gonna I'm gonna archive this stuff because I might need it for data retention where I'm just gonna delete it. You don't need it. All were >>plowing through your steps here. What's next on the >>journey? The next one is is in a nutshell. Preserve your data format. Don't. Don't, Don't. Don't boil the ocean here at music Cliche. You know, you you want to do a certain degree of lift and shift because you've got application dependencies on that data and the data format, the tables in which they sent the columns and the way they're named. So some degree, you are gonna be doing a lift and ship, but it's an intelligent lift and ship. The >>data lives in silos. So how do you kind of deal with that? Problem? Is that is that part of the journey? >>That's that's great pointed because you're right that the data silos happen because, you know, this business unit is start chartered with this task. Another business unit has this task and that's how you get those in stance creations of the same data occurring in multiple places. So you really want to is part of your cloud migration. You really want a plan where there's an opportunity to consolidate your data because that means it will be less to manage. Would be less data to secure, and it will be. It will have a smaller footprint, which means reduce costs. >>But maybe you could address data quality. Where does that fit in on the >>journey? That's that's a very important point, you know. First of all, you don't want to bring your legacy issues with U. S. As the point I made earlier. If you've got data quality issues, this is a good time to find those and and identify and remediate them. But that could be a laborious task, and you could probably accomplish. It will take a lot of work. So the opportunity used tools you and automate that process is really will help you find those outliers that >>what's next? I think we're through. I think I've counted six. What's the What's the lucky seven >>Lucky seven involved your business users. Really, When you think about it, you're your data is in silos, part of part of this migration to cloud as an opportunity to break down the silos. These silence that naturally occurs are the business. You, uh, you've got to break these cultural barriers that sometimes exists between business and say so. For example, I always advise there's an opportunity year to consolidate your sensitive data. Your P I. I personally identifiable information and and three different business units have the same source of truth From that, there's an opportunity to consolidate that into one. >>Well, great advice, Lester. Thanks so much. I mean, it's clear that the Cap Ex investments on data centers they're generally not a good investment for most companies. Lester really appreciate Lester Water CTO of Iot Tahoe. Let's watch this short video and we'll come right back. >>Use cases. Data migration. Accelerate digitization of business by providing automated data migration work flows that save time in achieving project milestones. Eradicate operational risk and minimize labor intensive manual processes that demand costly overhead data quality. You know the data swamp and re establish trust in the data to enable data signs and Data analytics data governance. Ensure that business and technology understand critical data elements and have control over the enterprise data landscape Data Analytics ENABLEMENT Data Discovery to enable data scientists and Data Analytics teams to identify the right data set through self service for business demands or analytical reporting that advanced too complex regulatory compliance. Government mandated data privacy requirements. GDP Our CCP, A, e, p, R HIPPA and Data Lake Management. Identify late contents cleanup manage ongoing activity. Data mapping and knowledge graph Creates BKG models on business enterprise data with automated mapping to a specific ontology enabling semantic search across all sources in the data estate data ops scale as a foundation to automate data management presences. >>Are you interested in test driving the i o ta ho platform Kickstart the benefits of data automation for your business through the Iot Labs program? Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iot. Top Click on the link and connect with the data engineer to learn more and see Iot Tahoe in action. Everybody, we're back. We're talking about enterprise data automation. The hashtag is data automated and we're going to really dig into data migrations, data migrations. They're risky, they're time consuming and they're expensive. Yousef con is here. He's the head of partnerships and alliances at I o ta ho coming again from London. Hey, good to see you, Seth. Thanks very much. >>Thank you. >>So let's set up the problem a little bit. And then I want to get into some of the data said that migration is a risky, time consuming, expensive. They're they're often times a blocker for organizations to really get value out of data. Why is that? >>I think I mean, all migrations have to start with knowing the facts about your data. Uh, and you can try and do this manually. But when you have an organization that may have been going for decades or longer, they will probably have a pretty large legacy data estate so that I have everything from on premise mainframes. They may have stuff which is probably in the cloud, but they probably have hundreds, if not thousands of applications and potentially hundreds of different data stores. >>So I want to dig into this migration and let's let's pull up graphic. It will talk about We'll talk about what a typical migration project looks like. So what you see, here it is. It's very detailed. I know it's a bit of an eye test, but let me call your attention to some of the key aspects of this, uh and then use if I want you to chime in. So at the top here, you see that area graph that's operational risk for a typical migration project, and you can see the timeline and the the milestones That Blue Bar is the time to test so you can see the second step. Data analysis. It's 24 weeks so very time consuming, and then let's not get dig into the stuff in the middle of the fine print. But there's some real good detail there, but go down the bottom. That's labor intensity in the in the bottom, and you can see hi is that sort of brown and and you could see a number of data analysis data staging data prep, the trial, the implementation post implementation fixtures, the transition to be a Blu, which I think is business as usual. >>The key thing is, when you don't understand your data upfront, it's very difficult to scope to set up a project because you go to business stakeholders and decision makers, and you say Okay, we want to migrate these data stores. We want to put them in the cloud most often, but actually, you probably don't know how much data is there. You don't necessarily know how many applications that relates to, you know, the relationships between the data. You don't know the flow of the basis of the direction in which the data is going between different data stores and tables. So you start from a position where you have pretty high risk and probably the area that risk you could be. Stack your project team of lots and lots of people to do the next phase, which is analysis. And so you set up a project which has got a pretty high cost. The big projects, more people, the heavy of governance, obviously on then there, then in the phase where they're trying to do lots and lots of manual analysis, um, manual processes, as we all know, on the layer of trying to relate data that's in different grocery stores relating individual tables and columns, very time consuming, expensive. If you're hiring in resource from consultants or systems integrators externally, you might need to buy or to use party tools. Aziz said earlier the people who understand some of those systems may have left a while ago. CEO even higher risks quite cost situation from the off on the same things that have developed through the project. Um, what are you doing with Ayatollah? Who is that? We're able to automate a lot of this process from the very beginning because we can do the initial data. Discovery run, for example, automatically you very quickly have an automated validator. A data met on the data flow has been generated automatically, much less time and effort and much less cars stopped. >>Yeah. And now let's bring up the the the same chart. But with a set of an automation injection in here and now. So you now see the sort of Cisco said accelerated by Iot, Tom. Okay, great. And we're gonna talk about this, but look, what happens to the operational risk. A dramatic reduction in that, That that graph and then look at the bars, the bars, those blue bars. You know, data analysis went from 24 weeks down to four weeks and then look at the labor intensity. The it was all these were high data analysis, data staging data prep trialling post implementation fixtures in transition to be a you all those went from high labor intensity. So we've now attacked that and gone to low labor intensity. Explain how that magic happened. >>I think that the example off a data catalog. So every large enterprise wants to have some kind of repository where they put all their understanding about their data in its price States catalog. If you like, imagine trying to do that manually, you need to go into every individual data store. You need a DB, a business analyst, reach data store. They need to do an extract of the data. But it on the table was individually they need to cross reference that with other data school, it stores and schemers and tables you probably with the mother of all Lock Excel spreadsheets. It would be a very, very difficult exercise to do. I mean, in fact, one of our reflections as we automate lots of data lots of these things is, um it accelerates the ability to water may, But in some cases, it also makes it possible for enterprise customers with legacy systems take banks, for example. There quite often end up staying on mainframe systems that they've had in place for decades. I'm not migrating away from them because they're not able to actually do the work of understanding the data, duplicating the data, deleting data isn't relevant and then confidently going forward to migrate. So they stay where they are with all the attendant problems assistance systems that are out of support. You know, you know, the biggest frustration for lots of them and the thing that they spend far too much time doing is trying to work out what the right data is on cleaning data, which really you don't want a highly paid thanks to scientists doing with their time. But if you sort out your data in the first place, get rid of duplication that sounds migrate to cloud store where things are really accessible. It's easy to build connections and to use native machine learning tools. You well, on the way up to the maturity card, you can start to use some of the more advanced applications >>massive opportunities not only for technology companies, but for those organizations that can apply technology for business. Advantage yourself, count. Thanks so much for coming on the Cube. Much appreciated. Yeah, yeah, yeah, yeah

>> Narrator: From around the globe, it's theCUBE! Covering HPE Discover Virtual Experience. Brought to you by HPE. >> Welcome back, I'm Stu Miniman, and this is theCUBE's coverage of HPE Discover 2020 the Virtual Experience, really happy to welcome to the program, we have a returning guest. Tarkan Maner is the Chief Commercial Officer at Nutanix, in a new role since the last time we had him on the program, and joining him, we have Herman Brown, who's the CIO for the City of San Francisco's District Attorney. Gentlemen, thank you so much for joining us. >> Thank you for having me. >> So, Tarkan, help set the stage for us. As I mentioned, we know you, our network knows you, but, new to Nutanix in the last year, and talk to us a little bit about this HPE Nutanix partnership. >> Yeah, if you noticed, first of all, thank you for hosting us, great to be here. This is probably, who knows, my fiftieth CUBE I guess, over the past two decades, especially the last twenty years have been crazy for us, obviously in the industry, lots of movement, lots of change. So let me go into the context, that led to Nutanix joining the company, about six months ago in the capacity of Chief Commercial Officer, a hybrid role with some product aspects, business development global market, our cloud infrastructure digitalizes and some of the Corp Dev we're working on. In that context, obviously, HPE is a very, very important strategic partner to us. As you know, the companies, the two companies have been working together for a long time, but especially the last, I would say, six to twelve months, we have this phenomenal relationship around what I call "three focused areas" of our business. Around our digital infrastructure, upward converge infrastructure, a business on top of that, our solutions from data center to DevOps and to this stuff, services, it's three specific segments, we built this really interesting really strong relationship with HPE with some of our philosophies and HPE's platform, now obviously, working through a multicloud channel, who are our own Nutanix cloud, our own hosted cloud, in addition to it, our Telco and SSP partners using their cloud infrastructure as well as some of the hyperscaling work we're doing, with Azure, in addition to our direct sales floors and private cloud approach, HPE and Nutanix are working hand in hand in this multicloud so to speak operative model. So it's a new relationship in some ways, from a multicloud perspective, but if it has to grow in segments, we had a phenomenal quarter in the last three months, we just released our results, and HPE is growing with us. And we're given definitely a great suite of solutions to our customers with the typical usual, simple to deploy, simple to use mechanics customers like on the HPE platform. So I'm sure we've got a whole lot more, but glad to be here also with Herman Brown, from the DA's office in San Francisco, my favorite city in the world, so glad to be here. Thank you, Stu, again, for hosting us. >> Great, thanks so much, Tarkan. You know, Herman, we're going to get into a lot of the technology pieces, you with your CIO hat on, you know, want to understand how cloud, how modern infrastructure, your applications are changing, but, give us a little bit about your personal background and really the purview that you cover in the city of San Francisco District Attorney's office. >> Yes, well, you know, I've been with the DA's office for just over 3 years, it'll be 4 years I guess in August of this year, and I come from twenty plus years of private sector experience, some government experience. And, you know, the city and county, the government is really no different than any other organization other than we're known to be a little bit slower to adopt the technologies, which is why I'm here. I want to help government become more efficient, more productive through the use of technology, and so I'm excited to be here and thank you, first and foremost, for having me on the show. I appreciate it. >> I love that you brought that up, because we've been doing theCUBE for just over a decade now, and in the early parts of that, it's like, right, okay, I'm talking to a local government, we understand, your budgets are tight, you're using older technology, you've got duct tape and baling wire to keep things going. The last few years, some of my favorite conversations have been in the public sector, because you talk about some of the tools that are out there, and don't need a huge capital investment to get started, I can modernize, so Herman, digital transformation, is that a term that you've brought from the private sector over to the public sector, or what kind of transformations are you going through and what is it that's I guess driving the need for transformation in your world? >> So yeah, I've been with the city and county of San Francisco for nine years, so I'd love to say that I brought digital transformation or at least the term with me, but I was actually here in the DA's office or in the city and county's employment when that terminology came out. Being the CIO for the San Francisco District Attorney's office, I mean, we're essentially a law firm. And law firms are historically just paper intensive organizations, right, you have court filings and rap sheets, all these physical documents that have to be physically ink signed and transferred from one attorney to another to the courts, and between police departments and sheriffs and so forth and so on. And we just looked at, what are we doing, how can we work more efficient, you know? As a lot of organizations, we're always finding ourselves to be understaffed for the amount of work that we have going on, the city and county of San Francisco, the DA's office, we see roughly 26,000 cases a year, we try about half of those cases per year. And we're a staff of 320 people. That includes everyone, the attorneys, the paralegals, finance folks, IT, investigators. And