(calming music) >> Hello and welcome back to fabulous Las Vegas, Nevada. We're here at AWS re:Invent day three of our scintillating coverage here on theCUBE. I'm Savannah Peterson, joined by John Furrier. John Day three energy's high. How you feeling? >> I dunno, it's day two, day three, day four. It feels like day four, but again, we're back. >> Who's counting? >> Three pandemic levels in terms of 50,000 plus people? Hallways are packed. I got pictures. People don't believe it. It's actually happening. Then people are back. So, you know, and then the economy is a big question too and it's still, people are here, they're still building on the cloud and cost is a big thing. This next segment's going to be really important. I'm looking forward to this next segment. >> Yeah, me too. Without further ado let's welcome our guests for this segment. We have Brad from AMD and we have Rahul from you are, well you do a variety of different things. We'll start with CloudFix for this segment, but we could we could talk about your multiple hats all day long. Welcome to the show, gentlemen. How you doing? Brad how does it feel? We love seeing your logo above our stage here. >> Oh look, we love this. And talking about re:Invent last year, the energy this year compared to last year is so much bigger. We love it. We're excited to be here. >> Yeah, that's awesome. Rahul, how are you feeling? >> Excellent, I mean, I think this is my eighth or ninth re:Invent at this point and it's been fabulous. I think the, the crowd, the engagement, it's awesome. >> You wouldn't know there's a looming recession if you look at the activity but yet still the reality is here we had an analyst on yesterday, we were talking about spend more in the cloud, save more. So that you can still use the cloud and there's a lot of right sizing, I call you got to turn the lights off before you go to bed. Kind of be more efficient with your infrastructure as a theme. This re:Invent is a lot more about that now. Before it's about the glory days. Oh yeah, keep building, now with a little bit of pressure. This is the conversation. >> Exactly and I think most companies are looking to figure out how to innovate their way out of this uncertainty that's kind of on everyone's head. And the only way to do it is to be able to be more efficient with whatever your existing spend is, take those savings and then apply them to innovating on new stuff. And that's the way to go about it at this point. >> I think it's such a hot topic, for everyone that we're talking about. I mean, total cost optimization figuring out ways to be more efficient. I know that that's a big part of your mission at CloudFix. So just in case the audience isn't versed, give us the pitch. >> Okay, so a little bit of background on this. So the other hat I wear is CTO of ESW Capital. We have over 150 enterprise software companies within the portfolio. And one of my jobs is also to manage and run about 40 to 45,000 AWS accounts of our own. >> Casual number, just a few, just a couple pocket change, no big deal. >> And like everyone else here in the audience, yeah we had a problem with our costs, just going out of control and as we were looking at a lot of the tools to help us kind of get more efficient one of the biggest issues was that while people give you a lot of recommendations recommendations are way too far from realized savings. And we were running through the challenge of how do you take recommendation and turn them into real savings and multiple different hurdles. The short story being, we had to create CloudFix to actually realize those savings. So we took AWS recommendations around cost, filtered them down to the ones that are completely non-disruptive in nature, implemented those as simple automations that everyone could just run and realize those savings right away. We then took those savings and then started applying them to innovating and doing new interesting things with that money. >> Is there a best practice in your mind that you see merging in this time? People start more focused on it. Is there a method or a purpose kind of best practice of how to approach cost optimization? >> I think one of the things that most people don't realize is that cost optimization is not a one and done thing. It is literally nonstop. Which means that, on one hand AWS is constantly creating new services. There are over a hundred thousand API at this point of time How to use them right, how to use them efficiently You also have a problem of choice. Developers are constantly discovering new services discovering new ways to utilize them. And they are behaving in ways that you had not anticipated before. So you have to stay on top of things all the time. And really the only way to kind of stay on top is to have automation that helps you stay on top of all of these things. So yeah, finding efficiencies, standardizing your practices about how you leverage these AWS services and then automating the governance and hygiene around how you utilize them is really the key >> Brad tell me what this means for AMD and what working with CloudFix and Rahul does for your customers. >> Well, the idea of efficiency and cost optimization is near and dear to our heart. We have the leading. >> It's near and dear to everyone's heart, right now. (group laughs) >> But we are the leaders in x86 price performance and density and power efficiency. So this is something that's actually part of our core culture. We've been doing this a long time and what's interesting is most companies don't understand how much more efficiency they can get out of their applications aside from just the choices they make in cloud. but that's the one thing, the message we're giving to everybody is choice matters very much when it comes to your cloud solutions and just deciding what type of instance types you choose can have a massive impact on your bottom line. And so we are excited to partner with CloudFix, they've got a great model for this and they make it very easier for our customers to help identify those areas. And then AMD can come in as well and then help provide additional insight into those applications what else they can squeeze out of it. So it's a great relationship. >> If I hear you correctly, then there's more choice for the customers, faster selection, so no bad choices means bad performance if they have a workload or an app that needs to run, is that where you you kind of get into the, is that where it is or more? >> Well, I mean from the AMD side right now, one of the things they do very quickly is they identify where the low hanging fruit is. So it's the thing about x86 compatibility, you can shift instance types instantly in most cases without any change to your environment at all. And CloudFix has an automated tool to do that. And that's one thing you can immediately have an impact on your cost without having to do any work at all. And customers love that. >> What's the alternative if this doesn't exist they have to go manually figure it out or it gets them in the face or they see the numbers don't work or what's the, if you don't have the tool to automate what's the customer's experience >> The alternative is that you actually have people look at every single instance of usage of resources and try and figure out how to do this. At cloud scale, that just doesn't make sense. You just can't. >> It's too many different options. >> Correct The reality is that your resources your human resources are literally your most expensive part of your budget. You want to leverage all the amazing people you have to do the amazing work. This is not amazing work. This is mundane. >> So you free up all the people time. >> Correct, you free up wasting their time and resources on doing something that's mundane, simple and should be automated, because that's the only way you scale. >> I think of you is like a little helper in the background helping me save money while I'm not thinking about it. It's like a good financial planner making you money since we're talking about the economy >> Pretty much, the other analogy that I give to all the technologists is this is like garbage collection. Like for most languages when you are coding, you have these new languages that do garbage collection for you. You don't do memory management and stuff where developers back in the day used to do that. Why do that when you can have technology do that in an automated manner for you in an optimal way. So just kind of freeing up your developer's time from doing this stuff that's mundane and it's a standard best practice. One of the things that we leverage AMD for, is they've helped us define the process of seamlessly migrating folks over to AMD based instances without any major disruptions or trying to minimize every aspect of disruption. So all the best practices are kind of borrowed from them, borrowed from AWS in most other cases. And we basically put them in the automation so that you don't ever have to worry about that stuff. >> Well you're getting so much data you have the opportunity to really streamline, I mean I love this, because you can look across industry, across verticals and behavior of what other folks are doing. Learn from that and apply that in the background to all your different customers. >> So how big is the company? How big is the team? >> So we have people in about 130 different countries. So we've completely been remote and global and actually the cloud has been one of the big enablers of that. >> That's awesome, 130 countries. >> And that's the best part of it. I was just telling Brad a short while ago that's allowed us to hire the best talent from across the world and they spend their time building new amazing products and new solutions instead of doing all this other mundane stuff. So we are big believers in automation not only for our world. And once our customers started asking us about or telling us about the same problem that they were having that's when we actually took what we had internally for our own purpose. We packaged it up as CloudFix and launched it last year at re:Invent. >> If the customers aren't thinking about automation then they're going to probably have struggle. They're going to probably struggle. I mean with more data coming in you see the data story here more data's coming in, more automation. And this year Brad price performance, I've heard the word price performance more this year at re:Invent than any other year I've heard it before, but this year, price performance not performance, price performance. So you're starting to hear that dialogue of squeeze, understand the use cases use the right specialized processor instance starting to see that evolve. >> Yeah and and there's so much to it. I mean, AMD right out of the box is any instance is 10% less expensive than the equivalent in the market right now on AWS. They do a great job of maximizing those products. We've got our Zen four core general processor family just released in November and it's going to be a beast. Yeah, we're very excited about it and AWS announced support for it so we're excited to see what they deliver there too. But price performance is so critical and again it's going back to the complexity of these environments. Giving some of these enterprises some help, to help them understand where they can get additional value. It goes well beyond the retail price. There's a lot more money to be shaved off the top just by spending time thinking about those applications. >> Yeah, absolutely. I love that you talked about collaboration we've been talking about community. I want to acknowledge the AWS super fans here, standing behind the stage. Rahul, I know that you are an AWS super fan. Can you tell us about that community and the program? >> Yeah, so I have been involved with AWS and building products with AWS since 2007. So it's kind of 15 years back when literally there were just a handful of API for launching EC2 instances and S3. >> Not the a hundred thousand that you mentioned earlier, my goodness, the scale. >> So I think I feel very privileged and honored that I have been part of that journey and have had to learn or have had the opportunity to learn both from successes and failures. And it's just my way of contributing back to that community. So we are part of the FinOps foundation as well, contributing through that. I run a podcast called AWS Insiders and a livestream called AWS Made Easy. So we are trying to make sure that people out there are able to understand how to leverage AWS in the best possible way. And yeah, we are there to help and hold their hand through it. >> Talk about the community, take a minute to explain to the audience watching the community around this cost optimization area. It's evolving, you mentioned FinOps. There's a whole large community developing, of practitioners and technologists coming together to look at this. What does this all mean? Talk about this community. >> So cost management within organizations is has evolved so drastically that organizations haven't really coped with it. Historically, you've had finance teams basically buy a lot of infrastructure, which is CapEx and the engineering teams had kind of an upper bound on what they would spend and where they would spend. Suddenly with cloud, that's kind of enabled so much innovation all of a sudden, everyone's realized it, five years was spent figuring out whether people should be on the cloud or not. That's no longer a question, right. Everyone needs to be in the cloud and I think that's a no-brainer. The problem there is that suddenly your operating model has moved from CapEx to OpEx. And organizations haven't really figured out how to deal with it. Finance now no longer has the controls to control and manage and forecast costs. Engineering has never had to deal with it in the past and suddenly now they have to figure out how to do all this finance stuff. And procurement finds itself in a very awkward way position because they are no longer doing these negotiations like they were doing in the past where it was okay right up front before you engage, you do these negotiations. Now it's kind of an ongoing thing and it's constantly changing. Like every day is different. >> And you got marketplace >> And you got marketplace. So it's a very complex situation and I think what we are trying to do with the FinOps foundation is try and take a lot of the best practices across organizations that have been doing this at least for the last 10, 15 years. Take all the learnings and failures and turn them into hopefully opinionated approaches that people can take organizations can take to navigate through this faster rather than kind of falter and then decide that oh, this is not for us. >> Yeah. It's a great model, it's a great model. >> I know it's time John, go ahead. >> All right so, we got a little bumper sticker exercise we used to say what's the bumper sticker for the show? We used to say that, now we're modernizing, we're saying if you had to do an Instagram reel right now, short hot take of what's going on at re:Invent this year with AMD or CloudFix or just in general what would be the sizzle reel, that would be on Instagram or TikTok, go. >> Look, I think when you're at re:Invent right now and number one the energy is fantastic. 23 is going to be a building year. We've got a lot of difficult times ahead financially but it's the time, the ones that come out of 23 stronger and more efficient, and cost optimize are going to survive the long run. So now's the time to build. >> Well done, Rahul let's go for it. >> Yeah, so like Brad said, cost and efficiencies at the top of everyone's mind. Stuff that's the low hanging fruit, easy, use automation. Apply your sources to do most of the innovation. Take the easiest part to realizing savings and operate as efficiently as you possibly can. I think that's got to be key. >> I think they nailed it. They both nailed it. Wow, well it was really good. >> I put you on our talent list of >> And alright, so we repeat them. Are you part of our host team? I love this, I absolutely love this Rahul we wish you the best at CloudFix and your 17 other jobs. And I am genuinely impressed. Do you sleep actually? Last question. >> I do, I do. I have an amazing team that really helps me with all of this. So yeah, thanks to them and thank you for having us here. >> It's been fantastic. >> It's our pleasure. And Brad, I'm delighted we get you both now and again on our next segment. Thank you for being here with us. >> Thank you very much. >> And thank you all for tuning in to our live coverage here at AWS re:Invent, in fabulous Sin City with John Furrier, my name's Savannah Peterson. You're watching theCUBE, the leader in high tech coverage. (calm music)