so it was like, we need to really embrace technology and be able to help transform this paper intensive processes into automated, digital forms and documents that can minimize the physical transferring of data, especially now, during Covid-19. >> Yeah, Herman, that transformation process is often multi-step, there's a lot of people, there's technology, and then there's the applications. It was at a Nutanix show that the comment I made is, well, let's modernize the platform, then you can modernize the applications on top of it. Tarkan, maybe, I'd love to hear just a little commentary from you, you've got a great perspective on this. That modernization effort, where your customers are, some of the levers that Nutanix is helping them along that journey. >> Yeah, so everything Herman said is very interesting, and obviously, a delight to my ears, because as a technologist in the industry for the past three decades, we're dealing with this, what I call, transformational waves, and you know, in the last ten years, the cloud transformation from the server to transition transformation now, increasingly, we're seeing this very fast migrations from the old school legacy data centers with legacy infrastructure and apps, basically are lifting and shifting these applications to a new cloud, so to speak, opened the model. The cloud to us, in a sense, it's not a destination, it's an open model, so if we see the customer's needs at the end of the day, just like Herman outlined, Herman is not trying to do cloud or digitization for the digital cloud's sake, he's trying to lead his team and the DA's office, with the most DAs by the way, in the nation, making sure that they can process data faster. They can achieve their goals, working especially in this post-pandemic world, and the entire change that are happening in our country, in a big way over the past few weeks, the events, and how our country is going to change for the future. So there's going to be a lot of work going to be happening in the government, this transformation or digitization, migration to the cloud, is going to be a big deal, so as a company, it very quickly we've seen this as a huge opportunity for our customers, as we're partnering with them in a multi cloud way. We still believe our server partners are super important in this context with HPE, but the cloud services around HPE Greenlake, the things we are doing with them, at the same time, working with HPE and some of our partners delivering our own Nutanix cloud services as well as some of the things we have been doing with some of Telco's and service providers, to give choice to our customers to consume the services we provide on-prem, through our old cloud services through a third party telephone service provider, or the choice of hyper scale into the U.S. As your Google, unlimited oracle. So in this context this partnership is hugely important, so there's a lot going on with HPE with Antonio, with our CEO, with Tarak, our CFO, with Tom Black, it's Sonali. The entire executive team are working very closely with them, and with Hyko in the fuel organization, our fuel organization, and we really cherish customers like the DA's office who are doing the transformation, who are leading the transformation, during this pandemic and during this massive change in our country and hopefully it's going to make a transformative change to our world in terms of obviously not only technology, but social change, so you see this as a transformative time frame for companies like us and HPE and partners like Herman and DA's office. >> Herman, please! >> Yeah, I was just going to say, and absolutely I agree with Tarak, and the way that we're able to react so quickly to this pandemic is the fact that we've already have started this digital transformation, that we've already been looking at these cloud services, we've already started down this path, and so it's made the transition with this surging overnight change of the office nine to five, five days a week to you know, everyone is remote every day now, we couldn't do that without having these cloud services such as Nutanix and HPE partnership, to make that possible. >> Yeah, is there something specific you talk, the work from home initiative, did you have to scale something out, did you have to, you know, bring us inside that change that helped enable your workforce that you wouldn't have been able to do without this technology. >> Yeah, we absolutely had to scale out the workforce. I would say that before the beginning of this pandemic, we had roughly 15 people that probably had VPN access from outside the office, now you have to also understand that the DA's office is very unique in the form of the types of data that we handle and deal with, so I have HIPPA data, I have CJIS, which is criminal justice information, that's managed by DOJ, so there are certain systems that we normally would not be able to access from outside the office that we had to be able to access now remotely. And so it's taken some time to get us there to that point, but you know, having this environment that allowed us to scale up easily, start looking at digitizing this process and being able to have the storage and compute and processing power to be able to support that initiative is really what we're talking about, and that's what we've been doing. We've been quickly scaling, adding in additional storage but popping in drives and making this all possible in a very quickly and seamlessly process. >> Excellent. Maybe we've talked a little bit about the results and how you can move faster, you know, digital information all about leveraging your data and be able to react more quickly, so you know, the pandemic definitely has put services to the test and it sounds like they're doing well. Maybe step us back a little bit as to what led you to HPE and Nutanix, how you made that decision. >> Well, you know, we went through a trial, a period, proof of concept, we looked at Dell, we looked at HPE and Nutanix, we looked at a few different solutions, and it really boiled down to cost, and what we were getting, bang for the dollar. I think there are some other great solutions out there or good solutions out there but none of them came to the value that the partnership with HPE and Nutanix actually have to offer to us. You know, one of the things is that with this partnership is when there's a support issue, I call Nutanix, I'm not calling HPE, I'm not calling this, the other third party vendor, I'm not getting the runaround of "oh, that's not our problem, that's someone else's problem, you need to call the software team, you need to call the hardware team," no. It's one person that you know, we call, as I like to say, "one throat to choke." And fortunately, we haven't had to go that route, Nutanix has been an excellent partner for ours and they have been great to work with, and on the ball, and that's what I always talk about, success is not just the success of the organization, but the success of the individuals and the success of the partnership between organizations. And that's what I looked for is a business partner that wants to help me at my role at my organization be successful. >> Great. Herman, we talked about modernizing the environment, bring us inside the applications, if you would, what applications you're using, you know, are there new initiatives that you're doing from an application standpoint? >> Yeah, so we're running the same standard applications that most organizations are running, with DHCPISS, you know, I have some other systems that we run just because of the CIO, CICA hat that I also wear within the organization, I'm very security conscious about talking about those applications. But we run pretty much the same basic applications as most organizations do. Those specialized applications that we also operate on, we do see an improvement in performance, we do see the speediness of the access, the more stability and reliability of the solutions, and so we're very pleased with the performance that we're getting. >> Excellent. You also, you talked about the efficiency of what you're doing. I mentioned earlier that, public sector, you can get started, you know, for smaller chunks using things like Nutanix, but budget, obviously, still a concern, I'm sure, anything you're doing with the verbalization in the infrastructure that is helping you keep budget under control? >> Absolutely, I mean, the Nutanix environment is scalable, it allows us to be able to look at other solutions such as CDI, which we're talking about and looking at, potentially doing for staff members that don't have laptops that may need laptops or need remote access into the system. We also have that ability to scale up with just another leg, more storage, it makes it very easy to go with where you're looking at cost-saving measures, currently running BMWare on the back end, but looking to convert that over AHV, yes, in the future, that can also help us reduce those costs in the future as well. Especially at this point in time, where city and county is looking for department budget savings. >> Excellent. Tarkan, I guess this would be a good point for you to chime in on, you know, generally, AHV and any other commentary you've got regarding-- >> I was just trying to hold my words back, because the things that Herman is doing are so exciting in a way, you know, techies like myself still get really excited. Like Herman talked about we're not doing infrastructure for infrastructure's sake. At the end of the day, Herman and his office like many government offices both in the fed as in state or local, have to do more with less. Obviously in this post-pandemic world, you get even more efficient, more innovative, and get most output from our input. In that context, bringing storage, compute, networking, all integrated in a converged way, it's smart, it's not just adding them up, one plus one plus one equals three, but one plus one plus one equals less than one, in terms of cost, making it make sure it's infrastructures are simplified, easy to deploy, easy to use, that's why we keep an NPS score of 90, by the way, part of the reason, a little bit of shameless plug there for you. I don't know many companies who have an NPS 90 because we make infrastructure simple. So if you settle this, to Herman's point, all those applications he's managing and building and then obviously digitizing, and in some way, lifting a shifting and creating a new cloud digitized model, he want to make sure Herman and companies and organizations like the DA's office under leadership, with innovative CIOs like Herman, making sure they have choice. They can choose the prem model they want, on-prem, off-prem, hybrid, or multicloud, or in a government cloud fashion, and deliver these services. To give you an example, we talked about home as the extended enterprise. Our home office is now part of the office. I have to secure my home the way I secure my Nutanix headquarters because I'm now running my business from home. So in the past, there was a delineation between home and office. Now home is part of the extended office. The way I manage my trash, the way I manage my peer flows, applications, the network, latency, everything has to be dealt with in a very smart way. But even our paper trash in our office, we manage it carefully because of the IP, you know, people steal IP. Guess what, now at home, I have to have the same vigor. Guess what, you know, DA's office, the things that Herman is dealing with, they have to be so careful, not only in the office, but at home. So in that sense, that's the better service, your two desktops, all these new technologies I'm going to deal with in this simple way. Our new solution, all requires a browser, that's it, and no deliver a browser-based application, integration, to home, in a secure way, the things that we've been praying for for a long, long time. So this post-pandemic world is going to make us more agile, is going to make us more efficient, and hopefully we're going to do much more with less. >> Excellent, well, Herman, I have one more question for you, if you can, give us a little bit of a look forward. We always love to hear from a CIO just, number one, what's on your plate, and as you look at this solution, what you'll be using it for and going, and secondly, if you've got anything, if you could have something more that the ecosystem, maybe HPE and Nutanix, or maybe just in general from the ecosystem out there, that would make your life and your staff's easier. >> Well, you know, that's a great question. We have over 30 projects on our project list right now that are active projects that's going on. I have a staff of 9 IT professionals with three open positions, so I should say, 9, I have six, actual staff members with three open positions, currently, and we're on a hiring freeze. So one of the great things about the Nutanix HP solution has been that I've been able to downsize from the two systems engineer to the one system engineer without necessarily losing any bandwidth or knowledge or experience because the environment is so easy to manage, which has been great. We will continue to move forward with the digitization of our records and utilizing the cloud services that are available, through the various channels, and it's just an unprecedented time. I see that this is going to be the new norm. >> Excellent, so Tarkan, we'll let you put the exclamation point on it, give us the final takeaway for HPE and Nutanix. >> So, look, at the end of the day, we are in this new software defined growth and multicloud fashion having a partnership within two companies which covers data center services, DevOps services, as well as end user services, end to end, both in private clouds, also in a multicloud fashion, through telco as well as hyperscalers and Azure, deliver the service, with the open end model the customer chooses. Again, end to end, from data center, to DevOps, to end user, is the perfect marriage that HPE and Nutanix's relationship delivers. So we are really looking forward to working with customers like Herman, to deliver on that dream, on that journey, making sure that cloud migration and cloud consolidation happens efficiently end to end. Again, from the data center, to DevOps, to end user, all the way in a fashion that we do more with less in this post-pandemic world, and we're looking forward to that partnership as we move forward, and thank you Stu and thank you, Herman, for the time today. >> Excellent, well, Tarkan Maner, always a pleasure to catch up with you, really great to get all the update from you and really appreciate HPE and Nutanix bringing us Herman Brown, CIO, Herman, thank you so much for joining us, really appreciate you sharing your story, hopefully, you'll be able to open up and hire those three people that you're looking to hire in your future. Thank you both so much for joining us. >> Thank you, thank you very much for having me, Tarkan, it's always a pleasure, thanks Nutanix and HPE for just making a solid, great solution that can help in the success of the DA's office. Really do appreciate it. >> Thank you so much, Herman, again, I really appreciate it. >> We'll be back with more coverage from HPE Discover 2020, the Virtual Experience. I'm Stu Miniman, thank you, as always, for watching theCUBE. (gentle music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering AWS re:Invent 2019. Brought to you by Amazon Web Services and Intel, along with it's ecosystem partners. >> Welcome inside Live here at the Sands as we continue our coverage of AWS re:Invent 2019 on theCUBE, day three. Always an exciting time I think to get a summary of what's happened here. Dave Vellante, John Walls, we're joined by a couple of gentlemen from Immuta, Steven Touw who's a co-founder and CTO. Steve, good to see you. >> Yeah thanks for having me. >> John Walls: And Rob Lancaster, who's the GM of Cloud at Immuta. Rob, thanks for joining us as well. >> Great to be here. >> First off, let's talk about Immuta a little bit. You're all about governance right? You're trying to make it simple, easy, taking out the complexity. But for those at home who might not be too familiar with your company, tell us a little bit about you. >> Yeah so the company started out, our roots are in the U.S. intelligence community. So we had been dealing with access and control issues for data for years and we said to ourselves, "Hey this product has to be useful for non-IC customers. "This problem has to exist." And with the advent of all these privacy regulations like CCPA, GDPR and of course HIPPA's been around for a long time, really our goal was to bring a product to the market that makes it easy to govern access to data in a way that you don't have to be technical to do it, you don't have to understand how to write SQL statements, you don't have to be a system administrator. We really bring together three personas, the users that want to get access to the data, legal compliance that needs to understand how the rules are being enforced or even enforce them themselves, and then of course the data owners and the DBAs who need to expose the data. So usually those three personas are at odds with one another, we bring them together in our platform and allow them to work together in a way that's compliant and also accelerates their data analytics. >> Could we talk a little bit about why this is such a problem? Because it is a big problem and especially today and in the cloud and we'll get into that, but you've got data lakes, data oceans now, you got data coming in, all types of data. Might be internal transaction data, it might be stuff in your data warehouse. And the organization say, "Well I want some other data. "I want to bring in maybe some social data." So certain data is, everybody can have access to. Certain data not everybody can have access to. And it's not necessarily just a security problem, edicts of my organization that need to be enforced. So first of all, is that sort of, the problem that you're solving? And maybe you can double-click on that a little bit. >> Yeah sure, so the market has evolved and is evolving. You allude to data lakes, I think you can point to the immersion of Hadoop, as a distributed infrastructure as kind of the original data lakes, or the most recent data lakes, where you can store all your data and run analytics on all your data, and now with the advent, with the emergence of Cloud you've effectively got very low, if not zero cost storage, and the ability to throw an unlimited amount of compute at the data. That, kind of in conjunction with heightened awareness for consumer data privacy and risk associated with data, has created a market for data governance beyond kind of the course-grained access controls that people have been using on their databases for decades now. >> Yeah I mean Hadoop really got it all started. You're right and despite all it's problems, it had some real epiphany-like technical innovations, but one of the things that it didn't worry about at the time was governance. So whose responsibility is this? Is it the CISO? That is essentially trying to build out a new cloud stack to provide security, privacy, governance and what does that stack look like? >> Rob: Go ahead. >> Yeah so it depends, it's actually pretty interesting that different organizations have tackled this different ways. So we have CISOs that maintain this. In other organizations we've got the legal compliance teams that want to do this but maybe don't have the technical chops. And the CISO doesn't necessarily know all the privacy rules that need to be enforced, so it's kind of moving into this world where security is about keeping the bad guys out and black or white access, like you either can see the data or you won't, but with privacy controls it gets into this gray area where there's a lot of technical complexity and there's a lot of legal complexity. So the organizations struggle with this 'cause you've got to play in that gray area where it's not just like I said, black and white. The analogy we use is, security is like a light switch, you're either in or you're out. With privacy controls you need to anonymize the data, you need to do privacy by design. It's like a dimmer switch where you want to play in that gray area and allow some utility out of the data but also protect privacy at differing levels of whatever you're doing analytically. So this can be challenging for an organization to wrestle with because it's not as, I would argue it's not as black and white as it is with security. >> Your question is in many cases it's the business that's running really fast and that is building these data lakes because they want to get value out of their data and the CISO or the compliance or risk officers are the ones that are telling them to slow down. So our product that Steve set up caters to both parties. It checks the boxes for risk, but it also enable the business to get utility out of their data lake. >> It's a very complicated situation because you've got this corpus of data that's organic and constantly changing and you have, you mentioned GDPR, you've got California now, every state's going to have it's own regulations so you've got to be able to sort of adjudicate that. And can you talk about, I mean obviously I've interviewed Matt Carroll, we covered you guys so I know a little bit about you, but can you talk about your tech in terms of it's ability? You've got a capability to do really granular level understanding and governance policies, can you describe that a little bit? >> Yeah sure, so when we talk about privacy controls, these are things like way beyond just table-level access. So instead of saying, "Hey you have access to this table or not," or even, "You have access to this column or not," you've got to go deeper than that, you've got to be able to make rows disappear based on what people are doing. So for example, we have financial institution customers that are using us for all their trading data and only some traders can see some trade desks and we manage all that dynamically. We're not making anonymized copies of data. Everything happens at query time, and depending on what compute you're using that all works differently, but then at the column level we're able to do these anonymization techniques like we could make numeric data less specific, we could use techniques like k-anonymization that allows analysts to analyze the data but ensures that small groups that exist in that data won't reveal someone's true identity. And we have techniques like differential privacy, which provides mathematical guarantees of privacy. So for example, one of our manufacturing customers set aside, these are the four analytical use cases that we're using our data for and under GDPR we want different levels of privacy associated to those use cases. So they could do that all with Immuta. So they could say, "When I'm doing this "I want these columns to be anonymized to this level "and these rows to disappear, but if I'm doing something, "maybe more critical, which our consumers have consented to "you know there's less privacy controls." And that all happens dynamically so the analysts could actually switch context of what they're doing and get a different view of the data and all of that is audited so we understand why someone's doing what they're doing and when they're running queries we can associate those queries to purpose. >> We've talked about customers of course and they're adapting right, to a new world? How are you adapting? I mean what are you learning about, in terms of policy regulation and governance, what have you, you said you came out of the intelligence community, high bar there right? >> Steven Touw: Yeah. >> So what have you done to evolve as a company and what are you, as the headlights basically for these folks, what are you seeing change that is going to require a lot of shift on the other side? >> Yeah so, I don't know if you have thoughts. >> I mean it's a great question but there's really two parts to it, there's what are we doing? But, what is the market doing as well, right? So if you think about when we got started, even a year ago people understood the technology, they thought it was cool but maybe a little nichey for government or financial services or maybe healthcare because there's well understood regulation, these vertical regulation. Even over the past year with kind of this increasing or heightened awareness for consumer data privacy, not just driven by CCPA and GDPR but kind of this, call it the Facebook Effect right? Cambridge Analytica has created this awareness within the general population for what are these organizations actually doing with my data? Before it was okay 'cause you give your data to Google and you get a better search result and you're okay with that but now they may be using your data for their own profit in different ways so this has created this rising tides effect for the overall market and we talk a lot about organizations using something like Immuta to protect their highly sensitive data. I like to think of it is their most valuable data, which may be highly sensitive but it also could be the crown jewels, trading data for a bank for example. So it's become about extracting value and operational benefit from data, whereas the risk offices are trying to lock it down in many cases. >> So, there's definitely a big problem and people are becoming more aware of it. I want to talk about where you guys fit into this whole cloud ecosystem. There's a sea change now, there's this sort of, this new cloud coming into play. It's not just about infrastructure anymore. I'll give you some examples, you got all these data lakes, maybe you got Redshift running, Snowflake's another one, you've now got this data exchange where you can bring data right in the Cloud bring in all different types of data, you're bringing in some AML and AI and it's all, really again, a complicated situation. So I see you guys as fitting in there and real need but can you describe where you fit in the ecosystem, what your relationship is with AWS, how do I engage with you? >> Yeah absolutely, so a core part of our value is that we are heterogeneous in terms of the environment that we support. We support a hybrid estate so the architecture of the product is fully microservices based so we can run on PRIM as well as on Cloud, on any Cloud, we support effectively any popular database system or analytical tool. So think of us as a data abstraction layer across a hybrid environment, so we're here because AWS is obviously the big boy in the market, they have market share, this is a strategic relationship for us. We're working very deeply with AWS field teams, particularly around some of their verticals, the verticals that align to our business and at the end of the day we're trying to define a category. It's a similar category that we've had for decades but with all the changes that are happening in data and regulation and infrastructure what we're trying to do is raise the level of awareness for the fact that Immuta has actually solved the problem that many of these risk officers are struggling with today. >> Yeah and from a, diving a little on the technical side of that answer is that we are, think of us as the way to enforce policy in the Cloud. We consider ourselves a Cloud-first software vendor. And you don't necessarily want one point solution in Redshift or another point solution on your on-premise Cloudera instance, whatever it may be where you're using your data and running analytics, you need to abstract the policies out into a consistent layer and then have them be enforced across whatever you're using. So you might be using Cloudera today and then you switch to Databricks tomorrow, that shouldn't be a hard change from you from a policy perspective. You just re-point Immuta at Databricks and all your policies are still working like they used to so it gives you this flexibility now to use all these different services that AWS provides 'cause as was stated in the keynote on Tuesday, there's no one database solves all. You're always going to be using a heterogenous set of compute to do your job in analytics so you need a consistent way to enforce policies across all of that. >> That's a great point. I mean I don't know if you saw the Vanguard guy today in the keynote, he basically said, "We rip down, or tore down our big data infrastructure "moved it to the Cloud, spun up EMR." I mean there's a perfect example of, you got to bring your governance with you. You can't have to rebuild that whole stack. Are you in the Marketplace yet? >> Steve and Rob: Yes. >> You are, great, awesome. >> Yeah we launched a managed version of Immuta over the summer on AWS Marketplace. We'll be launching a second one shortly and it's really, the offering that we have out there is really geared toward, for lack of a better term, democratizing data governance. It's actually free up to the fifth user so any organization can deploy Immuta in under 30 minutes through Marketplace and start protecting their data. >> That's great, we had Dave McCann on yesterday, he runs the Marketplace, he was telling us just now, private offers for every marketplace, so ICV, so that's from. Last question I have is, how do you see this all playing out? You got GDPR, remember you talked about California regulations, there's a technology component, any predictions you guys want to share? What's your telescope say? >> All data will be regulated data eventually. So if you're not thinking about that now you need to. So, at least that's our theory, obviously, so we think it's critical that you're doing that from day one instead of day 365 and in your migration strategy. And if you're not thinking about that it's going to potentially bite you in the ass. >> Yeah you're right, I mean Web 2.0 was the wild, wild west, there was no privacy, there was no regulation, GDPR started to get people focused on that and it's now a whole new world. >> Gentlemen thank you, appreciate the time and best of luck. I know you said you had the big launch this summer but good things are ahead no doubt. >> For sure, thank you. >> Thank you. >> Dave Vellante: Thanks guys. >> Back with more coverage here on theCUBE. You're watching AWS re:Invent 2019. We are live and we're in Las Vegas. (upbeat tones)

>> From the Silicon Angle Media office, in Boston Massachusetts, it's the Cube. Now, here's your host, Dave Vellante. >> Hi everybody, welcome to this Cube Conversation here in our studios, outside of Boston. My name is Dave Vellante. I'm here with Matt Carroll, who's the CEO of Immuta. Matt, good to see ya. >> Good, nice to have me on. >> So we're going to talk about governance, how to automate governance, data privacy, but let me start with Immuta. What is Immuta, why did you guys start this company? >> Yeah, Immuta is an automated data governance platform. We started this company back in 2014 because we saw a gap in the market to be able to control data. What's happened in the market as changes is that every enterprise wants to leverage their data. Data's the new app. But, governments want to regulate it and consumers want to protect it. These were at odds with one another, so we saw a need of creating a platform that could meet the needs of everyone. To democratize access to data and in the enterprise, but at the same time, provide the necessary controls on the data to enforce any regulation, and ensure that there was transparency as to who is using it and why. >> So let's unpack that a little bit. Just try to dig into the problem here. So we all know about the data explosion, of course, and I often say data used to be a liability, now it's turned into an asset. People used to say get rid of the data, now everybody wants to mine it, and they want to take advantage of it, but that causes privacy concerns for individuals. We've seen this with Facebook and many others. Regulations now come into play, GDPR, different states applying different regulations, so you have all these competing forces. The business guys just want to go and get out to the market, but then the lawyers and the compliance officers and others. So are you attacking that problem? Maybe you could describe that problem a little further and talk about how you guys... >> Yeah, absolutely. As you described, there's over 150 privacy regulations being proposed over 25 states, just in 2019 alone. GDPR has created or opened the flood gates if you will, for people to start thinking about how do we want to insert our values into data? How should people use it? And so, the challenge now is, you're right, your most sensitive data in an enterprise is most likely going to give you the most insight into driving your business forward, creating new revenue channels, and be able to optimize your operational expenses. But the challenge is that consumers have awoken to, we're not exactly sure we're okay with that, right? We signed a YULU with you to just use our data for marketing, but now you're using it for other revenue channels? Why? And so, where Immuta is trying to play in there is how do we give the line of business the ability to access that instantaneously? But also give the CISO, the Chief Information Security Officer, and the governance seems the ability to take control back. So it's a delicate balance between speed and safety. And I think what's really happening in the market is we used to think about security from building firewalls, we invested in physical security controls around managing external adversaries from stealing our data. But now it's not necessarily someone trying to steal it, it's just potentially misusing it by accident in the enterprise. And the CISO is having to step in and provide that level of control. And it's also the collision of the cloud and these privacy regulations. Cause now, we have data everywhere, it's not just in our firewalls. And that's the big challenge. That's the opportunity at hand, democratization of data in the enterprise. The problem is data's not all in the enterprise. Data's in the cloud, data's in SaaS, data's in the infrastructure. >> It's distributed by it's very nature. All right, so there's a lot of things