>>Hello, everyone. Welcome to the cubes presentation of the 80 minutes startup showcase. Season two, episode two, the theme is data as code, the future of analytics. I'm John furry, your host. We had a great day lineup for you. Fast growing startups, great lineup of companies, founders, and stories around data as code. And we're going to kick it off here with our opening keynote with Rahul Pathak VP of analytics at AWS cube alumni. Right? We'll thank you for coming on and being the opening keynote for this awesome event. >>Yeah. And it's great to see you, and it's great to be part of this event, uh, excited to, um, to help showcase some of the great innovation that startups are doing on top of AWS. >>Yeah. We last spoke at AWS reinvent and, uh, a lot's happened there, service loss of serverless as the center of the, of the action, but all these start-ups rock set Dremio Cribble monks next Liccardo, a HANA imply all doing great stuff. Data as code has a lot of traction. So a lot of still momentum going on in the marketplace. Uh, pretty exciting. >>No, it's, uh, it's awesome. I mean, I think there's so much innovation happening and you know, the, the wonderful part of working with data is that the demand for services and products that help customers drive insight from data is just skyrocketing and has no sign of no sign of slowing down. And so it's a great time to be in the data business. >>It's interesting to see the theme of the show getting traction, because you start to see data being treated almost like how developers write software, taking things out of branches, working on them, putting them back in, uh, machine learnings, uh, getting iterated on you, seeing more models, being trained differently with better insights, action ones that all kind of like working like code. And this is a whole nother way. People are reinventing their businesses. This has been a big, huge wave. What's your reaction to that? >>Uh, I think it's spot on, I mean, I think the idea of data's code and bringing some of the repeatability of processes from software development into how people built it, applications is absolutely fundamental and especially so in machine learning where you need to think about the explainability of a model, what version of the world was it trained on? When you build a better model, you need to be able to explain and reproduce it. So I think your insights are spot on and these ideas are showing up in all stages of the data work flow from ingestion to analytics to I'm out >>This next way is about modernization and going to the next level with cloud-scale. Uh, thank you so much for coming on and being the keynote presenter here for this great event. Um, I'll let you take it away. Reinventing businesses, uh, with ads analytics, right? We'll take it away. >>Okay, perfect. Well, folks, we're going to talk about, uh, um, reinventing your business with, uh, data. And if you think about it, the first wave of reinvention was really driven by the cloud. As customers were able to really transform how they thought about technology and that's well on her way. Although if you stop and think about it, I think we're only about five to 10% of the way done in terms of it span being on the cloud. So lots of work to do there, but we're seeing another wave of reinvention, which is companies reinventing their businesses with data and really using data to transform what they're doing to look for new opportunities and look for ways to operate more efficiently. And I think the past couple of years of the pandemic, it really only accelerated that trend. And so what we're seeing is, uh, you know, it's really about the survival of the most informed folks for the best data are able to react more quickly to what's happening. >>Uh, we've seen customers being able to scale up if they're in, say the delivery business or scale down, if they were in the travel business at the beginning of all of this, and then using data to be able to find new opportunities and new ways to serve customers. And so it's really foundational and we're seeing this across the board. And so, um, you know, it's great to see the innovation that's happening to help customers make sense of all of this. And our customers are really looking at ways to put data to work. It's about making better decisions, finding new efficiencies and really finding new opportunities to succeed and scale. And, um, you know, when it comes to, uh, good examples of this FINRA is a great one. You may not have heard of them, but that the U S equities regulators, all trading that happens in equities, they keep track of they're look at about 250 billion records per day. >>Uh, the examiner, I was only EMR, which is our spark and Hadoop service, and they're processing 20 terabytes of data running across tens of thousands of nodes. And they're looking for fraud and bad actors in the market. So, um, you know, huge, uh, transformation journey for FINRA over the years of customer I've gotten to work with personally since really 2013 onward. So it's been amazing to see their journey, uh, Pinterest, not a great customer. I'm sure everyone's familiar with, but, um, you know, they're about visual search and discovery and commerce, and, um, they're able to scale their daily lot searches, um, really a factor of three X or more, uh, drive down their costs. And they're using the Amazon Opus search service. And really what we're trying to do at AWS is give our customers the most comprehensive set of services for the end-to-end journey around, uh, data from ingestion to analytics and machine learning. And we will want to provide a comprehensive set of capabilities for ingestion, cataloging analytics, and then machine learning. And all of these are things that our partners and the startups that are run on us have available to them to build on as they build and deliver value for their customers. >>And, you know, the way we think about this is we want customers to be able to modernize what they're doing and their infrastructure. And we provide services for that. It's about unifying data, wherever it lives, connecting it. So the customers can build a complete picture of their customers and business. And then it's about innovation and really using machine learning to bring all of this unified data, to bear on driving new innovation and new opportunities for customers. And what we're trying to do AWS is really provide a scalable and secure cloud platform that customers and partners can build on a unifying is about connecting data. And it's also about providing well-governed access to data. So one of the big trends that we see is customers looking for the ability to make self-service data available to that customer there and use. And the key to that is good foundational governance. >>Once you can define good access controls, you then are more comfortable setting data free. And, um, uh, the other part of it is, uh, data lakes play a huge role because you need to be able to think about structured and unstructured data. In fact, about 80% of the data being generated today, uh, is unstructured. And you want to be able to connect data that's in data lakes with data that's in purpose-built data stores, whether that's databases on AWS databases, outside SAS products, uh, as well as things like data warehouses and machine learning systems, but really connecting data as key. Uh, and then, uh, innovation, uh, how can we bring to bear? And we imagine all processes with new technologies like AI and machine learning, and AI is also key to unlocking a lot of the value that's in unstructured data. If you can figure out what's in an imagine the sentiment of audio and do that in real-time that lets you then personalize and dynamically tailor experiences, all of which are super important to getting an edge, um, in, uh, in the modern marketplace. And so at AWS, we, when we think about connecting the dots across sources of data, allowing customers to use data, lakes, databases, analytics, and machine learning, we want to provide a common catalog and governance and then use these to help drive new experiences for customers and their apps and their devices. And then this, you know, in an ideal world, we'll create a closed loop. So you create a new experience. You observe our customers interact with it, that generates more data, which is a data source that feeds into the system. >>And, uh, you know, on AWS, uh, thinking about a modern data strategy, uh, really at the core is a data lakes built on us three. And I'll talk more about that in a second. Then you've got services like Athena included, lake formation for managing that data, cataloging it and querying it in place. And then you have the ability to use the right tool for the right job. And so we're big believers in purpose-built services for data because that's where you can avoid compromising on performance functionality or scale. Uh, and then as I mentioned, unification and inter interconnecting, all of that data. So if you need to move data between these systems, uh, there's well-trodden pathways that allow you to do that, and then features built into services that enable that. >>And, um, you know, some of the core ideas that guide the work that we do, um, scalable data lakes at key, um, and you know, this is really about providing arbitrarily scalable high throughput systems. It's about open format data for future-proofing. Uh, then we talk about purpose-built systems at the best possible functionality, performance, and cost. Uh, and then from a serverless perspective, this has been another big trend for us. We announced a bunch of serverless services and reinvented the goal here is to really take away the need to manage infrastructure from customers. They can really focus about driving differentiated business value, integrated governance, and then machine learning pervasively, um, not just as an end product for data scientists, but also machine learning built into data, warehouses, visualization and a database. >>And so it's scalable data lakes. Uh, data three is really the foundation for this. One of our, um, original services that AWS really the backbone of so much of what we do, uh, really unmatched your ability, availability, and scale, a huge portfolio of analytics services, uh, both that we offer, but also that our partners and customers offer and really arbitrary skin. We've got individual customers and estimator in the expert range, many in the hundreds of petabytes. And that's just growing. You know, as I mentioned, we see roughly a 10 X increase in data volume every five years. So that's a exponential increase in data volumes, Uh, from a purpose-built perspective, it's the right tool for the right job, the red shift and data warehousing Athena for querying all your data. Uh, EMR is our managed sparking to do, uh, open search for log analytics and search, and then Kinesis and Amex care for CAFCA and streaming. And that's been another big trend is, uh, real time. Data has been exploding and customers wanting to make sense of that data in real time, uh, is another big deal. >>Uh, some examples of how we're able to achieve differentiated performance and purpose-built systems. So with Redshift, um, using managed storage and it's led us and since types, uh, the three X better price performance, and what's out there available to all our customers and partners in EMR, uh, with things like spark, we're able to deliver two X performance of open source with a hundred percent compatibility, uh, almost three X and Presto, uh, with on two, which is our, um, uh, new Silicon chips on AWS, better price performance, about 10 to 12% better price performance, and 20% lower costs. And then, uh, all compatible source. So drop your jobs, then have them run faster and cheaper. And that translates to customer benefits for better margins for partners, uh, from a serverless perspective, this is about simplifying operations, reducing total cost of ownership and freeing customers from the need to think about capacity management. If we invent, we, uh, announced serverless redshifts EMR, uh, serverless, uh, Kinesis and Kafka, um, and these are all game changes for customers in terms of freeing our customers and partners from having to think about infrastructure and allowing them to focus on data. >>And, um, you know, when it comes to several assumptions in analytics, we've really got a very full and complete set. So, uh, whether that's around data warehousing, big data processing streaming, or cataloging or governance or visualization, we want all of our customers to have an option to run something struggles as well as if they have specialized needs, uh, uh, instances are available as well. And so, uh, really providing a comprehensive deployment model, uh, based on the customer's use cases, uh, from a governance perspective, uh, you know, like information is about easy build and management of data lakes. Uh, and this is what enables data sharing and self service. And, um, you know, with you get very granular access controls. So rule level security, uh, simple data sharing, and you can tag data. So you can tag a group of analysts in the year when you can say those only have access to the new data that's been tagged with the new tags, and it allows you to very, scaleably provide different secure views onto the same data without having to make multiple copies, another big win for customers and partners, uh, support transactions on data lakes. >>So updates and deletes. And time-travel, uh, you know, John talked about data as code and with time travel, you can look at, um, querying on different versions of data. So that's, uh, a big enabler for those types of strategies. And with blue, you're able to connect data in multiple places. So, uh, whether that's accessing data on premises in other SAS providers or, uh, clouds, uh, as well as data that's on AWS and all of this is, uh, serverless and interconnected. And, um, and really it's about plugging all of your data into the AWS ecosystem and into our partner ecosystem. So this API is all available for integration as well, but then from an AML perspective, what we're really trying to do is bring machine learning closer to data. And so with our databases and warehouses and lakes and BI tools, um, you know, we've infused machine learning throughout our, by, um, the state of the art machine running that we offer through SageMaker. >>And so you've got a ML in Aurora and Neptune for broths. Uh, you can train machine learning models from SQL, directly from Redshift and a female. You can use free inference, and then QuickSight has built in forecasting built in natural language, querying all powered by machine learning, same with anomaly detection. And here are the ideas, you know, how can we up our systems get smarter at the surface, the right insights for our customers so that they don't have to always rely on smart people asking the right questions, um, and you know, uh, really it's about bringing data back together and making it available for innovation. And, uh, thank you very much. I appreciate your attention. >>Okay. Well done reinventing the business with AWS analytics rural. That was great. Thanks for walking through that. That was awesome. I have to ask you some questions on the end-to-end view of the data. That seems to be a theme serverless, uh, in there, uh, Mel integration. Um, but then you also mentioned picking the right tool for the job. So then you've got like all these things moving on, simplify it for me right now. So from a business standpoint, how do they modernize? What's the steps that the clients are taking with analytics, what's the best practice? How do they, what's the what's the high order bit here? >>Uh, so the basic hierarchy is, you know, historically legacy systems are rigid and inflexible, and they weren't really designed for the scale of modern data or the variety of it. And so what customers are finding is they're moving to the cloud. They're moving from legacy systems with punitive licensing into more flexible, more systems. And that allows them to really think about building a decoupled, scalable future proof architecture. And so you've got the ability to combine data lakes and databases and data warehouses and connect them using common KPIs and common data protection. And that sets you up to deal with arbitrary scale and arbitrary types. And it allows you to evolve as the future changes since it makes it easy to add in a new type of engine, as we invent a better one a few years from now. Uh, and then, uh, once you've kind of got your data in a cloud and interconnected in this way, you can now build complete pictures of what's going on. You can understand all your touch points with customers. You can understand your complete supply chain, and once you can build that complete picture of your business, you can start to use analytics and machine learning to find new opportunities. So, uh, think about modernizing, moving to the cloud, setting up for the future, connecting data end to end, and then figuring out how to use that to your advantage. >>I know as you mentioned, modern data strategy gives you the best of both worlds. And you've mentioned, um, briefly, I want to get a little bit more, uh, insight from you on this. You mentioned open, open formats. One of the themes that's come out of some of the interviews, these companies we're going to be hearing from today is open source. The role opens playing. Um, how do you see that integrating in? Because again, this is just like software, right? Open, uh, open source software, open source data. It seems to be a trend. What does open look like to you? How do you see that progressing? >>Uh, it's a great question. Uh, open operates on multiple dimensions, John, as you point out, there's open data formats. These are things like JSI and our care for analytics. This allows multiple engines tend to operate on data and it'll, it, it creates option value for customers. If you're going to data in an open format, you can use it with multiple technologies and that'll be future-proofed. You don't have to migrate your data. Now, if you're thinking about using a different technology. So that's one piece now that sort of software, um, also, um, really a big enabler for innovation and for customers. And you've got things like squat arc and Presto, which are popular. And I know some of the startups, um, you know, that we're talking about as part of the showcase and use these technologies, and this allows for really the world to contribute, to innovating and these engines and moving them forward together. And we're big believers in that we've got open source services. We contribute to open-source, we support open source projects, and that's another big part of what we do. And then there's open API is things like SQL or Python. Uh, again, uh, common ways of interacting with data that are broadly adopted. And this one, again, create standardization. It makes it easier for customers to inter-operate and be flexible. And so open is really present all the way through. And it's a big part, I think, of, uh, the present and the future. >>Yeah. It's going to be fun to watch and see how that grows. It seems to be a lot of traction there. I want to ask you about, um, the other comment I thought was cool. You had the architectural slides out there. One was data lakes built on S3, and you had a theme, the glue in lake formation kind of around S3. And then you had the constellation of, you know, Kinesis SageMaker and other things around it. And you said, you know, pick the tool for the right job. And then you had the other slide on the analytics at the center and you had Redshift and all the other, other, other services around it around serverless. So one was more about the data lake with Athena glue and lake formation. The other one's about serverless. Explain that a little bit more for me, because I'm trying to understand where that fits. I get the data lake piece. Okay. Athena glue and lake formation enables it, and then you can pick and choose what you need on the serverless side. What does analytics in the center mean? >>So the idea there is that really, we wanted to talk about the fact that if you zoom into the analytics use case within analytics, everything that we offer, uh, has a serverless option for our customers. So, um, you could look at the bucket of analytics across things like Redshift or EMR or Athena, or, um, glue and league permission. You have the option to use instances or containers, but also to just not worry about infrastructure and just think declaratively about the data that you want to. >>Oh, so basically you're saying the analytics is going serverless everywhere. Talking about volumes, you mentioned 10 X volumes. Um, what are other stats? Can you share in terms of volumes? What are people seeing velocity I've seen data warehouses can't move as fast as what we're seeing in the cloud with some of your customers and how they're using data. How does the volume and velocity community have any kind of other kind of insights into those numbers? >>Yeah, I mean, I think from a stats perspective, um, you know, take Redshift, for example, customers are processing. So reading and writing, um, multiple exabytes of data there across from each shift. And, uh, you know, one of the things that we've seen in, uh, as time has progressed as, as data volumes have gone up and did a tapes have exploded, uh, you've seen data warehouses get more flexible. So we've added things like the ability to put semi-structured data and arbitrary, nested data into Redshift. Uh, we've also seen the seamless integration of data warehouses and data lakes. So, um, actually Redshift was one of the first to enable a straightforward acquiring of data. That's sitting in locally and drives as well as feed and that's managed on a stream and, uh, you know, those trends will continue. I think you'll kind of continue to see this, um, need to query data wherever it lives and, um, and, uh, allow, uh, leaks and warehouses and purpose-built stores to interconnect. >>You know, one of the things I liked about your presentation was, you know, kind of had the theme of, you know, modernize, unify, innovate, um, and we've been covering a lot of companies that have been, I won't say stumbling, but like getting to the future, some go faster than others, but they all kind of get stuck in an area that seems to be the same spot. It's the silos, breaking down the silos and get in the data lakes and kind of blending that purpose built data store. And they get stuck there because they're so used to silos and their teams, and that's kind of holding back the machine learning side of it because the machine learning can't do its job if they don't have access to all the data. And that's where we're seeing machine learning kind of being this new iterative model where the models are coming in faster. And so the silo brake busting is an issue. So what's your take on this part of the equation? >>Uh, so there's a few things I plan it. So you're absolutely right. I think that transition from some old data to interconnected data is always straightforward and it operates on a number of levels. You want to have the right technology. So, um, you know, we enable things like queries that can span multiple stores. You want to have good governance, you can connect across multiple ones. Uh, then you need to be able to get data in and out of these things and blue plays that role. So there's that interconnection on the technical side, but the other piece is also, um, you know, you want to think through, um, organizationally, how do you organize, how do you define it once data when they share it? And one of the asylees for enabling that sharing and, um, think about, um, some of the processes that need to get put in place and create the right incentives in your company to enable that data sharing. And then the foundational piece is good guardrails. You know, it's, uh, it can be scary to open data up. And, uh, the key to that is to put good governance in place where you can ensure that data can be shared and distributed while remaining protected and adhering to the privacy and compliance and security regulations that you have for that. And once you can assert that level of protection, then you can set that data free. And that's when, uh, customers really start to see the benefits of connecting all of it together, >>Right? And then we have a batch of startups here on this episode that are doing a lot of different things. Uh, some have, you know, new lake new lakes are forming observability lakes. You have CQL innovation on the front end data, tiering innovation at the data tier side, just a ton of innovation around this new data as code. How do you see as executive at AWS? You're enabling all this, um, where's the action going? Where are the white spaces? Where are the opportunities as this architecture continues to grow, um, and get traction because of the relevance of machine learning and AI and the apps are embedding data in there now as code where's the opportunities for these startups and how can they continue to grow? >>Yeah, the, I mean, the opportunity is it's amazing, John, you know, we talked a little bit about this at the beginning, but the, there is no slow down insight for the volume of data that we're generating pretty much everything that we have, whether it's a watch or a phone or the systems that we interact with are generating data and, uh, you know, customers, uh, you know, we talk a lot about the things that'll stay the same over time. And so, you know, the data volumes will continue to go up. Customers are gonna want to keep analyzing that data to make sense of it. They're going to want to be able to do it faster and more cheaply than they were yesterday. And then we're going to want to be able to make decisions and innovate, uh, in a shorter cycle and run more experiments than they were able to do. >>And so I think as long as, and they're always going to want this data to be secure and well-protected, and so I think as long as we, and the startups that we work with can continue to push on making these things better. Can I deal with more data? Can I deal with it more cheaply? Can I make it easier to get insight? And can I maintain a super high bar in security investments in these areas will just be off. Um, because, uh, the demand side of this equation is just in a great place, given what we're seeing in terms of theater and the architect for forum. >>I also love your comment about, uh, ML integration being the last leg of the equation here or less likely the journey, but you've got that enablement of the AIP solves a lot of problems. People can see benefits from good machine learning and AI is creating opportunities. Um, and also you also have mentioned the end to end with security piece. So data and security are kind of going hand in hand these days, not just the governments and the compliance stuff we're talking about security. So machine learning integration kind of connects all of this. Um, what's it all mean for the customers, >>For customers. That means that with machine learning and really enabling themselves to use machine learning, to make sense of data, they're able to find patterns that can represent new opportunities, um, quicker than ever before. And they're able to do it, uh, dynamically. So, you know, in a prior version of the world, we'd have little bit of systems and they would be relatively rigid and then we'd have to improve them. Um, with machine learning, this can be dynamic and near real time and you can customize them. So, uh, that just represents an opportunity to deepen relationships with customers and create more value and to find more efficiency in how businesses are run. So that piece is there. Um, and you know, your ideas around, uh, data's code really come into play because machine learning needs to be repeatable and explainable. And that means versioning, uh, keeping track of everything that you've done from a code and data and learning and training perspective >>And data sets are updating the machine learning. You got data sets growing, they become code modules that can be reused and, uh, interrogated, um, security okay. Is a big as a big theme data, really important security is seen as one of our top use cases. Certainly now in this day and age, we're getting a lot of, a lot of breaches and hacks coming in, being defended. It brings up the open, brings up the data as code security is a good proxy for kind of where this is going. What's your what's take on that and your reaction to that. >>So I'm, I'm security. You can, we can never invest enough. And I think one of the things that we, um, you know, guide us in AWS is security, availability, durability sort of jobs, you know, 1, 2, 3, and, um, and it operates at multiple levels. You need to protect data and rest with encryption, good key management and good practices though. You need to protect data on the wire. You need to have a good sense of what data is allowed to be seen by whom. And then you need to keep track of who did what and be able to verify and come back and prove that, uh, you know, uh, only the things that were allowed to happen actually happened. And you can actually then use machine learning on top of all of this apparatus to say, uh, you know, can I detect things that are happening that shouldn't be happening in near real time so they could put a stop to them. So I don't think any of us can ever invest enough in securing and protecting my data and our systems, and it is really fundamental or adding customer trust and it's just good business. So I think it is absolutely crucial. And we think about it all the time and are always looking for ways to raise >>Well, I really appreciate you taking the time to give the keynote final word here for the folks watching a lot of these startups that are presenting, they're doing well. Business wise, they're being used by large enterprises and people buying their products and using their services for customers are implementing more and more of the hot startups products they're relevant. What's your advice to the customer out there as they go on this journey, this new data as code this new future of analytics, what's your recommendation. >>So for customers who are out there, uh, recommend you take a look at, um, what, uh, the startups on AWS are building. I think there's tremendous innovation and energy, uh, and, um, there's really great technology being built on top of a rock solid platform. And so I encourage customers thinking about it to lean forward, to think about new technology and to embrace, uh, move to the cloud suite, modernized, you know, build a single picture of our data and, and figure out how to innovate and when >>Well, thanks for coming on. Appreciate your keynote. Thanks for the insight. And thanks for the conversation. Let's hand it off to the show. Let the show begin. >>Thank you, John pleasure, as always.