I want to follow up on. So first, there's GDPR. When GDPR came out of course, it was May of 2018 I think. It went into effect. It actually came out in 2017, but the penalties didn't take effect till '18. And I thought, okay, maybe this can be a framework for governments around the world and states. It sounds like yeah sort of, but not really. Maybe there's elements of GDPR that people are adopting, but then it sounds like they're putting in their own twists, which is going to be a nightmare for companies. So, are you not seeing a sort of, GDPR becoming this global standard? It sounds like, no. >> I don't think it's going to be necessarily global standard, but I do think the spirit of the GDPR, and at the core of it is, why are you using my data? What was the purpose? So traditionally, when we think about using data, we think about all right, who's the user, and what authorizations do they have, right? But now, there's a third question. Sure, you're authorized to see this data, depending on your role or organization right? But why are you using it? Are you using it for certain business use? Are you using it for personal use? Why are you using this? That's the spirit of GDPR that everyone is adopting across the board. And then of course, each state, or each federal organization is thinking about their unique lens on it, right? And so you're right. This is going to be incredibly complex. And the amount of policies being enforced at query time. I'm in my favorite, let's just say I'm in Tableau or Looker right? I'm just some simple analyst, I'm a young kid, I'm 22, my first job right? And I'm running these queries, I don't know where the data is, right? I don't know what I'm combining. And what we found is on average in these large enterprises, any query at any moment in time, might have over 500 thousand policies that need to be enforced in real time. >> Wow. >> And it's only getting worse. We have to automate it. No human can handle all those edge cases. We have to automate. >> So, I want to get into how you guys actually do that. Before I do, there seems to be... There's a lot of confusion in the marketplace. Take the word data management, data protection. All the backup guys are using that term, the database guys use that term, GOC folks use that term, so there's a lot of confusion there. You have all these adjacent markets coming together. You've got the whole governance risk and compliance space, you've got cyber security, there's privacy concerns, which is kind of two sides of the same coin. How do you see these adjacencies coming together? It seems like you sit in the middle of all that. >> Yeah, welcome to why my marketing budget is getting bigger and bigger. The challenge we're facing now is I think, who owns the problem right? The Chief Data Officer is taking on a much larger role in these organizations, the CISO is taking a much more larger role in reporting up to the board. You have the line of business who now is almost self-sustaining, they don't have to depend on IT as much any longer because of the cloud and because of the new compute layers to make it easier. So who owns it? At the end of the day, where we see it is we think there's a next generation of cyber tools that are coming out. We think that the CISO has to own this. And the reason is that the CISO's job is to protect the enterprise from cyber risk. And at the core of cyber risk is data. And they must own the data problem. The CDO must find the data, and explain what that data is, and make sure it's quality, but it is the CISO that must protect the enterprise from these threats. And so, I see us as part of this next wave of cyber tools that are coming out. There's other companies that are equally in our stratosphere, like BigID, we're seeing AWS with Macy doing sensitive data discovery, Google has their data loss prevention service. So the cloud players are starting to see, hey, we've got to identify sensitive data. There's other startups that are saying hey, we got to identify and catalog sensitive data. And for us, we're saying hey, we need to be able to consume all that cataloging, understand what's sensitive, and automatically apply policies to ensure that any regulation in that environment is met. >> I want to ask you about the cloud too. So much to talk to you about here, Matt. So, I also wanted to get your perspective on variances within industries. So you mentioned Chief Data Officers. The ascendancy of the Chief Data Officers started in financial services, healthcare, and government where we had highly regulation industries. And now it's sort of seeped into more commercial. But it terms of those regulated industries, take healthcare for example. There are specific nuances. Can you talk about what you're seeing in terms of industry variance. >> Yeah, it's a great point. Starting with like, healthcare. What does it mean to be HIPPA compliant anymore? There are different types of devices now where I can point it at your heartbeat from a distance away and I can have 99 percent accuracy of identifying you, right? It takes three data points in any data set to identify 87 percent of US citizens. If I have your age, sex, location, I can identify you. So, what does it mean anymore to be HIPPA compliant? So the challenge is how do we build guarantees of trust that we've de-identified these DESA's, cause we have to use it, right? No one's going to go into a hospital and say, "You know what, I don't want you to say my life. "Cause I want my data protected," right? No one's ever going to say that. So the challenges we face now across these regulated industries is the most sensitive data sets are critical for those businesses to operate. So there has to be a compromise. So, what we're trying to do in these organizations is help them leverage their data and build levels of proportionality, to access that right? So, the key isn't to stop people from using data. The key is to build the controls necessary to leverage a small bit of the data. Let's just say, we've made it indistinguishable. You can only ask Agriculture and Statistics the question. Well, you know what, we actually found some really interesting things there, we need to be a little bit more useful, it's this trade-off between privacy and utility. It's a pendulum that swings back and forth. As someone proves I need more of this, you can swing it, or just mask it. I need more of it? All right, we'll just redact some of the certain things. Nope, this is really important, it's going to save someone's life. Okay, completely unmasked, you have the raw data. But it's that control that's necessary in these environments, that's what's missing. You know, we came out of the US Intelligence community. We understood this better than anyone. Because highly regulated, very sensitive data, but we knew we needed the ability to rapidly control. Well is this just a hunch, or is this a 9-11 event? And you need the ability to switch like that. That's the difference and so, healthcare is going through a change of, we have all these new algorithms. Like Facebook the other day said, hey, we have machine learning algorithms that can look at MRI scans, and we're going to be better than anyone in the world at identifying these. Do you feel good about giving your data to Facebook? I don't know, but we can maybe provide guaranteed anonymization to them, to prove to the world they're going to do right. That's where we have to get to. >> Well, this is huge, especially for the consumer, cause you just gave several examples. Facebook's going to know a lot about me, a mobile device, a Fit Bit, and yet, if I want to get access to my own medical records, it's like Fort Knox to try to get, please, give this to my insurance company. You know, you got to go through all these forms. So, you've got those diverging objectives and so, as a consumer, I want to be able to trust that when I say yes you can use it, go, and I can get access to it, and other can get access to it. I want to understand exactly what it is that you guys do, what you sell. Is it software, is it SAS, and then let's get into how it works. So what is it? >> Yeah, so we're a software platform. We deploy into any infrastructure, but it is not multi-tenant so, we can deploy on any cloud, or on premises for any customer, and we do that with customers across the world. But if you think about at the core of what is Immuta, think of Immuta as a system of record for the CISO or the line of business where I can connect to any data, on any infrastructure, on any compute layer, and we connect into over 61 different storage platforms. We then have built a UI where lawyers... We actually have three lawyers as employees that act as product managers to help any lawyer of any stature take what's on paper, these regulations, these rules and policies, and they digitize it essentially, in active code. So they can build any policy they want on any data in the ecosystem, in the enterprise, and enforce it globally without having to write any code. And then because we're this plane where you can connect any tool to this data, and enforce any regulation because we're the man in the middle, we can audit who is using what data and why. In every action, in any change in policy. So, if you think about it, it's connect any tool to any data, control it, any regulation, and prove compliance in a court of law. >> So you can set the policy at the data set level? >> Correct. >> And so, how does one do that? Can you automate that on the creation of that data set? I mean you've got you know, dependencies. How does that all work? >> Yeah, what's a really interesting part of our secret sauce is that one, we could do that at the column level, we can do it at the row level, we can do it at the cell level. >> So very granular. >> Very, very granular. This is something again, we learned from the US Intelligence community, that we have to have very fine grained access to every little bit of the data. The reason is that, especially in the age of data, is people are going to combine many data sets together. The challenge isn't enforcing the policy on a static data set, the challenge is enforcing the policy across three data sets where you merge three pieces of data together, who have conflicting policies. What do you do then? That's the beauty of our system. We deal with that policy inheritance, we manage that lineage of the policy, and can tell you here's what the policy will be. >> In other words, you can manage to the highest common denominator as an example. >> Or we can automate it to the lowest common denominator, where you can work in projects together recognizing hey, we're going to bring someone into the project that's not going to have the level of access. Everyone else will automatically change it to the lowest common denominator. But then you share that work with another team and it'll automatically be brought to the highest common denominator. And we've built all these work flows in. That was what was missing and that's why I call it a system of record. It's really a symbiotic relationship between IT, the data owner, governance, the CISO, who are trying to protect the data, and the consumer, and all they want to do is access the data as fast as possible to make better, more informed decisions. >> So the other mega-trend you have is obviously, the super power of machine intelligence, or artificial intelligence, and then you've got edge devices and machine to machine communication, where it's just an explosion of IP addresses and data, and so, it sounds like you guys can attack that problem as well. >> Any of this data coming in on any system, the idea is that eventually it's going to land somewhere, right? And you got to protect it. We call that like rogue data, right? This is why I said earlier, when we talk about data, we have to start thinking about it as it's not in some building anymore. Data's everywhere. It's going to be on a cloud infrastructure, it's going to be on premises, and it's likely, in the future, going to be on many distributed data centers around the world cause business is global. And so, what's interesting to us is no matter where the data's sitting, we can protect it, we can connect to it, and we allow people to access it. And that's the key thing is not worrying about how to lock down your physical infrastructure, it's about logically separating it. And that's why what differentiates us from other people is one, we don't copy the data, right? That's the always the barrier for these types of platforms. We leave the data where it is. The second is we take all those regulations and we can actually, at query time, push it down to where that data is. So rather than bring it to us, we push the policy to the data. And what that does is that's what allows us, what differentiates us from everyone else is, it allows us to guarantee that protection, no matter where the data's living. >> So you're essentially virtualizing the data? >> Yeah, yeah. It's virtual views of data, but it's not all the data. What people have to realize is in the day of apps, we cared about storage. We put all the data into a database, we built some services on top of it and a UI, and it was controlled that way, right? You had all the nice business logic to control it. In the age of data, right? Data is the new app, right? We have all these automation tools, Data Robot, and H20, and Domino, and Tableau's building all these automation work flows. >> The robotic process automation. >> Yeah, RPA, UI Path, the Work Fusion, right? They're making it easier and easier for any user to connect to any data and then automate the process around it. They don't need an app to build a unique work flows, these new tools do that for them. The key is getting to the data. And the challenge with the supply chain of data is time to data is the most critical aspect of that. Cause, the time to insight is perishable. And so, what I always tell people, a little story, I came from the government, I worked in Baghdad, we had 42 minutes to know whether or not a bad guy in the environment, we could go after him. After that, that data was perishable, right? We didn't know where he was. It's the same thing in the real world. It's like imagine if Google told you, well, in 42 minutes it might be a good time to go 495. (laughter) It's not very useful, I need to know the information now. That's the key. What we see is policy enforcement and regulations are the key barrier of entry. So our ability to rapidly, with no latency, be able to connect anyone to that data and enforce those policies where the data lives, that's the critical nature. >> Okay, so you can apply the policies and you do it quickly, and so now you can help solve the problem. You mentioned a cloud before, or on prem. What is the strategy there with regard to various clouds and how do you approach multi-clouds? >> I think cloud is what used to be an infrastructure as a service game, is now becoming a compute game. I think large, regulated enterprises, government, healthcare, financial services, insurance, are all moving to cloud now in a different way. >> What do you mean by that? Cause people think infrastructure as service, they'll say oh that's compute storage and some networking. What do you mean by that? >> I think there's a whole new age of software that's being laid on top of the availability of compute and the availability of storage. That's companies like Databricks, companies like Snowflake, and what they're doing is dramatically changing how people interact with data. The availability zones, the different types of features, the ability to rip and replace legacy warehouses and main frames. It's changing the ability to not just access, but also the types of users that could even come on to leverage this data. And so these enterprises are now thinking through, "How do I move my entire infrastructure of data to them? "And what are these new capabilities "that I could get out of that?" Which, that is just happening now. A lot of people have been thinking, "Oh, this has been happening over the past five years," no, the compute game is now the new war. I used to think of like, Big Data, right? Big Data created, everyone started to understand, "Ah, if we've got our data assets together, "we can get value." Now they're thinking, "All right, let's move beyond that." The new cloud at our currents works is Snowflake and Databricks. What they're thinking about is, "How do I take all your meta-data "and allow anyone to connect any BI tool, "any data science tool, and provide highly performance, "and highly dependable compute services "to process petabytes of data?" It's pretty fantastic. >> And very cost efficient and being able to scale, compute independent of storage, from an architectural perspective. A lot of people claim they can do that, but it doesn't scale the same way. >> Yeah, when you're talking about... Cause that's the thing is you got to remember, these financial systems especially, they depend on these transactions. They cannot go down and they're processing petabytes of data. That's what the new war is over, is that data