>>Hey, welcome back everyone. We're live here in the cube in Las Vegas Raiders reinvent 2021. I'm Jeffrey hosted the key we're in person this year. It's a hybrid event online. Great action. Going on. I'm rolling. Vice-president of ADF analytics. David is great to see you. Thanks for coming on. >>It's great to be here, John. Thanks for having me again. >>Um, so you've got a really awesome job. You've got serverless, you've got analytics. You're in the middle of all the action for AWS. What's the big news. What are you guys announcing? What's going on? >>Yeah, well, it's been an awesome reinvent for us. Uh, we've had a number of several us analytics launches. So red shift, our petabyte scale data warehouse, EMR for open source analytics. Uh, and then we've also had, uh, managed streaming for Kafka go serverless and then on demand for Kinesis. And then a couple of other big ones. We've got RO and cell based security for AWS lake formation. So you can get really fine grain controls over your data lakes and then asset transactions. You can actually have a inserts, updates and deletes on data lakes, which is a big step forward. >>Uh, so Swami on stage and the keynote he's actually finishing up now. But even last night I saw him in the hallway. We were talking about as much as about AI. Of course, he's got the AI title, but AI is the outcome. It's the application of all the data and this and a new architecture. He said on stage just now like, Hey, it's not about the old databases from the nineties, right? There's multiple data stores now available. And there's the unification is the big trend. And he said something interesting. Governance can be an advantage, not an inhibitor. This is kind of this new horizontally scalable, um, kind of idea that enables the vertical specialization around machine learning to be effective. It's not a new architecture, but it's now becoming more popular. People are realizing it. It's sort of share your thoughts on this whole not shift, but the acceleration of horizontally scalable and vertically integrated. Yeah, >>No, I think the way Swami put it is exactly right. What you want is the right tool for the right job. And you want to be able to deliver that to customers. So you're not compromising on performance or functionality of scale, but then you wanted all of these to be interconnected. So they're, well-integrated, you can stay in your favorite interface and take advantage of other technologies. So you can have things like Redshift integrated with Sage makers, you get analytics and machine learning. And then in Swami's absolutely right. Governance is actually an enabler of velocity. Once you've got the right guardrails in place, you can actually set people free because they can innovate. You don't have to be in the way, but you know that your data is protected. It's being used in the way that you expect by the people that you are allowing to use that data. And so it becomes a very powerful way for customers to set data free. And then, because things are elastic and serverless, uh, you can really just match capacity with demand. And so as you see spikes in usage, the system can scale out as those dwindle, they can scale back down, and it just becomes a very efficient way for customers to operate with data at scale >>Every year it reinvented. So it was kind of like a pinch me moment. It's like, well, more that's really good technology. Oh my God, it's getting easier and easier. As the infrastructure as code becomes more programmable, it's becoming easier, more Lambda, more serverless action. Uh, you got new offerings. How are customers benefiting for instance, from the three new offerings that you guys announced here? What specifically is the value proposition that you guys are putting out there? Yeah, so the, >>Um, you know, as we've tried to do with AWS over the years, customers get to focus on the things that really differentiate them and differentiate their businesses. So we take away in Redshift serverless, for example, all of the work that's needed to manage clusters, provision them, scale them, optimize them. Uh, and that's all been automated and made invisible to customers, the customers to think about data, what they want to do with it, what insights they can derive from it. And they know they're getting the most efficient infrastructure possible to make that a reality for them with high performance and low costs. So, uh, better results, more ability to focus on what differentiates their business and lower cost structure over time. >>Yeah. I had the essential guys on it's interesting. They had part of the soul cloud. Continuous is their word for what Adam was saying is clouds everywhere. And they're saying it's faster to match what you want to do with the outcomes, but the capabilities and outcomes kind of merging together where it's easy to say, this is what we want to do. And here's the outcome it supports that's right with that. What are some of the key trends on those outcomes that you see with the data analytics that's most popular right now? And kind of where's that, where's that going? >>Yeah. I mean, I think what we've seen is that data's just becoming more and more critical and top of mind for customers and, uh, you know, the pandemic has also accelerated that we found that customers are really looking to data and analytics and machine learning to find new opportunities. How can they, uh, really expand their business, take advantage of what's happening? And then the other part is how can they find efficiencies? And so, um, really everything that we're trying to do is we're trying to connect it to business outcomes for customers. How can you deepen your relationship with your customers? How can you create new customer experiences and how can you do that more efficiently, uh, with more agility and take advantage of, uh, the ability to be flexible. And you know, what is a very unpredictable world, as we've seen, >>I noticed a lot of purpose-built discussion going on in the keynote with Swami as well. How are you creating this next layer of what I call purpose-built platform like features? I mean, tools are great. You see a lot of tools in the data market tools are tools of your hammer. You want to look for a nail. We see people over by too many tools and you have ultimately a platform, but this seems to be a new trend where there's this connect phenomenon was showing me that you've got these platform capabilities that people can build on top of it, because there's a huge ecosystem of data tools out there that you guys have as partners that want to snap together. So the trend is things are starting to snap together, less primitive, roll your own, which you can do, but there's now more easier ways. Take me through that. Explain that, unpack that that phenomenon role rolling your own firm is, which has been the way now to here. Here's, here's some prefabricated software go. >>Yeah. Um, so it's a great observation and you're absolutely right. I mean, I think there's some customers that want to roll their own and they'll start with instances, they'll install software, they'll write their own code, build their own bespoke systems. And, uh, and we provide what the customers need to do that. But I think increasingly you're starting to see these higher level abstractions that take away all of that detail. And mark has Adam put it and allow customers to compose these. And we think it's important when you do that, uh, to be modular. So customers don't have to have these big bang all or nothing approaches you can pick what's appropriate, uh, but you're never on a dead end. You can always evolve and scale as you need to. And then you want to bring these ideas of unified governance and cohesive interfaces across so that customers find it easy to adopt the next thing. And so you can start off say with batch analytics, you can expand into real time. You can bring in machine learning and predictive capabilities. You can add natural language, and it's a big ecosystem of managed services as well as third parties and partners. >>And what's interesting. I want to get your thoughts while I got you here, because I think this is such an important trend and historic moment in time, Jerry chin, who one of the smartest VCs that we know from Greylock and coin castles in the cloud, which kind of came out of a cube conversation here in the queue years ago, where we saw the movement of that someone's going to build real value on AWS, not just an app. And you see the rise of the snowflakes and Databricks and other companies. And he was pointing out that you can get a very narrow wedge and get a position with these platforms, build on top of them and then build value. And I think that's, uh, the number one question people ask me, it's like, okay, how do I build value on top of these analytic packages? So if I'm a startup or I'm a big company, I also want to leverage these high level abstractions and build on top of it. How do you talk about that? How do you explain that? Because that's what people kind of want to know is like, okay, is it enabling me or do I have to fend for myself later? This is kind of, it comes up a lot. >>That's a great question. And, um, you know, if you saw, uh, Goldman's announcement this week, which is about bringing, building their cloud on top of AWS, it's a great example of using our capabilities in terms of infrastructure and analytics and machine learning to really allow them to take what's value added about Goldman and their position to financial markets, to build something value, add, and create a ton of value for Goldman, uh, by leveraging the things that we offer. And to us, that's an ideal outcome because it's a win-win for us in Goldman, but it's also a win for Goldman and their customers. >>That's what we call the Supercloud that's the opportunity. So is there a lot of Goldmans opportunities out there? Is that just a, these unicorns, are these sites? I mean, how do you, I mean, that's Goldman Sachs, they're huge. Is there, is this open to everybody? >>Absolutely. I mean, that's been one of the, uh, you know, one of the core ideas behind AWS was we wanted to give anybody any developer access to the same technology that the world's largest corporations had. And, uh, that's what you have today. The things that Goldman uses to build that cloud are available to anybody. And you can start for a few pennies scale up, uh, you know, into the petabytes and beyond >>When I was talking to Adams, Lipski when I met with him prior to re-invent, I noticed that he was definitely had an affinity towards the data, obviously he's Amazonia, but he spent time at Tableau. So, so as he's running that company, so you see that kind of mindset of the data advantage. So I have to ask you, because it's something that I've been talking about for a while and I'm waiting for it to emerge, but I'm not sure it's going to happen yet. But what infrastructure is code was for dev ops and then dev sec ops, there's almost like a data ops developing where data as code or programmable data. If I can connect the dots of what Swami's saying, what you're doing is this is like a new horizontal layer of data of freely available data with some government governance built in that's right. So it's, data's being baked into everything. So data is any ingredient, not a query to some database, it's gotta be baked into the apps, that's data as code that's. Right. So it's almost a data DevOps kind of vibe. >>Yeah, no, you're absolutely right. And you know, you've seen it with things like ML ops and so on. It's all the special case of dev ops. But what you're really trying to do is to get programmatic and systematic about how you deal with data. And it's not just data that you have. It's also publicly available data sets and it's customers sharing with each other. So building the ecosystem, our data, and we've got things like our open data program where we've got publicly hosted data sets or things like the AWS data exchange where customers can actually monetize data. So it's not just data as code, but now data as a monetizeable asset. So it's a really exciting time to be in the data business. >>Yeah. And I think it's so many too. So I've got to ask you while I got you here since you're an expert. Um, okay. Here's my problem. I have a lot of data. I'm nervous about it. I want to secure it. So if I try to secure it, I'm not making it available. So I want to feed the machine learning. How do I create an architecture where I can make it freely available, but yet maintain the control and the comfort that this is going to be secure. So what products do I buy? >>Yeah. So, uh, you know, a great place to start at as three. Um, you know, it's one of the best places for data lakes, uh, for all the reasons. That's why we talked about your ability scale costs. You can then use lake formation to really protect and govern that data so you can decide who's allowed to see it and what they're allowed to see, and you don't have to create multiple copies. So you can define that, you know, this group of partners can see a, B and C. This group can see D E and F and the system enforces that. And you have a central point of control where you can monitor what's happening. And if you want to change your mind, you can do that instantly. And all access can be locked down that you've got a variety of encryption capabilities with things like KMS. And so you can really lock down your data, but yet keep it open to the parties that you want and give them specifically the access that you want to give them. And then once you've done that, they're free to use that data, according to the rules that you defined with the analytics tools that we offer to go drive value, create insight, and do something >>That's lake formation. And then you got a Thena querying. Yes, we got all kinds of tooling on top of it. >>It's all right. You can have, uh, Athena query and your data in S3 lake formation, protecting it. And then SageMaker is integrated with Athena. So you can pull that data into SageMaker for machine learning, interrogate that data, using natural language with things like QuickSight Q a like we demoed. So just a ton of power without having to really think too deeply about, uh, developing expert skill sets in this. >>So the next question I want to ask you is because that first part of the great, great, great description, thank you very much. Now, 5g in the edges here, outpost, how was the analytics going on that as edge becomes more pervasive in the architecture? >>Yeah, it's going to be a key part of this ecosystem and it's really a continuum. So, uh, you know, we find customers are collecting data at the edge. They might be making local ML or inference type decisions on edge devices, or, you know, automobiles, for example. Uh, but typically that data with some point will come back into the cloud, into S3 will be used to do heavy duty training, and then those models get pushed back out to the edge. And then some of the things that we've done in Athena, for example, with federated query, as long as you have a network path, and you can understand what the data format or the database is, you can actually run a query on that data. So you can run real-time queries on data, wherever it lives, whether it's on an edge device, on an outpost, in a local zone or in your cloud region and combine all of that together in one place. >>Yeah. And I think having that data copies everywhere is a big thing deal. I've got to ask you now that we're here at reinvent, what's your take we're back in person last year was all virtual. Finally, not 60,000 people, like a couple of years ago, it's still 27,000 people here, all lining up for the sessions, all having a great time. Um, all good. What's the most important story from your, your area that people should pay attention to? What's the headline, what's the top news? What should people pay attention to? >>Yeah, so I think first off it is awesome to be back in person. It's just so fun to see customers and to see, I mean, you, like, we've been meeting here over the years and it's, it's great to so much energy in person. It's been really nice. Uh, you know, I think from an analytics perspective, there's just been a ton of innovation. I think the core idea for us is we want to make it easy for customers to use the right tool for the right job to get insight from all of their data as cost effectively as possible. And I think, uh, you know, I think if customers walk away and think about it as being, it's now easier than ever for me to take advantage of everything that AWS has to offer, uh, to make sense of all the data that I'm generating and use it to drive business value, but I think we'll have done our jobs. Right. >>What's the coolest thing that you're seeing here is that the serverless innovation, is it, um, the new abstraction layer with data high level services in your mind? What's the coolest thing. Got it. >>It's hard to pick the coolest that sticks like kicking the candies. I mean, I think the, uh, you know, the continued innovation in terms of, uh, performance and functionality in each of our services is a big deal. I think serverless is a game changer for customers. Uh, and then I think really the infusion of machine learning throughout all of these systems. So things like Redshift ML, Athena ML, Pixar, Q a just really enabling new experiences for customers, uh, in a way that's easier than it ever has been. And I think that's a, that's a big deal and I'm really excited to see what customers do with it. >>Yeah. And I think the performance thing to me, the coolest thing that I'm seeing is the graviton three and the gravitron progression with the custom stacks with all this ease of use, it's just going to be just a real performance advantage and the costs are getting lowered. So I think the ECE two instances around the compute is phenomenal. No, >>Absolutely. I mean, I think the hardware and Silicon innovation is huge and it's not just performance. It's also the energy efficiency. It's a big deal for the future reality. >>We're at an inflection point where this modern applications are being built. And in my history, I'm old, my birthday is today. I'm in my fifties. So I remember back in the eighties, every major inflection point when there was a shift in how things were developed from mainframe client server, PC inter network, you name it every time the apps change, the app owners, app developers all went to the best platform processing. And so I think, you know, that idea of system software applications being bundled together, um, is a losing formula. I think you got to have that decoupling large-scale was seeing that with cloud. And I think now if I'm an app developer, whether whether I'm in a large ISV in your ecosystem or in the APN partner or a startup, I'm going to go with my software runs the best period and where I can create value. That's right. I get distribution, I create value and it runs fast. I mean, that's, I mean, it's pretty simple. So I think the ecosystem is going to be a big action for the next couple of years. >>Absolutely. Right. And I mean, the ecosystem's huge and I think, um, and we're also grateful to have all these partners here. It's a huge deal for us. And I think it really matters for customers >>What's on your roadmap this year, what you got going on. What can you share a little bit of a trajectory without kind of, uh, breaking the rules of the Amazonian, uh, confidentiality. Um, what's, what's the focus for the year? What do you what's next? >>Well, you know, as you know, we're always talking to customers and, uh, I think we're going to make things better, faster, cheaper, easier to use. And, um, I think you've seen some of the things that we're doing with integration now, you'll see more of that. And, uh, really the goal is how can customers get value as quickly as possible for as low cost as possible? That's how we went to >>Yeah. They're in the longterm. Yeah. We've always say every time we see each other data is at the center of the value proposition. I've been saying that for 10 years now, it's actually the value proposition, powering AI. And you're seeing because of it, the rise of superclouds and then the superclouds are emerging. I think you guys are the under innings of these emerging superclouds. And so it's a huge treading, the Goldman Sachs things of validation. So again, more data, the better, sorry, cool things happening. >>It is just it's everywhere. And the, uh, the diversity of use cases is amazing. I mean, I think from, you know, the Australia swimming team to, uh, to formula one to NASDAQ, it's just incredible to see what our >>Customers do. We see the great route. Good to see you. Thanks for coming on the cube. >>Pleasure to be here as always John. Great to see you. Thank you. Yeah. >>Thanks for, thanks for sharing. All of the data is the key to the success. Data is the value proposition. You've seen the rise of superclouds because of the data advantage. If you can expose it, protect it and govern it, unleashes creativity and opportunities for entrepreneurs and businesses. Of course, you got to have the scale and the price performance. That's what doing this is the cube coverage. You're watching the leader in worldwide tech coverage here in person for any of us reinvent 2021 I'm John ferry. Thanks for watching.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Ongoing coverage of AWS reinvent virtual Cuba's Gone Virtual along with most events these days are all events and continues to bring our digital coverage of reinvent With me is Rahul Pathak, who is the vice president of analytics at AWS A Ro. It's great to see you again. Welcome. And thanks for joining the program. >>They have Great co two and always a pleasure. Thanks for having me on. >>You're very welcome. Before we get into your leadership discussion, I want to talk about some of the things that AWS has announced. Uh, in the early parts of reinvent, I want to start with a glue elastic views. Very notable announcement allowing people to, you know, essentially share data across different data stores. Maybe tell us a little bit more about glue. Elastic view is kind of where the name came from and what the implication is, >>Uh, sure. So, yeah, we're really excited about blue elastic views and, you know, as you mentioned, the idea is to make it easy for customers to combine and use data from a variety of different sources and pull them together into one or many targets. And the reason for it is that you know we're really seeing customers adopt what we're calling a lake house architectural, which is, uh, at its core Data Lake for making sense of data and integrating it across different silos, uh, typically integrated with the data warehouse, and not just that, but also a range of other purpose. Both stores like Aurora, Relation of Workloads or dynamodb for non relational ones. And while customers typically get a lot of benefit from using purpose built stores because you get the best possible functionality, performance and scale forgiven use case, you often want to combine data across them to get a holistic view of what's happening in your business or with your customers. And before glue elastic views, customers would have to either use E. T. L or data integration software, or they have to write custom code that could be complex to manage, and I could be are prone and tough to change. And so, with elastic views, you can now use sequel to define a view across multiple data sources pick one or many targets. And then the system will actually monitor the sources for changes and propagate them into the targets in near real time. And it manages the anti pipeline and can notify operators if if anything, changes. And so the you know the components of the name are pretty straightforward. Blues are survivalists E T Elling data integration service on blue elastic views about our about data integration their views because you could define these virtual tables using sequel and then elastic because it's several lists and will scale up and down to deal with the propagation of changes. So we're really excited about it, and customers are as well. >>Okay, great. So my understanding is I'm gonna be able to take what's called what the parlance of materialized views, which in my laypersons terms assumes I'm gonna run a query on the database and take that subset. And then I'm gonna be ableto thio. Copy that and move it to another data store. And then you're gonna automatically keep track of the changes and keep everything up to date. Is that right? >>Yes. That's exactly right. So you can imagine. So you had a product catalog for example, that's being updated in dynamodb, and you can create a view that will move that to Amazon Elasticsearch service. You could search through a current version of your catalog, and we will monitor your dynamodb tables for any changes and make sure those air all propagated in the real time. And all of that is is taken care of for our customers as soon as they defined the view on. But they don't be just kept in sync a za long as the views in effect. >>Let's see, this is being really valuable for a person who's building Looks like I like to think in terms of data services or data products that are gonna help me, you know, monetize my business. Maybe, you know, maybe it's a simple as a dashboard, but maybe it's actually a product. You know, it might be some content that I want to develop, and I've got transaction systems. I've got unstructured data, may be in a no sequel database, and I wanna actually combine those build new products, and I want to do that quickly. So So take me through what I would have to do. You you sort of alluded to it with, you know, a lot of e t l and but take me through in a little bit more detail how I would do that, you know, before this innovation. And maybe you could give us a sense as to what the possibilities are with glue. Elastic views? >>Sure. So, you know, before we announced elastic views, a customer would typically have toe think about using a T l software, so they'd have to write a neat L pipeline that would extract data periodically from a range of sources. They then have to write transformation code that would do things like matchup types. Make sure you didn't have any invalid values, and then you would combine it on periodically, Write that into a target. And so once you've got that pipeline set up, you've got to monitor it. If you see an unusual spike in data volume, you might have to add more. Resource is to the pipeline to make a complete on time. And then, if anything changed in either the source of the destination that prevented that data from flowing in the way you would expect it, you'd have toe manually, figure that out and have data, quality checks and all of that in place to make sure everything kept working but with elastic views just gets much simpler. So instead of having to write custom transformation code, you right view using sequel and um, sequel is, uh, you know, widely popular with data analysts and folks that work with data, as you well know. And so you can define that view and sequel. The view will look across multiple sources, and then you pick your destination and then glue. Elastic views essentially monitors both the source for changes as well as the source and the destination for any any issues like, for example, did the schema changed. The shape of the data change is something briefly unavailable, and it can monitor. All of that can handle any errors, but it can recover from automatically. Or if it can't say someone dropped an important table in the source. That was part of your view. You can actually get alerted and notified to take some action to prevent bad data from getting through your system or to prevent your pipeline from breaking without your knowledge and then the final pieces, the elasticity of it. It will automatically deal with adding more resource is if, for example, say you had a spiky day, Um, in the markets, maybe you're building a financial services application and you needed to add more resource is to process those changes into your targets more quickly. The system would handle that for you. And then, if you're monetizing data services on the back end, you've got a range of options for folks subscribing to those targets. So we've got capabilities like our, uh, Amazon data exchange, where people can exchange and monetize data set. So it allows this and to end flow in a much more straightforward way. It was possible before >>awesome. So a lot of automation, especially if something goes wrong. So something goes wrong. You can automatically recover. And if for whatever reason, you can't what happens? You quite ask the system and and let the operator No. Hey, there's an issue. You gotta go fix it. How does that work? >>Yes, exactly. Right. So if we can recover, say, for example, you can you know that for a short period of time, you can't read the target database. The system will keep trying until it can get through. But say someone dropped a column from your source. That was a key part of your ultimate view and destination. You just can't proceed at that point. So the pipeline stops and then we notify using a PS or an SMS alert eso that programmatic action can be taken. So this effectively provides a really great way to enforce the integrity of data that's going between the sources and the targets. >>All right, make it kindergarten proof of it. So let's talk about another innovation. You guys announced quicksight que, uh, kind of speaking to the machine in my natural language, but but give us some more detail there. What is quicksight Q and and how doe I interact with it. What What kind of questions can I ask it >>so quick? Like you is essentially a deep, learning based semantic model of your data that allows you to ask natural language questions in your dashboard so you'll get a search bar in your quick side dashboard and quick site is our service B I service. That makes it really easy to provide rich dashboards. Whoever needs them in the organization on what Q does is it's automatically developing relationships between the entities in your data, and it's able to actually reason about the questions you ask. So unlike earlier natural language systems, where you have to pre define your models, you have to pre define all the calculations that you might ask the system to do on your behalf. Q can actually figure it out. So you can say Show me the top five categories for sales in California and it'll look in your data and figure out what that is and will prevent. It will present you with how it parse that question, and there will, in line in seconds, pop up a dashboard of what you asked and actually automatically try and take a chart or visualization for that data. That makes sense, and you could then start to refine it further and say, How does this compare to what happened in New York? And we'll be able to figure out that you're tryingto overlay those two data sets and it'll add them. And unlike other systems, it doesn't need to have all of those things pre defined. It's able to reason about it because it's building a model of what your data means on the flight and we pre trained it across a variety of different domains So you can ask a question about sales or HR or any of that on another great part accused that when it presents to you what it's parsed, you're actually able toe correct it if it needs it and provide feedback to the system. So, for example, if it got something slightly off you could actually select from a drop down and then it will remember your selection for the next time on it will get better as you use it. >>I saw a demo on in Swamis Keynote on December 8. That was basically you were able to ask Quick psych you the same question, but in different ways, you know, like compare California in New York or and then the data comes up or give me the top, you know, five. And then the California, New York, the same exact data. So so is that how I kind of can can check and see if the answer that I'm getting back is correct is ask different questions. I don't have to know. The schema is what you're saying. I have to have knowledge of that is the user I can. I can triangulate from different angles and then look and see if that's correct. Is that is that how you verify or there are other ways? >>Eso That's one way to verify. You could definitely ask the same question a couple of different ways and ensure you're seeing the same results. I think the third option would be toe, uh, you know, potentially click and drill and filter down into that data through the dash one on, then the you know, the other step would be at data ingestion Time. Typically, data pipelines will have some quality controls, but when you're interacting with Q, I think the ability to ask the question multiple ways and make sure that you're getting the same result is a perfectly reasonable way to validate. >>You know what I like about that answer that you just gave, and I wonder if I could get your opinion on this because you're you've been in this business for a while? You work with a lot of customers is if you think about our operational systems, you know things like sales or E r. P systems. We've contextualized them. In other words, the business lines have inject context into the system. I mean, they kind of own it, if you will. They own the data when I put in quotes, but they do. They feel like they're responsible for it. There's not this constant argument because it's their data. It seems to me that if you look back in the last 10 years, ah, lot of the the data architecture has been sort of generis ized. In other words, the experts. Whether it's the data engineer, the quality engineer, they don't really have the business context. But the example that you just gave it the drill down to verify that the answer is correct. It seems to me, just in listening again to Swamis Keynote the other day is that you're really trying to put data in the hands of business users who have the context on the domain knowledge. And that seems to me to be a change in mindset that we're gonna see evolve over the next decade. I wonder if you could give me your thoughts on that change in the data architecture data mindset. >>David, I think you're absolutely right. I mean, we see this across all the customers that we speak with there's there's an increasing desire to get data broadly distributed into the hands of the organization in a well governed and controlled way. But customers want to give data to the folks that know what it means and know how they can take action on it to do something for the business, whether that's finding a new opportunity or looking for efficiencies. And I think, you know, we're seeing that increasingly, especially given the unpredictability that we've all gone through in 2020 customers are realizing that they need to get a lot more agile, and they need to get a lot more data about their business, their customers, because you've got to find ways to adapt quickly. And you know, that's not gonna change anytime in the future. >>And I've said many times in the The Cube, you know, there are industry. The technology industry used to be all about the products, and in the last decade it was really platforms, whether it's SAS platforms or AWS cloud platforms, and it seems like innovation in the coming years, in many respects is coming is gonna come from the ecosystem and the ability toe share data we've We've had some examples today and then But you hit on. You know, one of the key challenges, of course, is security and governance. And can you automate that if you will and protect? You know the users from doing things that you know, whether it's data access of corporate edicts for governance and compliance. How are you handling that challenge? >>That's a great question, and it's something that really emphasized in my leadership session. But the you know, the notion of what customers are doing and what we're seeing is that there's, uh, the Lake House architectural concept. So you've got a day late. Purpose build stores and customers are looking for easy data movement across those. And so we have things like blue elastic views or some of the other blue features we announced. But they're also looking for unified governance, and that's why we built it ws late formation. And the idea here is that it can quickly discover and catalog customer data assets and then allows customers to