in the compute layer. >> And the opportunity for you is that data that can come from anywhere, it's not sitting in a God box, where you can enforce policies on that corpus. You don't know where it's coming from. >> We want to be invisible to that right? You're using Snowflake, it's just automatically enforced. You're using Databricks, it's automatically enforced. All these policies are enforced in flight. No one should even truly care about us. We just want to allow you to use the data the way you're used to using it. >> And you do this, this secret sauce you talked about is math, it's artificial intelligence? >> It's math. I wish I could say it was like super fancy, unsupervised neural nets or what not, it's 15 years of working in the most regulated, sticky environments. We learned about very simple novel ways of pushing it down. Great engineering's always simple. But what we've done is... At query time, what's really neat is we figured a way to take user attributes from identity management system and combine that with a purpose, and then what we do is we've built all these libraries to connect into all these dispert storage and compute systems, to push it in there. The nice thing about that is prior to this what people were doing, was making copies. They'd go to the data engineering team and they'd say hey, "I need to ETL this "and get a copy and it'll be anatomized." Think about that for a second. One, the load on your production systems, of all these copies, all the time, right? The second is CISO, the surface area. Now you've got all this data that in a snapshot in time, is legal and ethical, might change tomorrow. And so, now you've got an increase surface area of risk. Like that no-copy aspect. So the pushing it down and then the no-copy aspect really changed the game for enterprises. >> And you've got providence issues, like you say. You've got governance and compliance. >> And imagine trying, if someone said to you, imagine Congress said hey, "Any data source that you've processed "over the past five years, I want to know if "there was these three people in any of these data sources "and if there were, who touched that data "and why did they touch it?" >> Yeah and storage is cheap, but there's unintended consequences. People are, management isn't. >> We just don't have a unified way to look at all of the logs cross listed. >> So we started to talk about cloud and then I took you down a different path. But you offer your software on any cloud, is that right? >> Yeah, so right now, we are in production on Immuta's Marketplace. And that is a managed service, so you can go deploy in there, it'll go into your VPC, and we can manage the updates for you, we have no insight into your infrastructure, but we can push those updates, it'll automatically update, so you're getting our quarterly releases, we release every season. But yeah, we started with AWBS, and then we will grow out. We see cloud is just too ubiquitous. Currently, we still support though, Bigquery, Data Praq, we support Azure, Data Light Storage version two, as well as Azure Databricks. But you can get us through Immuta's Marketplace. We're also investing in ReInvent, we'll be out there in Vegas in a couple weeks. It's a big event for us just because obviously, the government has a very big stake in AWBS, but also commercial customers. It's been a massive endeavor to move. We've seen lots of infrastructure. Most of our deals now are on cloud infrastructure. >> Great, so tell us about the company. You've raised, I think in a Series B, about 28 million to date. Maybe you could give us the head count, and whatever you can share about momentum, maybe customer examples. >> Yeah, so we've raised 32 million to date. >> 32 million. >> From some great investors. The company's about 70 people now. So not too big, but not small anymore. Just this year, at this point, I haven't closed my fiscal year, so I don't want to give too much, but we've doubled our ARR and we've tripled our LOGO count this year alone and we've still got one more quarter here. We just started our fourth quarter. And some customer cases, the way I think about our business is I love healthcare, I love government, I love finance. To give you some examples is like, COGNO is a really great example. COGNO and what they're trying to solve is can they predict where a child is on the autism spectrum? And they're trying to use machine learning to be able to narrow these children down so that they can see patterns as to how a provider, a therapist is helping these families give these kids the skills to operate in the real world. And so it's like this symbiotic relationship utilizing software, surveys and video and what not, to help connect these kids that are in similar areas of the spectrum, to help say hey, this is a successful treatment, right? The problem with that is we need lots of training data. And this is children, one, two, this is healthcare, and so, how do you guarantee HIPPA compliance? How do you get through FDA trials, through third party, blind testing? And still continue to validate and retrain your models, while protecting the identity of these children? So we provide a platform where we can anonymize all the data for them, we can guarantee that there's blind studies, where the company doesn't have access to certain subsets of the data. We can also then connect providers to gain access to the HIPPA data as needed. We can automate the whole thing for them. And they're a startup too, there are 100 people. But imagine if you were a startup in this health-tech industry and you had to invest in the backend infrastructure to handle all of that. It's too expensive. What we're unlocking for them, I mean yes, it's great that they're HIPPA compliant and all that, that's what we want right? But the more important thing is like, we're providing a value add to innovate in areas utilizing machine learning, that regulations would've stymied, right? We're allowing startups in that ecosystem to really push us forward and help those families. >> Cause HIPPA compliance is table stay compulsory. But now you're talking about enabling new business models. >> Yeah, yeah exactly. >> How did you get into all this? You're CEO, you're business savvy, but it sounds like you're pretty technical as well. What's your background? >> Yeah I mean, so I worked in the intelligence community before this. And most of my focus was on how do we take data and be able to leverage it, either for counter-terrorism missions, to different non-kinetic operations. And so, where I kind of grew up in is in this age of, think about billions of dollars in Baghdad. Where I learned is that through the computing infrastructure there, everything changed. 2006 Baghdad created this boom of technology. We had drones, right? We had all these devices on our trucks that were collecting information in real time and telling us things. And then we started building computing infrastructure and it burst Hadoop. So, I kind of grew up in this era of Big Data. We were collecting it all, we had no idea what to do with it. We had nowhere to process it. And so, I kind of saw like, there's a problem here. If we can find the unique little, you know, nuggets of information out of that, we can make some really smart decisions and save lives. So once I left that community, I kind of dedicated myself to that. The birth of this company again, was spun out of the US Intelligence community and it was really a simple problem. It was, they had a bunch of data scientists that couldn't access data fast enough. So they couldn't solve problems at the speed they needed to. It took four to six months to get to data, the mission said they needed it in less than 72 hours. So it was orthogonal to one another, and so it was very clear we had to solve that problem fast. So that weird world of very secure, really sensitive, but also the success that we saw of using data. It was so obvious that we need to democratize access to data, but we need to do it securely and we need to be able to prove it. We work with more lawyers in the intelligence community than you could ever imagine, so the goal was always, how do we make a lawyer happy? If you figure that problem out, you have some success and I think we've done it. >> Well that's awesome in applying that example to the commercial business world. Scott McNeely's famous for saying there is no privacy in the internet, get over it. Well guess what, people aren't going to get over it. It's the individuals that are much more concerned with it after the whole Facebook and fake news debacle. And as well, organizations putting data in the cloud. They need to govern their data, they need that privacy. So Matt, thanks very much for sharing with us your perspectives on the market, and the best of luck with Immuta. >> Thanks so much, I appreciate it. Thanks for having me out. >> All right, you're welcome. All right and thank you everybody for watching this Cube Conversation. This is Dave Vellante, we'll see ya next time. (digital music)

(upbeat music) >> Announcer: Live from Miami, Florida. It's theCUBE. Covering IBM's Data and AI Forum. Brought to you by IBM. >> We're back in Miami everybody. You're watching theCube, the leader in live tech coverage. We go out to the events and extract the signal from the noise we hear. Covering the IBM Data and AI Forum, John Thomas is here, many time CUBE guest. He's not only a distinguished engineer but he's also the chief data scientist for IBM Data and AI. John, great to see you again. >> Great to see you again Dave. >> I'm always excited to talk to you because you're hard core data science. You're working with the customers and you're kind of where the action is. The watchword today is end to end data science life cycle. What's behind that? I mean it's been a lot of experimentation, a lot of tactical things going on. You're talking about end to end life cycle, explain. >> So Dave, what we are saying in our client engagements is, actually working with the data, building the models. That part is relatively easy. The tougher part is to make the business understand what is the true value of this. So it's not a science project, right? It is not a, an academic exercise. So how do you do that? In order for that to happen these models need to go into production. Well, okay, well how do you do that? There is this business of, I've got something in my development environment that needs to move up through QA and staging, and then to production. Well, lot of different things need to happen as you go through that process. How do you do this? See this is not a new paradigm. It is a paradigm that exists in the world of application development. You got to go through a dev ops life cycle. You got to go through continuous integration and continuous delivery mindset. You got to have the same rigor in data science. Then at the front end of this is, what business problem are you actually solving? Do you have business KPIs for that? And when the model is actually is in production, can you track, can you monitor the performance of the model against the business KPIs that the business cares about? And how do you do this on an end to end fashion? And then in there is retraining the model when performance degrades, et cetera, et cetera. But this notion of following dev ops mindset in the world of data science is absolutely essential. >> Dave: So when you think about dev ops, you think of agile. So help me square this circle, when you think end to end data life cycle, you think chewy, big, waterfall, but I'm inferring you're not prescribing a waterfall. >> John: No, no, no. >> So how are organizations dealing with that wholistic end to end view but still doing it in an agile manner? >> Yeah, exactly. So, I always say do not boil the ocean, especially if you're approaching AI use cases. Start with something that is convened, that you can define and break it into springs. So taking an agile approach to this. Two, three springs, if you're not seeing value in those two, three springs, go back to the drawing board and see what is it that you're doing wrong. So for each of your springs, what is the specific successful criteria that you care about and the business cares about? Now, as you go through this process, you need a mechanism to look at, okay, well I've got something in development, how do I move the assets? Not just the model, but, what is the set of features that you're working with? What is the data prep pipeline? What are the scripts being used to evaluate the model? All of these things are logical assets surrounding the model. How do you move them from development to staging? How do you do QA against these set of assets? Then how do you do third party approval oversight? How do you do code review? How do make sure that when you move these assets all of the surrounding mechanisms are being adhered to, compliance requirements, regulatory requirements? And then finally get them to production. So there's a technology aspect of it, obviously. You have a lot of discussion around cube flow, ml flow, et cetera, et cetera as technology options. But there is also mindset that needs to be followed here. >> So once you find a winner, business people want a scale, 'cause they can make more money the more and more times they can replicate that value. And I want to understand this trust and transparent, 'cause when you scale, if you're scaling things that aren't compliant, you're in trouble. But before we get there, I wonder if we can take an example of, pick an industry, or some kind of use case where you've seen this end to end life cycle be successful. >> Yeah, across industries. I mean it's not just specific industry related. But, I'll give you an example. This morning Wunderman Thompson was talking about how they are applying machine learning to, a very difficult problem, which is how to improve how they create a first-time buyer list for their clients. But think of the problem here. It's not just about a one time building of a model. The model needs, okay you got data, understand what data says you're working with, what is the lineage of that data. Once I have their understanding of their data then I get into feature selection, feature engineering, all the steps that I need in your machine learning cycle. Once I am done with selecting my features, doing my feature engineering, I go into model building. Now, it's a pipeline that is being built. It is not a one time activity. Once that model, the pipeline has been vetted, you got to move it from development to your QA environment, from there to your production environment, and so on. And here comes, and this is where it links to the question, transparency discussion. Well the model is in production, how do I make sure the model is being fair? How do I make sure that I can explain what is going on? How do I make sure that the model is not unfairly biased? So all of these are important discussions in the trust and transparency because, you know, people are going to question the outcome of the model. Why did it make a decision? If a campaign was run for an end individual, why did you choose him and not somebody else? If it's a credit card fraud detection scenario, why was somebody tagged as fraudulent and not the other person? If a loan application was rejected, why was he rejected and not someone else? You got to explain this. So, it's not an explain ability that Tom has a lot of, it's over loaded at times, but. The idea here is you should be able to retrace your steps back to an individual scoring activity and explain an individual transaction. You should be able to play back an individual transaction and say version 15 of my model used these features, these hundred features for it's scoring. This was the incoming payload, this was the outcome, and, if I had changed five of my incoming payload variables out of the 500 I use, or hundred I use, the outcome would have been different. Now you can say, you know what, ethnicity, age, education, gender. These parameters did play a role in the decision but they were within the fairness bracket. And the fairness bracket is something that you have to define. >> So, if I could play that back. Take fraud detection. So you might have the machine tell you with 90% confidence or greater that this is fraud but it throws back a false positive. When you dig in, you might see well there's some bias included in there. Then what? You would kind of re-factor the model? >> A couple of different things. Sometimes a bias is in the data itself and it may be valid bias. And you may not want to change that. Well, that's what the system allows you to do. It tells you, this is the kind of bias that exists in the data already. And you can make a business decision as to whether it is good to retain that bias or to correct it in the data itself. Now, if the bias is in how the algorithm is processing their data, again, it's a business decision. Should I correct it or not. Sometimes, bias is not a bad thing. (laughs) It's not a bad thing. No, because, you are actually looking at what signal exists in their data. But what