define granular access policies centrally around that data. And once you have defined that, it then sets customers free to give broader access to the data because they put the guardrails in place. They put the protections in place. So you know you can tag columns as being private so nobody can see them on gun were announced. We announced a couple of new capabilities where you can provide row based control. So only a certain set of users can see certain rose in the data, whereas a different set of users might only be able to see, you know, a different step. And so, by creating this fine grained but unified governance model, this actually sets customers free to give broader access to the data because they know that they're policies and compliance requirements are being met on it gets them out of the way of the analyst. For someone who can actually use the data to drive some value for the business, >>right? They could really focus on driving value. And I always talk about monetization. However monetization could be, you know, a generic term, for it could be saving lives, admission of the business or the or the organization I meant to ask you about acute customers in bed. Uh, looks like you into their own APs. >>Yes, absolutely so one of quick sites key strengths is its embed ability. And on then it's also serverless, so you could embed it at a really massive scale. And so we see customers, for example, like blackboard that's embedding quick side dashboards into information. It's providing the thousands of educators to provide data on the effectiveness of online learning. For example, on you could embed Q into that capability. So it's a really cool way to give a broad set of people the ability to ask questions of data without requiring them to be fluent in things like Sequel. >>If I ask you a question, we've talked a little bit about data movement. I think last year reinvent you guys announced our A three. I think it made general availability this year. And remember Andy speaking about it, talking about you know, the importance of having big enough pipes when you're moving, you know, data around. Of course you do. Doing tearing. You also announced Aqua Advanced Query accelerator, which kind of reduces bringing the computer. The data, I guess, is how I would think about that reducing that movement. But then we're talking about, you know, glue, elastic views you're copying and moving data. How are you ensuring you know, maintaining that that maximum performance for your customers. I mean, I know it's an architectural question, but as an analytics professional, you have toe be comfortable that that infrastructure is there. So how does what's A. W s general philosophy in that regard? >>So there's a few ways that we think about this, and you're absolutely right. I think there's data volumes were going up, and we're seeing customers going from terabytes, two petabytes and even people heading into the exabyte range. Uh, there's really a need to deliver performance at scale. And you know, the reality of customer architectures is that customers will use purpose built systems for different best in class use cases. And, you know, if you're trying to do a one size fits all thing, you're inevitably going to end up compromising somewhere. And so the reality is, is that customers will have more data. We're gonna want to get it to more people on. They're gonna want their analytics to be fast and cost effective. And so we look at strategies to enable all of this. So, for example, glue elastic views. It's about moving data, but it's about moving data efficiently. So What we do is we allow customers to define a view that represents the subset of their data they care about, and then we only look to move changes as efficiently as possible. So you're reducing the amount of data that needs to get moved and making sure it's focused on the essential. Similarly, with Aqua, what we've done, as you mentioned, is we've taken the compute down to the storage layer, and we're using our nitro chips to help with things like compression and encryption. And then we have F. P. J s in line to allow filtering an aggregation operation. So again, you're tryingto quickly and effectively get through as much data as you can so that you're only sending back what's relevant to the query that's being processed. And that again leads to more performance. If you can avoid reading a bite, you're going to speed up your queries. And that Awkward is trying to do. It's trying to push those operations down so that you're really reducing data as close to its origin as possible on focusing on what's essential. And that's what we're applying across our analytics portfolio. I would say one other piece we're focused on with performance is really about innovating across the stack. So you mentioned network performance. You know, we've got 100 gigabits per second throughout now, with the next 10 instances and then with things like Grab it on to your able to drive better price performance for customers, for general purpose workloads. So it's really innovating at all layers. >>It's amazing to watch it. I mean, you guys, it's a It's an incredible engineering challenge as you built this hyper distributed system. That's now, of course, going to the edge. I wanna come back to something you mentioned on do wanna hit on your leadership session as well. But you mentioned the one size fits all, uh, system. And I've asked Andy Jassy about this. I've had a discussion with many folks that because you're full and and of course, you mentioned the challenges you're gonna have to make tradeoffs if it's one size fits all. The flip side of that is okay. It's simple is you know, 11 of the Swiss Army knife of database, for example. But your philosophy is Amazon is you wanna have fine grained access and to the primitives in case the market changes you, you wanna be able to move quickly. So that puts more pressure on you to then simplify. You're not gonna build this big hairball abstraction layer. That's not what he gonna dio. Uh, you know, I think about, you know, layers and layers of paint. I live in a very old house. Eso your That's not your approach. So it puts greater pressure on on you to constantly listen to your customers, and and they're always saying, Hey, I want to simplify, simplify, simplify. We certainly again heard that in swamis presentation the other day, all about, you know, minimizing complexity. So that really is your trade office. It puts pressure on Amazon Engineering to continue to raise the bar on simplification. Isn't Is that a fair statement? >>Yeah, I think so. I mean, you know, I think any time we can do work, so our customers don't have to. I think that's a win for both of us. Um, you know, because I think we're delivering more value, and it makes it easier for our customers to get value from their data way. Absolutely believe in using the right tool for the right job. And you know you talked about an old house. You're not gonna build or renovate a house of the Swiss Army knife. It's just the wrong tool. It might work for small projects, but you're going to need something more specialized. The handle things that matter. It's and that is, uh, that's really what we see with that, you know, with that set of capabilities. So we want to provide customers with the best of both worlds. We want to give them purpose built tools so they don't have to compromise on performance or scale of functionality. And then we want to make it easy to use these together. Whether it's about data movement or things like Federated Queries, you can reach into each of them and through a single query and through a unified governance model. So it's all about stitching those together. >>Yeah, so far you've been on the right side of history. I think it serves you well on your customers. Well, I wanna come back to your leadership discussion, your your leadership session. What else could you tell us about? You know, what you covered there? >>So we we've actually had a bunch of innovations on the analytics tax. So some of the highlights are in m r, which is our managed spark. And to do service, we've been able to achieve 1.7 x better performance and open source with our spark runtime. So we've invested heavily in performance on now. EMR is also available for customers who are running and containerized environment. So we announced you Marnie chaos on then eh an integrated development environment and studio for you Marco D M R studio. So making it easier both for people at the infrastructure layer to run em are on their eks environments and make it available within their organizations but also simplifying life for data analysts and folks working with data so they can operate in that studio and not have toe mess with the details of the clusters underneath and then a bunch of innovation in red shift. We talked about Aqua already, but then we also announced data sharing for red Shift. So this makes it easy for red shift clusters to share data with other clusters without putting any load on the central producer cluster. And this also speaks to the theme of simplifying getting data from point A to point B so you could have central producer environments publishing data, which represents the source of truth, say into other departments within the organization or departments. And they can query the data, use it. It's always up to date, but it doesn't put any load on the producers that enables these really powerful data sharing on downstream data monetization capabilities like you've mentioned. In addition, like Swami mentioned in his keynote Red Shift ML, so you can now essentially train and run models that were built in sage maker and optimized from within your red shift clusters. And then we've also automated all of the performance tuning that's possible in red ships. So we really invested heavily in price performance, and now we've automated all of the things that make Red Shift the best in class data warehouse service from a price performance perspective up to three X better than others. But customers can just set red shift auto, and it'll handle workload management, data compression and data distribution. Eso making it easier to access all about performance and then the other big one was in Lake Formacion. We announced three new capabilities. One is transactions, so enabling consistent acid transactions on data lakes so you can do things like inserts and updates and deletes. We announced row based filtering for fine grained access control and that unified governance model and then automated storage optimization for Data Lake. So customers are dealing with an optimized small files that air coming off streaming systems, for example, like Formacion can auto compact those under the covers, and you can get a 78 x performance boost. It's been a busy year for prime lyrics. >>I'll say that, z that it no great great job, bro. Thanks so much for coming back in the Cube and, you know, sharing the innovations and, uh, great to see you again. And good luck in the coming here. Well, >>thank you very much. Great to be here. Great to see you. And hope we get Thio see each other in person against >>I hope so. All right. And thank you for watching everybody says Dave Volonte for the Cube will be right back right after this short break

>> Announcer: Live from Boston, Massachusetts, it's theCUBE. Covering IFS World Conference 2019. Brought to you by IFS. >> Welcome back to Boston everybody, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, I'm here with my co-host Paul Gillin. This is IFS World Conference 2019, theCUBE's second year covering this conference. Michael Ouissi is here. He's the Chief Customer Officer at IFS. And Raul Sahas. Industry Partner, Enterprise Application Services at TCS, a platinum partner at IFS World. Gents, welcome to theCUBE. >> Thank you. >> Thank you for having us. >> You're very welcome. So last night I poked around the customer event and I was impressed with the number of partners here. I think the number is 400, is the public number. What is it about the ecosystem that's attracted to IFS? >> Well, first of all, I think the ecosystem has now understood that we have renewed our commitment to the ecosystem. That is something a shift in mindset in IFS that is demanded by our customers, that our customers actually ask of us especially while we're moving into also more global corporations, and win more business there. They appreciate the choice of either IFS or our partners, or a combination of our partners and IFS actually helping them deliver the value that they expect from an ERP solution. >> So Rahul, from your perspective, so TCS you're obviously platinum partner so you're making a big investment. Why, what's happening in the market place? Where's the momentum with the tailwind? >> If you look at TCS, TCS is obviously helping customers to become business 4.0 organization, which is all about harnessing the abundance of possibilities around digital technologies and getting more intelligent, better, lean, harmonized, standardized. And so that's where we believe we are partnering with, and we are trying to leverage the ecosystem and one of the ecosystem obviously partners are IFS, which is a strategy partnership for us. And we believe that the investments that IFS has made and some of the unique last-mile solutions are going to help us to deliver those different shaded offerings to the customer, and create newer partnerships with them. >> Michael your role is a net new role at IFS, did you get to write your own job description? I guess, what does a Chief Customer Officer do? >> Well, first of all, well, in a sense yes. We actually did specify exactly what that role is, and we did discuss what the best is for the journey we want to get on, when Darren asked me to take on the role. And what a Chief Customer Officer does, and there's a specific reason why we're doing it that way, a Chief Customer Officer really is heading up, and that's what I'm doing, is I'm heading up all customer-facing functions within IFS. So from sales, to pre-sales, to support, to services. So it's all the customer-facing functions, coming from how do we engage with a customer, pre-sales, and after sales. And the reason why we did it that way is we wanted to have complete ownership and accountability for the transformation that we underwent and that we wanted to go through because we really needed to make sure that all parts of the business were aligning around this transformation, and pulling in the same direction. And that's why this role got created newly. >> So what's the nature of the partnership, what the history of the partnership? How did it start, and where do you guys want to take it? >> Well I think we have a obviously longstanding partnership with IFS. And I think both of the organizations have a deep mutual respect. And I think that one thing that we are trying to see the centricity around our partnership is all about the customer. We keep the customer and we want to ensure that we help our customers. We're customer-first organizations. And obviously the investments that IFS made, especially in the field services area, ERP area. I guess those are the areas which is helping, because ERP, if you see, one of the strategic lever for an organization to elevate their digital agenda, and get the right infrastructure in place, the right partner in place, to ensure that they create a differentiation and create exponential value for the customer. And that's exactly where IFS and TCS are looking at the market, and ensuring that we are helping our customer create exponential value for themselves in the market. >> Michael: Yeah and I think that maybe adding to that, we share the same belief as well that actually the time of the monolithic ERP, one solution for a huge enterprise- >> Who are you talking about? (Laughter) >> They are gone, those days are gone. I think it's about blended solutions where the ERP is much more agile, it has to be much more open and allow for much more agile deployments and much more agile development around the core ERP. So that actually customers can digitally transform, because it's all about speed. And TCS sees it the same way so we've got the same view. >> But the cloud mindset has changed that right Paul? >> Absolutely. And Rahul I'm interested the companies like Tata historically have done a lot of custom development work for customers that we have been hearing from Darren on down today is no customization. What value do you add to a customer bringing in an IFS solution? >> See there are two things here, very simple. One is basically customers are moving from best in class to the sub-breed, that's quite evident. And secondly, while IFS brings the software expertise, we bring the industry expertise. We bring the domain expertise. We bring the SI, system integration expertise. And that's where, it's a very strategic combination. Strategic combination is helping the customer to get the right software, the right domain expertise, the right industry expertise. And together they're helping them to address their business requirements, business need, and last mile critical mile needs that they need to differentiate themselves in the marketplace. And, as a result, create exponential value, and also, a great customer experience for the customers. >> Paul: So, how does that engage and differ from a more traditional one where you would come in and you would build custom screens and custom processes? You're not doing that. Now, what does that relationship look like? >> Yeah so I think if you see the scratch approach, obviously it has really transformed over the course of time. Customers are wanting off the shelf, out of the box products. Best of the beat products to help them differentiate their business function, create exponential value for the customer for that business function as a matter of say, service. If I look at fin services as an example, and you talk about telcos, you talk about utilities. Where last mile delivery, last mile solution for that customer is very very important to create the positive customer experience. And the investments that IFS have made in there makes them a premium choice. And that's where I believe that developing something