you want to make sure is that it's fair. Now what is fair, that is up to the regulatory body. Are your business defined? You know what, age range between 26 and 45, I want to treat them a certain way. If this is a conscious decision that you, as a business, or your industry is making, that's fair game. But if it is, this is what I wanted that model to do for this age range but the model is behaving a different way, I want to catch that. And I want to either fix the bias in the data or in how the algorithm is behaving with the model itself. >> So, you can eject the edits of the company into the model, but then, and then appropriately and fairly apply that, as long as it doesn't break the law. >> Exactly. (laughs) >> Which is another part of the compliance. >> So, this is not just about compliance. Compliance is a big, big part here. But, this also just answering what your end customer is going to ask. I put in an application for a loan and I was rejected. And, I want an explanation as to why it was rejected, right? >> So you got to be transparent, is your point there. >> Exactly, exactly. And if the business can say, you know what, this is the criteria we used, you fell in this range, and this, in our mind, is a fair range, that is okay. It may not be okay for the end customer but at least you have a valid explanation for why the decision was made by the model. So, it's some black box making some.. >> So the bank might say, well, the decision was made because we don't like the location of the property, we think they're over valued. It had nothing to do with your credit. >> John: Exactly. >> We just don't want to invest in this, by the way, maybe we advise you don't invest in that either. >> Right, right, right. >> So that feedback loop is there. >> This is, being able to find it for each individual transaction, each individual model scoring. What weighed in into the decision that was made by the model. This is important. >> So you got to have atomic access to that data? >> John: At the transaction level. >> And then make it transparent. Are organizations, banks, are they actually making it transparent to their consumers, 'cause I know in situations that I'm involved in, it's either okay go or no but, we're not going to tell you why. >> Everyone is beginning to look into this place. >> Healthcare is another one, right, where we would love more transparency in healthcare. >> Exactly. So this is happening. This is happening where people are looking at oh we can't do just black box in decision making, we have to get serious about this. >> And I wonder, John, if a lot of that black box decision making is just easy to not share information. Healthcare, you're worried about HIPPA. Financial services is just so highly regulated so people are afraid to actually be transparent. >> John: Yup. >> But machine intelligence potentially solves that problem? >> So, internally, at least internal to the company, when the decision is made, you need to have a good idea why the decision was made, right. >> Yeah right. >> As to what you use to explain to the end client or to regulatory body, is up to you. At least internally you need to have clarity on how the decision was arrived at. >> When you were talking about feature selection and feature engineering and model building, how much of that is being done by AI or things like auto AI? >> John: Yup >> You know, versus humans? >> So, it depends. If it's a relatively straightforward use case, you're dealing with 50, maybe a hundred features. Not a big deal. I mean, a good data scientist can sit down and do that. But, again, I'm going back to the Wunderman Thomas example from this morning's keynote, they're dealing with 20,000 features. You just, that is, you just can't do this economically at scale with a bunch of data scientists, even if they're super data scientists doing this in a programmatic way. So this is where something like auto AI comes into play and says, you know what, out of this 20,000 plus feature set, I can select, no. This percentage, maybe a thousand or 2,000 features that are actually relevant. Two, now here comes interesting things. Not just that it has selected 2,000 features out of 20,000, but it says, if I were to take three of these features and two of these features and combine them. Combine them, maybe to do a transpose. Maybe do an inverse of one and multiply it with something else or whatever, right. Do a logarithm make approach to one and then combine it with something else, XOR, whatever, right. Some combination of operations on these features generates a new feature which boosts the signal in your data. Here is the magic, right. So suddenly you've gone from this huge array of features to a small subset and in there you are saying, okay, if I were to combine these features I can now get much better productivity, prediction power for my model. And that is very good, and auto AI is very heavily used in the Wunderman example. In scenarios like that where you have very large scale feature selection, feature engineering. >> You guys use this concept of the data ladder, collect, organize, analyze, and infuse. Correct me if I'm wrong, but a lot of data scientists times is spent collecting, organizing. They want to do more analysis and so ultimately they can infuse. Talk about that analyze portion and how to get there? What kind of progress the industry, generally and IBM is making to help data scientists? >> So analyzers typically.. You don't jump into building machine learning models. The first part is to just do explore re-analysis. You know, age old exploration of your data to understand what is there. I mean people jump into the exhibit first and it's normal, but if you don't understand what your data is telling you, it is foolish to expect magic to happen from your data. So, explorate reanalysis, your traditional approaches. You start there. Then you say, in that context I think I can do model building to solve a particular business problem and then comes the discussion, okay am I using neural nets or am using classical mechanisms, am I doing this framework, XGBoost or Tensorflow? All of that is secondary once you get to explorate reanalysis, looking at framing the business problem as a set of models that can be built, then say what technique do I use now. And auto AI, for example, will help you select the algorithms once you have framed the problem. It's says, should I use lite GBN? Should I use something else? Should I use logistic regression? Whatever, right. So, it is something that the algorithm selection can be helped by auto AI. >> John, we're up against the clock. Great to have you. A wonderful discussion Thanks so much, really appreciate it. >> Absolutely, absolutely. >> Good to see you again. >> Yup, same here. >> All right. Thanks for watching everybody. We'll be right back right after this short break. You're watching theCUBE from the IBM Data and AI Forum in Miami. We'll be right back. (upbeat music)

live from Las Vegas it's the cube covering AWS reinvent 2018 brought to you by Amazon Web Services Intel and their ecosystem partners hey welcome back everyone we're live here in Las Vegas with AWS Amazon webster's reinvent our 6th year I'm Jeff our table what they did six years two sets people rolling out of the keynote so much action we got another day coming tomorrow they're two great guests here we got dr. feci philomon is the general manager the machine learning and AI at Amazon Web Services and dr. Taha costs senior leader at healthcare and AI at Amazon guys welcome to the cube Thank You thanks itíd that you're here because I've been waiting to have this conversation Dave and I have been we just had an analysis of the distractions and glued up the stack around machine learning so much value now coming online that's been in the works around AI are really mainly machine learning that's creating a I like benefits and II just had to spend a lot of time with key nuts they almost a third of it around a I like capabilities and how Amazon integrates in from you know chipsets with elastic inference beautiful it's just good stuff so congratulations so what does it mean what does it mean for customers right now who want to kind of grok what's going on with Amazon and AI is that new sense the services coming online is that how long has been the works explaining yeah our mission at AWS has always been to take technologies that have been traditionally available for a few special technology companies and take that and make it available to all developers and we've done that I should say that we've done that fairly well when it comes to compute when it comes to storage when it comes to databases the analytics and we're doing the same thing for machine learning and AI and what we're doing because it's a new field is we've got to innovate at three layers of our stack to the bottom most layer as you saw in the keynote earlier has to do with frameworks and infrastructure so this is more for the people that fully understand how to deal with machine learning models and like to go in and tweak these models the middle layer then is for everyday developers and the data scientists and that's sort of where sage maker fits in and finally at the top layer of the stack is where we have our application services and this is meant for developers that don't want to get into the weeds of machine learning but they still want to use make use of all of these technologies to make their applications more smarter so they get the insight benefits get the insights have the day that without getting in town on the weeds exactly who want to get down in the weeds you can get down and dirty with all this other stuff yeah look at that right yeah and typically what we do with the top layer of the stack as we try and solve really hard problems and so customers can now take advantage of it because we've solved it for them and they can just take that and integrate it into their Apple quick what what's the hardest problem that you guys solve I mean traditionally speech recognition is a very hard problem that's one of the hard problems the other one is NLP natural language processing but I would say speech recognition is probably a hard problem and we just launched streaming transcription so you can now transcribe live as somebody speaks and of course you can connect it to translate and translate it as well live so great for our cute beers looking forward to having that on as a health care practitioner how does this all apply to that industry what kind of projects are you guys working on in that regard of course yeah so I mean to to posses point is want to continue to innovate on behalf of the customers across all layers of the stack machine learning in particular this week we launched Amazon comprehend medical particularly in a hardier heart problem where the majority of healthcare data is captured conversation and observations and unstructured formality so petabytes of data is stored across entire healthcare system that's a nun structure for form so to drive actionable insights and to be able to find the right elements to treat patients or to manage a population or even to do accurate billing it's been really an important that we can empower our customers with building blocks for them to build the right solutions to take advantage of that so Amazon comprehend Medical is able to understand the medical language and the context similar how clinicians understand the medical language and context for example if you're looking at a patient medical note Amazon campaign medicals able to with high accuracy extract medical conditions medications tests procedures being done on the patients as well as the relationship between those and understanding that context at this condition and this treatment go together as well as the nuances for example you know a patient has no family history of X or there's no smoking history all those are things in relation in the past or in the future or other members and this is really what we're really proud about launched an Amazon comprehend medical talk about how it works because you know I Healthcare has been a great field around where a is old-fashioned a is a queer when I wasn't doing it in the 80s early 90s ontologies were really popular and it's linguistics is kind of known but now that but you need that linguistics guru to do that he mentioned streaming the transcribed got metadata how do you guys get this kind of benefit when the balls moving so fast around these rapidly changing and verticals like healthcare because healthcare is got a big problem like other verticals where it's too many notifications what I pay attention to so much data how do you put the puzzle together let me first give you some context here as you probably we're at last reinvent we launched Amazon comprehend right comprehend is a text analytics service it helps you look into text and understand what's in there right we started out with general things that we could detect like people places things sentiment the language the text is written in and so on but when we started customers are picked on it and they're using it a lot but as they keep using it they came back to us and said hey it's great that you guys have this this you're giving us the capability to understand general language but some of our domains have some special language like jargon like yeah like take the legal domain for example right it's got charges and defendants and very particular things that are very relevant to the legal domain so they were asking us for a capability to sort of extend the comprehend to include their custom domain terms and phrases as well right so last week we actually launched a custom custom entities feature that allows them to bring in their custom domain into comprehend so the comprehend be extended to include their domain the so legal language is difficult to understand but medical language on the other hand is even more harder to understand that quick right acronyms jargon absolutely what is an entity looks like extracting that and extracting it uses alone yeah miss spells right but relating those entities together is super important because you could in one clinical note you could have multiple drugs in there with different dosages different frequencies and so you need to be able to relate those entities together right and that's the sort of thing that comprehend Medical allows our customers to do to solve some really so you're doing one of that entity extraction is under the covers is that right has it were I mean how does comprehending the medical work I mean just out of the box you have to train it there's no training meet needed know machine learning expertise needed so the algorithm extract these entities as well as the relationship between those entities and then also extracts any attributes that might be related such as negation or past and future or what's anatomy of the body relates one now all that is done out of the box and that's super important you want to know whether the patient's stopped taking a medication right yeah so negation things like that you want to know because that gives you the context just getting the terms alone doesn't really tell you much it each has had a great video about the f1 point of ethics imagine that for personal that's right you're not doing good right now take a break yeah so I feel like we're kind of now scratching the service of stress in the surface of health care yeah information yeah think about the health care industry for years it's been compliance-driven yeah whether it's hip Affordable Care Act yeah EMR and meaningful use right but the industry hasn't been you know dramatically transformed and disrupted and it kind of needs to be yeah how do you guys see that evolving I feel like you're now beginning to see that see change and that's going to take a while it's a high-risk business obviously but what's your sort of prognosis for that transformation and what's the vision as to the outcome yes now that's a really great question I mean one thing I mean one great things happen over the last decade is the digitization of your medical record so and that's really wonderful because before was all paper-based primarily unless you were an acute setting so now the majority of the US for example and globally there's this huge adopt adoption and propagation of these electronic medical records the issue there remains now when the majority of that data is observations and conversations as well as unstructured that that creates a different kind of roadblock for our customers and this is what we're hoping for service like Amazon comprehend medical that's HIPPA eligible means a lot of the early the compliance or help our customer meet their compliance needs that we'll be able to remove the heavy lifting of this undepreciated task about you know having in a large amount of time being spent on analyzing this text and extracting very low we're now with Amazon company and medical be able to really fast track that and be able to elevate it hit the nail on the head of the undifferentiated heavy lifting right that's the ethos of DevOps is that yeah let me give you some stats actually there are one point two billion medical documents that are generated every year in the US and 80% of them it's unstructured text so to make sense of that it's going to enable our customers to do some really amazing things one of the things one of the use cases that we see is its clinical trial recruitment so Fred Hutchinson which is one of the yeah the nation's top cancer research centers they recruit patients for clinical trials if you go to clinical trials.gov you'll see like 290 thousand four and 50 clinical trials open and typically from history we know that most of these clinical trials don't end up recruiting they don't end up meeting their recruiting goals because it's very hard to figure out which patients fit the clinical trial that you're actually trying to perform so comprehend medical helps these customers to very quickly narrow it down expand on the involvement of people in the community mentioned Fred hutch Roach has also been involved what I heard yeah what who was involved in this project sound it was a collaboration take a minute to explain that right I mean it's very similar to a lot of other services that we put it into the market we collaborate a lot with customers 90% of what we do is really coming from customers so we've collaborated with people like Fred hutch and some of the nation's top institutions to help us validate the service that we've built to actually make sure that its meeting sort of the requirements for those use cases that they are thinking of so we collaborate closely with them to get the service to where this today and we announced it as generally available yesterday ok so what's the use case I'll go ahead yeah I can expand a little bit some of the customers as well their use cases we're talking anywhere from hospital systems that when I use or take advantage of their unstructured text for things such as identify people who are for their follow-up appointments or stopping treatments or find an alternative routes to billers we're trying to identify it is accurate procedures were done if we account for all the procedures or care for all the billing which often time is hidden in those unstructured text and require a lot of manual process and often time the rules that can't really scale to things such as clinical trials recruitment how can you if example in Fred Hutchinson Cancer Institute use case for identify a patient and match them to the right clinical trial these patients often time have Harry Potter's worth of clinical notes down on the minute their longitudinal journey and to go from one institution another another and be able to really find it's no longer needed a haystack it's like a needle in the bottom of Atlantic Ocean and then be able to really do that match from hours and months down to a few seconds and that's really the beauty about the service John likes to talk about the 20 mile stare and I wonder if we could just look ahead how far can we take AI and machine learning in in healthcare and how far should we take it and maybe a more specific question as as a practitioner you know when do you think machines might make better diagnosis than doctors if ever how do you feel about that where do you see this all going I think I mean the whole idea about machine learning the beauty about it I mean the seta scope was introduced or how the thermometer was introduced in medicine and these are tools that we use to our advantage to really provide better care and and better outcomes and that's really what we're that's the mission that our health IT and customers and wanna are really driving tower's machine learning can do a lot of great things for routine things that human being can't can go and focus their attention to other things such as the Fred Hutchinson instead of going and mining these diagnoses in mountain amounts of data a machine learning will be able to identify that with a clinical staff can focus on care and that's really where I think I mean over the next decade and so we can see a lot of this advancement in in these building blocks as well as what Amazon's offering from forecasting and prediction algorithms Rana will be able to find you know fine-tune our capabilities to help customers achieve even precision medicine real-world impact because you're changing the workflow I mean someone's within the wrong line or the wrong process based upon their history yeah HIPPA HIPPA requirements really cause a lot of this record sharing thing to be a problem from what we've been reporting over the years it's kind of a solution to that so if I move to a service medical service I get all that records with me it's just kind of how you see going and how does other regulations that are holding you back that are blockers is that clear now how does that solve the industry challenge it's of privacy and if you look at the healthcare system today there are lots of inefficiencies in there right in the end this is all about improving patient outcomes and making sure that we reduce costs and that's what this boils down to and these are tools that allow our customers to do exactly that well guys thanks for sharing this insight comprehend medicals really awesome opportunities I think it's early days day one is you guys think right I think there's so much more that could be there I'd love to see the industry just from the personal is decided change it's just get out of the way of all these pretty broad hurdles get the data out there expose the data check the privacy box would be good right this is gonna change the game yeah maybe we should say a little bit about the how we built the service in terms of that right as you know at AWS security and privacy is number one for us right so this service is HIPAA eligible it's a stateless service what that means is nothing gets stored this is not the data is not used to improve the models or anything like that the only person that can actually see the data is the customer he's got the keys he's the only one that's sending the data to the endpoint and whatever he gets back only he can decrypt it so we've taken care to make sure that we can remove some of those hurdles that people have always been worried about well doctors take you so much for sharing thank you so much for having us here we are bringing you all the action here from 80s reinvent again as the compute power is increased as software is written with new apps a eyes changing the game of course the cube a lot of video we don't need some of these services to make these transcribes on the fly they succumb and I really appreciate it you think back on the more after this short break [Music]

>> Announcer: Live from Orlando, Florida, it's theCUBE, covering .conf2018. Brought to you by Splunk. >> Welcome back to .conf2018 everybody, this is theCUBE the leader in live tech coverage. I'm Dave Vellante with Stu Miniman, wrapping up day one and we're pleased to have Bina Khimani, who's the global head of Partner Ecosystem for the infrastructure segments at AWS. Bina, it's great to see you, thanks for coming on theCUBE. >> Thank you for having me. >> You're very welcome. >> Pleasure to be here. >> It's an awesome show, everybody's talking data, we love data. >> Yes. >> You guys, you know, you're the heart of data and transformation. Talk about your role, what does it mean to be the global head Partner Ecosystems infrastructure segments, a lot going on in your title. >> Yes. >> Dave: You're busy. (laughing) >> So, in the infrastructure segment, we cover dev apps, security, networking as well as cloud migration programs, different types of cloud migration programs, and we got segment leaders who really own the strategy and figure out where are the best opportunities for us to work with the partners as well as partner development managers and solution architects who drive adoption of the strategy. That's the team we have for this segment. >> So everybody wants to work with AWS, with maybe one or two exceptions. And so Splunk, obviously, you guys have gotten together and formed an alliance. I think AWS has blessed a lot of the Splunk technology, vice versa. What's the partnership like, how has it evolved? >> So Splunk has been an excellent partner. We are really joined hands together in many fronts. They are fantastic AWS marketplace partner. We have many integrations of Splunk and AWS services, whether it is Kinesis data, Firehose, or Macy, or WAF. So many services Splunk and AWS really are well integrated together. They work together. In addition, we have joined go to market programs. We have field engagement, we have remand generation campaigns. We join hands together to make sure that our customers, joint customers, are really getting the best value out of it. So speaking of partnership, we recently launched migration program for getting Splunk on prem, Splunk Enterprise customers to Splunk Cloud while, you know, they are on their journey to Cloud anyway. >> Yeah, Bina let's dig into that some, we know AWS loves talking about migrations, we dig into all the databases that are going and we talk at this conference, you know Splunk started out very much on premises but we've talked to lots of users that are using the Cloud and it's always that right. How much do they migrate, how much do they start there? Bring us instead, you know, what led to this and what are the workings of it. >> So what, you know if you look at the common problems people have customers have on prem, they are same problems that customers have with Splunk Enterprise on prem, which is, you know, they are looking for resiliency. Their administrator goes on vacation. They want to keep it up and running all the time. They help people making some changes that shouldn't have been made. They want the experts to run their infrastructure. So Splunk Cloud is run by Splunk which is, you know they are the best at running that. Also, you know I just heard a term called lottery proof. So Splunk Cloud is lottery proof, what that means the funny thing is, that you know, your administrator wins lottery, you're not out of business. (laughs) At the same time if you look at the the time to value. I was talking to a customer last night over dinner and they were saying that if they wanted to get on Splunk Enterprise, for their volume of data that they needed to be ingested in Splunk, it would take them six months to just get the hardware in place. With Splunk Cloud they were running in 15 minutes. So, just the time to value is very important. Other things, you know, you don't need to plan for your peak performance. You can stretch it, you can get all the advantages of scalability, flexibility, security, everything you need. As well as running Splunk Cloud you know you are truly cost optimized. Also Splunk Cloud is built for AWS so it's really cost optimized in terms of infrastructure costs, as well as the Splunk licensing cost. >> Yeah it's funny you mentioned the joke, you know you go to Splunk cloud you're not out of a job, I mean what we've heard, the Splunk admins are in such high demand. Kind of running their instances probably isn't, you know a major thing that they'd want to be worrying about. >> Yes, yes, so-- >> Dave: Oh please, go. >> So Splunk administrators are in such a high demand and because of that, you know, not only that customers are struggling with having the right administrators in place, also retaining them. And when they go to Cloud, you know, this is a SAS version, they don't need administrators, nor they need hardware. They can just trust the experts who are really good at doing that. >> So migrations are a tricky thing and I wonder if we can get some examples because it's like moving a house. You don't want to move, or you actually do want to move but it's, you have be planful, it's a bit of a pain, but the benefits, a new life, so. In your world, you got to be better, so the world that you just described of elastic, you don't have to plan for peaks, or performance, the cost, capex, the opex, all that stuff. It's 10 X better, no debate there. But still there's a barrier that you have to go through. So, how does AWS make it easier or maybe you could give us some examples of successful migrations and the business impact that you saw. >> Definitely. So like you said, right, migration is a journey. And it's not always easy one. So I'll talk about different kinds of migration but let me talk about Splunk migration first. So Splunk migration unlike many other migration is actually fairly easy because the Splunk data is transient data, so customers can just point all their data sources to Splunk Cloud instead of Splunk Enterprise and it will start pumping data into Splunk Cloud which is productive from day one. Now if some customers want to retain 60 to 90 days data, then they can run this Splunk Enterprise on prem for 60 more days. And then they can move on to Splunk Cloud. So in this case there was no actual data migration involved. And because this is the log data that people want to see only for 60 to 90 days and then it's not valuable anymore. They don't really need to do large migration in this case it's practically just configure your data sources and you are done. That's the simplest part of the migration which is Splunk migration to Splunk Cloud. Let's talk about different migrations. So... you have heard many customers, you know like Capital One or many other Dow-Jones, they are saying that we are going all in on AWS and they are shutting down their data centers, they are, you know, migrating hundreds of thousands of applications and servers, which is not as simple as Splunk Cloud, right? So, what AWS, you know, AWS does this day in and day out. So we have figured it out again and again and again. In all of our customer interactions and migrations we are acquiring ton of knowledge that we are building toward our migration programs. We want to make sure that our customers are not reinventing the wheel every time. So we have migration programs like migration acceleration program which is for custom large scale migrations for larger customers. We have partner migration programs which is entirely focused on working with SI partners, consulting partners to lead the migrations. As well as we're workload migration program where we are standardizing migrations of standard applications like Splunk or Atlassian, or many of their such standard applications, how we can provide kind of easy button to migrate. Now, when customers are going through this migration journey, you know, it's going to be 10 X better like you said, but initially there is a hump. They are probably needing to run two parallel environments, there is a cost element to that. They are also optimizing their business processes there is some delay there. They are doing some technical work, you know, discovery, prioritization, landing zone creations, security, and networking aspects. There are many elements to this. What we try to do is, if you look at the graph, their cost is right now where this and it's going to go down but before that it goes up and then goes down. So what we try to do is really provide all the resources to take that hump out in terms of technical support, technical enablement, you know, partner support, funding elements, marketing. There are all types of elements as well as lot of technical integrations and quick starts to take that hump out and make it really easy for our customers. >> And that was our experience, we're Amazon customer and we went through a migration about, I don't know five or six years ago. We had, you know, server axe and a cage and we were like, you know, moving wires over and you'd get an alert you'd have to go down and fix things. And so it took us some time to get there, but it is 10 X better now though. >> It is. >> The developers were so excited and I wanted to ask you about, sort of the dev-ops piece of it because that's really, it became, we just completely eliminated all the operational pieces of it and integrated it and let the developers take care of it. Became, truly became infrastructure as code. So the dev-ops culture has permeated our small organization, can't imagine the impact on a larger company. Wonder if you could talk about that a little bit. >> Definitely. So... As customers are going through this cloud migration journey they are looking at their entire landscape of application and they're discovering things that they never did. When they discover they are trying to figure out should I go ahead and migrate everything to AWS right now, or should I a refactor and optimize some of my applications. And there I'm seeing both types of decisions where some customers are taking most of their applications shifting it to cloud and then pausing and thinking now it is phase two where I am on cloud, I want to take advantage of the best of the breed whatever technology is there. And I want to transform my applications and I want to really be more agile. At the same time there are customers who are saying that I'm going to discover all my workload and applications and I'm going to prioritize a small set of applications which we are going