with scratch means you know you're boarding the entire ocean again. And whereas we have got softwares like, IFS build softwares which have invested their years of expertise, the years of, I would say, competency in building that. Getting the best of the breed solution, get the best KPIs into there in this solution, gives the customer a choice. A ready choice to take, to expedite their time to reality, time to value, and time to production reality. >> So, a few times now, Raoul, you've mentioned last mile solutions. I like that term, I think it has meaning. Especially deep in specific industries. And I think the intent is so that you don't have to do customizations. And I asked Aaron about tailoring, which he said, I wouldn't use that word. That wasn't my word, by the way, that was Christian's word. He used that in his keynote. So I'm trying to understand here. I think what Christian meant is look, we got this API platform to allow people to bring in whatever solutions they want, if it's a RPA solution, or a blockchain solution, or some AI module, they can bring that in and tailor it for their needs, as opposed to customizing the software. Is that correct? >> I think when you listen to Darren, what he's talking about is customizing the core, which very often has happened in the past, where customizations have gone into the core, have been mandated to be on the core platform, which then actually has customers being stuck at some stage on the platform upgrades becoming paid for. So with Christian's talk track around the APIs, API enabling the whole solution so that the core actually remains untouched. There will always be customizations, because customers need to differentiate. But they will be outside the core. There will be a level that you can upgrade the core solutions, you will have those maintained either application services, which will be custom out of the box solutions, best in breed, that actually tap into what we're doing. Or actually you'll have bespoke solutions that you will write yourself, and that is then a choice a customer can make, but without actually having the pain of not being able to upgrade the very stable, very performant transactional backbone. >> So the API announcements give you guys a real opportunity to do integrations, right? And it's been harder to do integrations. But that now, to your previous question, opens up I would think a whole new tam for you all. Can you comment? >> Oh absolutely. As I said, bringing exponential value means integrating and delivering a frictionless business. And that's where it'll fit in, rightly fit in, and obviously that would result in creating exponential value for the customer. Not only they can differentiate themselves from the market but also get their product faster to the market, and ensure that also focus on custom centers as we are. >> So the core can be, it should be, Evergreen. We want people to get the new version as soon as possible. Bug fixes, security updates, et cetera. >> New functionality. >> New functionality, avoid custom mods, but rely on service providers and partners to do further integrations that make sense. >> Rahul, I want to ask you the same question we just asked Melissa Di Donato about digital transformation. I'm sure your company does a lot of that kind of consulting work. What are the mistakes that companies make that we hear that these transformation products, most of them fail. What are the biggest mistakes that companies make? >> Let me put it this way. I think there are three elements to it. I think digital transformation, see I think creating the agenda for the digital transformation, what you're expecting out of it is very very important. Creating a charter, what you want to expect, what is the output of it. Where do you want to take it. What does a futuristic organization on a digital platform means? It's very very important. I think if you look at TCS, our vision has been helping the customer get into a business 4.0 enterprise. I think we have made the agenda very very very clear. Now how we can mass personalize the experience for the customer, how you can leverage the ecosystem, how you can basically help the customer embrace the risk, and obviously harness the abundance. I believe these are the pillars of any transformation, or digital transformation, that customers are taking. I believe if we can stick to these agendas, I think getting to the production reality, seeing the success has become more evident. If you're going to go to the nitty gritty, I think there are many things, looking at the processes, making sure that they are harmonized, standardized and rationalized, getting the right KPIs in the business. So I think these are things that is very very important as a precursor to our digital transformation. Once we do that, we know that roads ahead will be much smoother than what it looks like. >> Is it more important to do a transformation with the customer at the center, with operational efficiency at the center, or can it be either? >> The customer centricity is very very key to all our organization at this point of time, because if you look at any organization at this point of time, they're looking at the customer experience as the top most agenda. Keeping the customer experience on the agenda, when you're trying to keep that agenda, it means that you are trying to bring up a customer first organization. So customer first organization, it just doesn't mean that you have a very intelligent front office, but also have a very intelligent back office. And gluing this two together, very intelligent mid office. So I guess customer centricity has to be on the top of the agenda, and then you have to ensure that your processes are streamlined, harmonized, standardized, lean, to meet that objective. >> Makes sense. >> So I think, for customer centricity, so I feel as though, but part of that's cultural, you know? And it's true, you said this earlier this morning. Some companies are customer centrics, some are product centric, some are competitive. And you can kind of tell the difference, especially when you're a customer. But I think true customer centricity mandates data access as central to the philosophy, the core. And I think the role that ERP provider or vendor provides is you have a data pipeline that gives access to an organization such that a digital transformation allows them to put data at their core, and then build whatever processes around it. I think that's a real challenge for incumbents especially where data's all over the place, in different stove pipes and silos. But your thoughts on the role of data in terms of digital transformation, and IFS's role in that regard? >> Okay. >> A long-winded question, but I haven't heard enough about data I guess. >> Okay, (laughs) I'll try it, sweet and short. I think data is absolutely key to anything we do. Once you have and when you go into a digital transformation, what you need to start with in my humble view is you need to start with what business outcome do you want to achieve? Most of the time it's customer centricity, it's something centered around the customer which you want to achieve. That will define both the digital transformation agenda, the KPI's you're measuring to, but also the flow of data and processes. So you will need to build your digital transformation agenda around the targets you have, and then define where does data need to reside, which data do I need to fulfill on that outcome? And I think that consistency going through that whole chain is actually something that very often isn't at the moment taken into account, but it's very often isolated efforts to do something fast without actually looking at the implications of what kind of transactional engine do I need, what kind of data exits do I need, and how do I get through the process to the KPI that I want to influence? >> Okay, and let me peel the onion on that, and I'd love for your thoughts. To me when you talk to a C-suite executive, what that business outcome ultimately comes down to is I want to increase revenue, so I want to cut my cost. Now of course if you're in a different hospital, you want to save lives. But generally in a commercial business, increase revenue, cut cost. Now how I get there, I might want to have a better customer service organization to get cohort sales or follow on sales. I mean the strategy is different. But it comes back to data and how data affects the monetization of my organization, whether it's increasing revenue or cutting cost. Do you buy that premise, or am I just simplifying it too much? >> No, completely agreed. I think in a business world it's always either top line or bottom line, but the challenges are obviously very different from company to company and from industry to industry. So if you're looking at manufacturing companies, trying to actually be less commoditized and getting into a situation where they stabilize revenue streams, increase margins, servitization is the name of the game. Very different value proposition to, for example in the finance industry, in banking and insurance. So there are very different models here where there it's about ease of use and speed of actually interacting and transacting as a customer with the company. So very different value propositions, very different data streams you need to tap into. And things you need to know about your customer, and know about the service you're providing. So completely agree with it is always about revenue and cost, that's what businesses are in for. But eventually, data is at the core, but how to get that data, which data you need, that is then specific to each. >> And bringing it back to IFS, your ability to go that last mile as you've been saying Rahul allows companies to think, construction and engineering, supply chain, contractors, just more efficiently managing their ecosystem, their resources to either cut costs or do more business and scale. >> Exactly. And that's really where the whole idea of API, enabling the whole suite came from, enabling the reuse of services, the reuse of data within those services, exposing it transparently, making it available for customers to then use in their digital transformation effort. Whatever they need. We can't predict and we can't actually preempt what a customer will need, we'll just need to make it all available, and then with partners like TCS, make sure we actually go on to the right journey with a customer to digitally transform and use the right data streams. We can make it easy and accessible. >> And that's the different between a platform and a product. To the extent that you can deliver an API-enabled system, it becomes a platform that you can evolve versus a product that you install and manage. Final thoughts, Rahul? >> I think what we discussed obviously, I fully agree on that. And as I mentioned that our take is to ensure that we have the customer built future systems enterprises, and we believe our partnership with IFS is a very key and strategic partnership for us to achieve the same, and we have some early success, and we want to ensure that we scale that, we ensure we go to the market together, and create a differentiation for our customers. >> Michael, your thoughts. Where do you want to see this ecosystem go? >> Where do I want to see it go? Well I want to see it thrive. I want partners to be successful with their customers on IFS implementations. That's what our ambition is. We need to provide world class technology, a world class platform, as you said, that actually then can be used to help the digital transformation that all our customers will have to go through in one or the other way. >> Success is outcome driven. Good outcomes mean people come back, more business? >> Absolutely, absolutely. >> Exactly. >> That's core to our DNA, I'm sure core to DNA to IFS as well. Repeat customers. >> Congratulations on the partnerships, and good luck going forward. >> Thank you very much. >> Appreciate you coming on theCUBE, you're welcome. >> Thank you very much. >> Thank you. >> All right thank you for watching everybody, we'll be right back with our next guest, Paul Gillan and Dave Vellante. You're watching theCUBE. (electronic jingle)

>> live from Boston, Massachusetts. It's the you covering your red have some twenty nineteen. You >> buy bread >> and welcome back to Boston as we continue our coverage here on the Cube of the Red Hat Summit twenty nineteen, along with two minimum. I'm John Walls, and we're now joined by the V, P and C e o of Delta Airlines. Mr. Rahul Samad. Good to see you, sir. Good to see you too, Jamie, For joining us. And you have a little keynote appearance coming through with five. Forty five s. So we will not be well, we won't hold you back. >> But thank you for squeezing this, and we really do. We appreciate that. >> First off, let's talk about just Delta >> from the macro level in terms of the technology emphasis that you have tohave now, obviously running an airline. Extraordinarily complex, sophisticated systems. But how the view of technology has evolved. Maybe over the last five, ten years, where it is today. >> Yeah. I mean, you know, technology has always been core. I mean, we had a reservation systems going back to the sixties on IBM mainframes, but it's as as things have digitalized and the customer experience has become the key and empowering our employees with insights and tools so they can take better care. Even better care of the customers has become the other problem, so it's kind of a two pronged approach to digitalize ing. The company and technology has become central Now. Our culture is all about people, and our frontline teammates take great care off our customers. But then technology plays a great role in empowering them to do that even better. Sighting. It's Ah, within the company. We say, you know, we're transforming technology until competitive advantage for Delta, and so relevance is not a problem. We are extremely relevant to the company, have been forever. But I think it's getting Mohr and Maury even more so today, especially at the customer interaction. Touch point. >> So we're who we understand how important technology is. You know, in your field there talk a little bit about kind of the role of the CEO. How you know, what's the business asking for you? What? The stressors on that and a little bit of that dynamic. >> Yeah, I think. Look, >> you know, I'm an equal member of the CEOs executive team, but you still have to earn your right. And so things like reliability and stability, availability, security become table stakes. And so, in sixteen and seventeen, I started in two thousand sixteen and we needed to focus on that. So I came in, you know, starry eyed going. I'm gonna digitalize the airline experience. But what I needed to focus on was, you know, the table stakes and sort of earning my place at that table rightfully And then that gives you permission to really start collaborating with the business and bringing technology solutions to bear on business opportunity. So we're there now, so it's really exciting time we launched in the Enterprise. Why the digital transformation of the company in early two thousand eighteen, which is again both employees and customers focus. And so clearly we are central to the role ofthe Delta and the airline. >> You just can you share with us? What are some of those key goals of that digital transformation? Obviously, you know, we're all your end ultimate customers wait, value there, but, you know, is data at the core of that digital train. >> You said it. You took >> the words right out of my mouth. You know, I mean any legacy legacy is like a four letter word when it comes to technology everywhere else. We take great pride in our ninety plus year legacy, but not so much with our aging technology. So part of it was, of course, you know you got to modernize the technology, so we're doing that in the background. But data was strewn all over the company. We know a lot about our customers, but we hadn't brought it together. So now we have we have a three sixty degree view. We call it the single view of the customer. Along with that, we also have a single view of the operation. So those two data repositories are now real time and building a pea eye's on top of that and unlocking the power of that data. Two equipped Like I said, the frontline employees, they've now got tools there mobile enable, and they have insights that they can take to serving the customer and then directly guessing both off your customers and directly with you. We've mobile enable the experience and given you ah, whole lot more across the entire traveled ribbon. So >> what are you >> learning then or what have you learned about customers then, in terms of that data collection, I'm sure. I mean, there's there's pretty first level stuff when they buy tickets where the travel to that kind of thing. But then I guess going deeper and learning more about behaviors and impulsive sze impulsive reactions to certain use. Whatever. >> Yep. What do you get it out? We're just >> starting. You know, that's an interesting when, John, because we we do have it. It's a huge data repository, and we're just starting to get the use case is built on that and where we focus our attention is on service. Recovered because we >> do it with >> service would call recovery. So you know whether when weather goes bad and the airline, you know, goes into what we call an irregular operation or an IRA in airline terms, you gotta put that back together and you've got to recover the customers. They might be delayed. They might have suffered a canceled flight or miss bag in spite of all our best efforts. And that's where we're applying the single view of the customer because we know the history ofthe all your interactions with us. And so at the top of the house. The executives decided that that's where we wanted to go. We wanted to make sure that we could acknowledge to you we could recognize interruptions on your next travel with us. But while it's happening, we could actually help get you out of that and on your way again. So now we're moving from that two more revenue generation and targeted offers and targeted recognition. But where we started was