to take through transformation right now. And for the rest of it we will lift and shift and then we will transform. But as they go through this transformation they are changing the way they do business. They are changing the way they are utilizing different technology. Their core focus is on how do I really compete with my competition in the industry and for that how can IT provide me that agility that I need to roll out changes in my business day in day out. And for that, you know, Lambda, entire code portfolio, code build, code commit, code deploy, as well as cloud trail, and you know all the things that, all the services we have as well as our partners have, they provide them truly that edge on their industry and market. >> Bina, how has the security discussion changed? When Stu and I were at the AWS public sector summit in June, the CIO of the CIA stood up on stage in front of 10,000 people and said, "The cloud on my worst day from a security perspective "is better than my client server infrastructure "on a best day." That's quite an endorsement from the CIA, who's got some chops in security. How has that discussion changed? Obviously it's still fundamental, critical, it's something that you guys emphasize. But how has the perception and reality changed over the last five years? >> Cloud is, you know, security in cloud is a shared responsibility. So, Amazon is really, really good at providing all the very, very secure infrastructure. At the same time we are also really good at providing customers and business partners all of the tools and hand-holding them so that they can make their application secure. Like you said, you know, AWS, many of the analysts are saying that AWS is far more secure than anything they can have within their own data center. And as you can see that in this journey also customers are not now thinking about is it secure or not. We are seeing the conversation that, how in fact, speaking of Splunk right, one customer that I talked to he was saying that I was asking them why did you choose Splunk cloud on AWS and his take was that, "I wanted near instantaneous SOA compliant "and by moving to Splunk cloud on AWS "I got that right away." Even I'm talking to public sector customers they are saying, you know, I want fair DRAM I want in healthcare industry, I want HIPPA Compliance. Everywhere we are seeing that we are able to keep up with security and compliance requirements much faster than what customers can do on their own. >> So they, so you take care of, certainly from the infrastructure standpoint, those certifications and that piece of the compliance so the customer can worry about maybe some of the things that you don't cover, maybe some of their business processes and other documentation, ITIL stuff that they have to do, whatever. But now they have more time to do that presumably 'cause that's check box, AWS has that covered for me, right? Is that the right thinking? >> Yes, plus we provide them all the tools and support and knowledge and everything so that they, and even partner support who are really good at it so that not only they understand that the application and infrastructure will come together as entire secure environment but also they have everything they need to be able to make applications secure. And Splunk is another great example, right? Splunk helps customer get application level security and AWS is providing them infrastructure and together we are working together to make sure our customers' application and infrastructure together are secure. >> So speaking about migrations database, hot topic at a high level anyway, I wonder if you could talk about database migrations. Andy Jassy obviously talks a lot about, well let's see we saw RDS on Prim at VMworld, big announcement. Certainly Aurora, DynamoDB is one of the databases we use. Redshift obviously. How are database migrations going, what are you doing to make those easier? >> So what we do in a nutshell, right for everything we try to build a programatic reputable, scalable approach. That's what Amazon does. And what we do is that for each of these standard migrations for databases, we try to figure out, that let's take few examples, and let's figure out Play Books, let's figure out runbooks, let's make sure technical integrations are in place. We have quick starts in place. We have consulting partners who are really good at doing this again and again and again. And we have all the knowledge built into tools and services and support so that whenever customers want to do it they don't run into hiccups and they have really pleasant experience. >> Excellent. Well I know you're super busy thanks for making some time to come on theCUBE I always love to have AWS on. So thanks for your time Bina. >> Thank you very nice to meet you both. >> Alright you're very welcome. Alright so that's a wrap for day one here at Splunk .conf 2018, Stu and I will be back tomorrow. Day two more customers, we got senior executives coming on tomorrow, course Doug Merritt, always excited to see Doug. Go to siliconangle.com you'll see all the news theCUBE.net is where all these videos live and wikibon.com for all the research. We're out day one Splunk you're watching theCUBE we'll see you tomorrow. Thanks for watching. >> Bina: Thank you. (electronic music)

>> Live from Orlando, Florida. It's theCUBE. Covering Microsoft Ignite. Brought to you by Cohesity, and theCUBE's ecosystem partners. >> Welcome back everyone to theCUBE's live coverage of Microsoft Ignite here in Orlando, Florida. I'm your host, Rebecca Knight along with my cohost Stu Miniman. We're joined by Joachim Hammer, he is the Principal Product Manager at Microsoft. Thanks so much for coming on the show. >> Sure, you're welcome. Happy to be here. >> So there's been a lot of news and announcements with Azure SQL, can you sort of walk our viewers through a little bit about what's happened here at Ignite this week? >> Oh sure thing, so first of all I think it's a great time to be a customer of Azure SQL Database. We have a lot of innovations, and the latest one that we're really proud of, and we're just announced GA is SQL Managed Instance. So our family of database offers had so far a single database and then a pool of databases where you could do resource sharing. What was missing was this one ability for enterprise customers to migrate their workloads into Azure and take advantage of Azure without having to do any rewriting or refactoring and Managed Instance does exactly this. It's a way for enterprise customers to take their workloads, migrate them, it has all the features that they are used to from sequel server on-prem including all the security, which is of course as you can imagine always a concern in the cloud where you need to have the same or better security that customers are used to from on-prem, and with Managed Instance we have the security isolation, we have private IPV nets, we have all the intelligent protection that we have in Azure so it's a real package. And so this is a big deal for us, and the general purpose went GA yesterday actually, so I heard. >> Security's really interesting 'cause of course database is at the core of so many customer's businesses. You've been in this industry for a while, what do you see from customers as to the drivers and the differences of going to public cloud deployments versus really owning their database in-house and are security meeting the needs of what customers need now? >> Yeah sure, so, you're right, security is probably the most important topic or one of the most important topics that comes up when you discuss the cloud. And what customers want is they want a trust, they want this trust relationship that we do the right thing and doing the right thing means we have all the compliances, we adhere to all the privacy standards, but then we also offer them state of the art security so that they can rely on Microsoft on Azure for the next however many years they want to use the cloud to develop customer leading-edge security. And we do this for example with our encryption technology with Always Encrypted. This is one of those technologies that helps you protect your database against attacks by encrypting sensitive data and the data remains encrypted even though we process queries against it. So we protect against third-party attacks on the database, so Always Encrypted is one of those technologies that may not be for everybody today but customers get the sense that yes, Microsoft is thinking ahead, they're developing this security offering, and I can trust them that they continue to do this, keep my data safe and secure. >> Trust is so fundamental to this whole entire enterprise. How do you build trust with your customers, I mean you have the reputation, but how do you really go about getting your customers to say "Okay, I'm going to board your train?" >> That's a good question, Rebecca. I think as I said it starts with the portfolio of compliance requirements that we have and that we provide for Azure's SQL Database and all the other Azure services as well. But it also goes beyond that, it goes, for example, we have this right to audit capability in Azure where a company can come to us and says we want to look behind the scenes, we want to see what auditors see so that we can really believe that you are doing all the things you're saying. You're updating your virus protection, you're patching and you have all the right administrative workflows. So this is one way for us to say our doors are open if you want to come and see what we do, then you can come and peek behind the scenes so to speak. And then the other, the third part is by developing features like we do that help customers, first of all make it easy to secure the database, and help them understand vulnerabilities, and help them understand the configurations of their database and then implement the security strategy that they feel comfortable with and then letting them move that strategy into the cloud and implement it, and I think that's what we do in Azure, and that's why we've had so much success so far. >> Earlier this week we interviewed one of your peers, talked about Cosmos DB. >> Okay. >> There's a certain type of scale we talk about there. Scale means different things to different sized customers. What does scale mean in your space? >> Yeah so you're right, scale can mean a lot of different things, and actually thank you for bringing this up so we have another announcement that we made on namely Hyper-Scale architecture. So far in Azure SQL DB, we were pretty much constrained in terms of space by the underlying hardware, how much storage comes on these VMs, and thanks to our re-architectured hardware, sorry software, we now have the ability to scale way beyond four terabytes which is the current scale of Azure SQL DB. So we can go to 64 terabytes, 100 terabytes. And we can, not only does that free up, free us from the limitations, but it also keeps it simple for customers. So customers don't have to go and build a complicated scale out architecture to take advantage of this. They can just turn a knob in a portal, and then we give them as much horsepower as they need to include in the storage. And in order for this to happen, we had to do a lot of work. So it doesn't just mean, we didn't just re-architect storage but we also have to make fail-over's faster. We have to continue to invest in online operations like online index rebuild and create to make those resumable, pause and resumable, so that with bigger and bigger databases, you can actually do all those activities that you used to do ya know, without getting in the way of your workloads. So lot of work, but we have Hyper-Scale now in Azure SQL DB and so I think this is another sort of something that customers will be really excited about. >> Sounds like that could have been a real pain point for a lot of DBA's out there, and I'm wondering, I'm sure, as a PM, you get lots of feedback from customers. What are the biggest challenges they're facing? What are some of the things they're excited about that Microsoft's helping them with these days? >> So you're right, this was a big pain point, because if you go to a big enterprise customer and say, hey bring your workload to Azure, and then they say oh yeah great, we've got this big telemetry database, what's your size limit? And you have to say four terabytes, that doesn't go too well. So that's one thing, we've removed that blocker thankfully. Other pain points I think we have by and large, I think the large pain points are we've removed, I think we have small ones where we're still working on making our deployments less painful for some customers. There's customers who are really, really sensitive to disconnects or latent variations in latency. And sometimes when we do deployments, worldwide deployments, we are impacting somebody's customer, so this is a pain point that we're currently working on. Security, as you said, is always a pain point, so this is something that will stay with us, and we just have to make sure that we're keeping up with the security demands from customers. And then, another pain point, or has been a pain point for customers, especially customers sequel server on-prem is the performance tuning. When you have to be a really, really good DBA to tune your workloads well, and so this is something that we are working on in Azure SQL DB with our intelligence performance tuning. This is a paint point that we are removing. We've removed a lot of it already. There's still, occasionally, there's still customers who complaining about performance and that's understood. And this is something that we're also trying to help them with, make it easier, give 'em insights into what their workload is doing, where are the weights, where are the slow queries, and then help them diffuse that. >> So thinking about these announcements and the changes that you've made to improve functionality and increase, not have size limits be such a road block, when you're thinking ahead to making the database more intelligent, what are some of the things you're most excited about that are still in progress right now, still in development, that we'll be talking about at next year's Ignite? >> Yeah, so personally for me on the security side, what's really exciting to me is the, so security's a very complicated topic, and not all of our customers are fully comfortable figuring out what is my security strategy and how do I implement it, and is my data really secure. So understanding threats, understanding all this technology, so I think one of the visions that gets me excited about the potential of the cloud, is that we can make security in the future hopefully as easy as we were able to make query processing with the invention of the relational model, where we made this leap from having to write code to access your data to basically a declarative SQL type language where you say this is what I want and I don't care how to database system returns it to me. If you translate that to security, what would be ideal the sort of the North Star, is to tell it to have customers in some sort of declarative policy based manner, say I have some data that I don't trust to the cloud please find the sensitive information here, and then protect it so that I'm meeting ISO or I'm meeting HIPPA requirements or that I'm meeting my internal ya know, every company has internal policies about how data needs to be secured and handled. And so if you could translate that into a declarative policy and then upload that to us, and we figure out behind the scenes these are the things we need, you need to turn on auditing, these are where the audit events have to go, and this is where the data has to be protected. But before all that, we actually identify all the sensitive data for you, we'll tag it and so forth. That to me has been a tremendous, sort of untapped potential of the cloud. That's where I think this intelligence could go potentially. >> Yeah, great. >> Who knows, maybe. >> (laughs) Well, we shall see at next year's Ignite. >> We are making handholds there. We have a classification engine that helps customers find sensitive data. We have a vulnerability assessment, a rules engine that allows you to basically test the configuration of your database against potential vulnerabilities, and we have threat detection. So we have a lot of the pieces, and I think the next step for us is to put these all together into something that can then be much more automated so that a customer doesn't have to think technology anymore. They can they business. They can think about the kinds of compliances they have to meet. They can think about, based on these compliances, this data can go this month, this data can go maybe next year, or ya know, in that kind of terms. So I think, that to me is exciting. >> Well Joachim, thank you so much for coming on theCUBE. It was a pleasure having you here. >> It was my pleasure too. Thank you. >> I'm Rebecca Knight for Stu Miniman, we'll have more from theCUBE's live coverage of Microsoft Ignite coming up in just a little bit. (upbeat music)