really around service recovery because we think you know that that's where customers sometimes feel the pain azaz. Muchas way try for them not to. But you know, whether it's not our ally at times >> and making the business case for that, then are you able to then see how behavior is modified in terms of whether it's customer reaction or customer uptake on your services, whatever and how that's translating to either pretension or business growth or something >> along Absolutely. Even even with the early use cases that we've put forward, we're seeing that I mean the the expectations off airlines over time the customers have and that they're going to use data and technology. Ah, effectively is, I think, fairly low on DSO the when we go up and our folks walk down the aisle with the handheld device on board and they acknowledge someone for hitting a million mile milestone or for achieving diamond status >> in a way, customers are are impressed and, you know, and then you go >> the next level and you're able to take care of them on a on a delay or on a cancel and re accommodate. Before they even called the service center. They've been re accommodated and rebuild. Those are things that I mean, they engender so much loyalty. Andi, I think its technology equipping our our employees in a big way. So the employees are doing great. Now you've put another helping of technology on top of it. Customers are are paying us for that way. Have ah revenue premium on. >> So you talk about internal, Tell us a little bit about your team. How much has this been in a digital transfer? Information is retraining. So how much you trying to get people from the outside? You know, we go to shows like this. Companies like yours are heavy recruiting mode. Typical absent skill sets are tough. You know what you're looking for? And give a little >> Yes, we've had >> Ah, very seasoned, you know, t team an organization. As you would expect, an attrition very low at adult. What what I needed to do was bring in about fifteen to twenty percent of the total team. Strength is knew. That's what I brought in about six hundred people in the last thirty six months. And those were people who were hired for contemporary skills. I call them Been there, done that type people. So Cloud Engineers, FBI people, agile cyber expert, and blending that with the seasoned veterans that know a lot about Del Tighty and know a lot about the airline domain was really important. So you didn't create haves and have nots because that could have easily happened. And then that causes a rupture. So we spent a lot of time on integrating those those two halves and making sure that this was a sort of a shot of adrenaline into the bloodstream. But the blood stream is strong, and the combined force of those two groups has been terrific for us. So that that's the other thing I would say. And I'm not saying that because I'm sitting here in the Red Hat Summit is the use ofthe partners, not just for products but a set of strategic partners. Whether it's Red Hat or IBM or Microsoft, right, a small set of partners becomes a force multiplier from a talent perspective. So they become an accelerant to the transformation. >> Well, you brought it up. Talk a little bit of partnerships. How do you look at this? Is it? I want to have a primary one. Is it a handful? Talk about that depth of relationship and what you're looking for from that Federico >> system. Absolutely. And look, we've got about a dozen that I meet at the the CEO president type level on an annual basis where I would say, you know, ten to twelve that we really are tight with and that are inside the tent. They understand the pillars off our transformation, and they know where they can provide swift acceleration to our transformation. And of course, right at is one and the others that I named. But they're they're they're giving us not just the product and the service, but they're in there helping us with setting the strategy and making sure that they put the right team on the ground with us or training our people. So it runs the gamut from, you know, sort of the system integrator type all the way to open source product pipes >> for the Red Happy's. Can you highlight What are you using? And, you know, are they involved in some of that training and transformation? >> And I think you know, >> the behind the scenes sort of under the hood. The platform is a service that gives us tremendous interoperability. We are young in our journey to the cloud, and like any big company, we're going to be multi cloud and hybrid. So we built our private cloud. We've got the the red had open shift container platform hosted in our private cloud. And so we're moving a lot of application components into that >> prior to that. And that's only >> about a year that we've been doing that. But prior to that, we've been big Lennox users, you know, Red Hat Enterprise, Lin X J boss, a whole plethora of products. But I think the platform is the service is really helping us with our cloud journey, and we're we're totally jazzed about that. >> You talked about hiring and six hundred two employees in a very short period of time class door. It just stood up and said, Hey, Delta Airlines, one of the top of companies for hiring software engineers >> after it was a very nice distinction to get. What does that do? Does that mean terms of first off? How do you do >> that in such an environment where you know everybody's after the same market, if you >> will. I think, you know, how do you feel about something today? I'm I'm validate a little bit really proud of that. And it actually wasn't something that you self >> nominate or you even have, you know, some kind of a selection process. It just arrived, you know, we didn't know about it. And those are some of the best ones because it's also recognition from your employees >> because they're the >> ones who are voting with their their posts and their the ones that are telling glass Door that this is a terrific place to work and we're doing a lot of new things and we're doing them at speed and it's very relevant to the customer experience into our front line employees experience. So >> there's an impact >> story this is this is the great thing about working for an airline. There's no place to run or hide when you're in I t. Because if it's down within fifteen minutes were front page news right somewhere. And so we strive hard to make sure it's never down. And on top of that, we're building, you know, these great digital experiences. So it's been really gratifying, and I think it's going to help us even further with our recruiting efforts. >> Yeah, it's interesting, you know, without getting political. It's like you're doing this modernization. But I mean, you've got heavy regulations on, you know, just some of the basic infrastructure of your industry is a little bit antiquated, you know, and comments >> on that. Well, I think it's It's a dichotomy, and I don't think we're >> unique. And I came out of banking to insurance to airlines, And you think that the way the financial services guys spend money on it, there would be no aging technology and there'd be no you no, none of that. Webb off connectivity. It's not true. I think any company that's been around forty fifties, you know, years >> has all the generations of technology still existing. So our Endeavour >> is to make sure that we deprecate out of that technology as quickly as we can and where it's useful. I mean, >> we still use mainframes >> for a really good purpose, and someone asked me just couple of weeks ago would you get out of it? And I said, >> No, it's a half a billion dollars project >> and it's a high risk project and IBM serves me really well, And for that purpose, the mainframe is exactly what the doctor ordered. So this >> isn't about >> ideology, right? This is about purpose built and custom build. So if there's a technology that fits the purpose, I'm gonna leave well alone. And I'm going to train people and recruit people so that I don't have a talent issue in ten or twenty years when it comes to mainframe people. We've had no problem in getting apprentices and keeping our mainframe talent pipeline gold so they never get away from it. >> Can you give us just a little sneak peek on the keynote tonight? >> I mean, just a maybe a high >> level here, a couple of things just for John, and it's going to be a fireside with Jim you'LL have to come in and we'll be there and listen. But I think Jim Jim's probably got a few questions up his sleeve is also, you know, Jim's got a heritage with Delta. He was our >> chief operating officer until I think about ten years ago. And so it >> should be a fun. He hasn't told me what he's going to ask, so it's gonna be interesting as to which way he's going to come. But I would assume he >> wants to talk about, you know, digital transformation and and, of course, how right ATS helping I would, I would seem there's going to be a question or two about about red >> handed. My only warning, obi, is what >> I hear when I walk on a Delta flight. Let's fasten your seat belt. >> Yes, there. Thank you. Thanks for the time and looks forward to Aquino tonight. Thank you so much, guys. All right. Back with more here on the Cube were watching coverage right now. Right. Had summit >> and we're in Boston, Massachusetts

(futuristic electronic music) >> Live from Las Vegas, its theCUBE covering AWS re:Invent 2018. Brought to you by Amazon Web Services, Intel, and their ecosystem partners. >> Hey welcome back everyone. Live here in Las Vegas with AWS, Amazon Web Services, re:Invent 2018's CUBE coverage. Two sets, wall-to-wall coverage here on the ground floor. I'm here with Dave Vellante. Dave, six years we've been coming to re:Invent. Every year except for the first year. What a progression. We got great news. Always raising the bar, as they say at Amazon. This year, big announcements. One of them is blockchain. Really kind of laying out early formation of how they're going to roll out, thinking about blockchain. We're here to talk about here, with Rahul Pathak, who's the GM of analytics, and data lakes, and blockchain. Managing that. And Shawn Bice who's the vice president of non-relational databases. Guys, welcome to theCUBE. >> Thank you. >> Thank you, it's great to be here. >> I wish my voice was a little bit stronger. I love this segment. You know, we've been doing blockchain. We've been following one of the big events in the industry. If you separate out the whole token ICO scam situation, token economics is actually a great business model opportunity. Blockchain is an infrastructure, a decentralized infrastructure, that's great. But it's early. Day one really for you guys in a literal sense. How are you guys doing blockchain? Take a minute to explain the announcement because there are use cases, low-hanging use cases, that look a lot like IoT and supply chain that people are interested in. So take a minute to explain the announcements and what it means. >> Absolutely, so when we began looking at blockchain and blockchain use cases, we really realized there are two things that customers are trying to do. One case is really keep an immutable record of transactions and in a scenario where centralized trust is okay. And for that we have Amazon QLDB, which is an immutable cryptographically verifiable ledger. And then in scenarios where customers really wanted the decentralized trust and the smart contracts, that's where blockchain frameworks like Hyperledger Fabric and Ethereum play a role. But they're just super complicated to use and that's why we built Managed Blockchain, to make it easy to stand up, scale, and monitor these networks, so customers can focus on building applications. And in terms of use cases on the decentralized side, it's really quite diverse. I mean, we've got a customer, Guardian Life Insurance, so they're looking at Managed Blockchain 'cause they have this distributed network of partners, providers, patients, and customers, and they want to provide decentralized verifiable records of what's taking place. And it's just a broad set of use cases. >> And then we saw in the video this morning, I think it was Indonesian farmers, right? Wasn't that before the keynote? Did you see that? It was good. >> I missed that one. >> Yeah, so they don't have bank accounts. >> Oh, got it. >> And they got a reward system, so they're using the blockchain to reward farmers to participate. >> So a lot of people ask the question is, why do I need blockchain? Why don't you just put in database? So there are unique, which is true by the way, 'cause latency's an issue. (chuckles) Certainly, you might want to avoid blockchain in the short term, until that gets fixed. Assume that every one will get fixed over time, but what are some of the use cases where blockchain actually is relevant? Can you be specific because that's really people starting to make their selection criteria on. Look, I still use a database. I'm going to have all kinds of token and models around, but in a database. Where is the blockchain specifically resonating right now? >> I'll take a shot at this or we can do it together, but when you think of QLDB, it's not that customers are asking us for a ledger database. What they were really saying is, hey, we'd like to have this complete immutable, cryptographically verifiable trail of data. And it wasn't necessarily a blockchain conversation, wasn't necessarily a database conversation, it was like, I really would like to have this complete cyrptographic verifiable trail of data. And it turns out, as you sort of look at the use cases, in particular, the centralized trust scenario, QLDB does exactly that. It's not about decentralized trust. It's really about simply being able to have a database that when you write to that database, you write a transaction to the database, you can't change it. You know, a typical database people are like, well, hey, wait a second, what does immutable really mean? And once you get people to understand that once that transaction is written to a journal, it cannot be changed at all and attached, then all of a sudden there's that breakthrough moment of it being immutable and having that cryptographic trail. >> And the advantage relative to a distributive blockchain is performance, scale, and all the challenges that people always say. >> Yeah, exactly. Like with QLDB, you can find it's going to be two to three times faster cause you're not doing that distributing consensus. >> How about data lakes? Let's talk about data lakes. What problem were you guys trying to solve with the data lakes? There's a lot of them, but. (chuckles) >> That's a great question. So, essentially it's been hard for customers to set up data lakes 'cause you have to figure out where to get data from, you have to land it in S3, you've got to secure it, you've then got to secure every analytic service that you've got, you might have to clean your data. So with lake formation, what we're trying to do is make it super easy to set up data lakes. So we have blueprints for common databases and data sources. We bring that data into an S3 data lake and we've created a central catalog for that data where customers can define granular access policies with the table, and the column, and the row level. We've also got ML-based data cleansing and data deduplication. And so now customers can just use lake formation, set up data lakes, curate their data, protect it in a single place, and have those policies that enforce across all of the analytic services that they might use. >> So does it help solve the data swamp problem, get more value out of the data lake? And if so, how? >> Absolutely, so the way it does that is by automatically cataloging all datas that comes in. So we can recognize what the data is and then we allow customers to add business metadata to that so they can tag this as customer data, or PII data, or this is my table of sales history. And that then becomes searchable. So we automatically generate a catalog as data comes in and that addresses the, what do I have in my data lake problem. >> Okay, so-- >> Go ahead. >> So, Rahul, you're the general manager. Shawn, what's your job, what do you do? >> So our team builds all the non-relational databases at Amazon. So DynamoDB, Neptune, ElastiCache, Timestream, which you'll hear about today, QLDB, et cetera. So all those things-- >> Beanstalk too, Elastic Beanstalk? >> No we do not build Beanstalk. >> Okay, we're a customer of DynamoDB, by the way. >> Great! >> We're happy customers. >> That's great! >> And we use ElastiCache, right? >> Yup, the elastic >> There you go! >> surge still has it. >> So-- >> Haven't used Neptune yet. >> What's the biggest problem stigmas that you guys are trying to raise the bar on? What's the key focus as you get this new worlds and use cases coming together? These are new use cases. How are you guys evaluating it? How are you guys raising the bar? >> You know, that's a really good question you ask. What I've found in my experience is developers that have been building apps for a long time, most people are familiar with relational databases. For years we've been building apps in that context, but when you kind of look at how people are building apps today, it's very different than how they did in the past. Today developer do what they do best. They take an application, a big application, break it down into smaller parts, and they pick the right tool for the right job. >> I think the game developer mark is going to be a canary in the coal mine for developers, and it's a good spot for data formation in these kind of unstructured, non-relational scenarios. Okay, now all this engagement data, could be first person shooter, whatever it is, just throw it, I need to throw it somewhere, and I'll get to it and let it be ready to be worked on by analytics. >> Well, yeah, if you think about that gamer scenario, think about if you and I are building a game, who knows if there's going to be one user, ten players, or 10 million, or 100 million. And if we had 100 million, it's all about the performance being steady. At 100 million or ten. >> You need a fleet of servers. (John laughing) >> And a fleet of servers! >> Have you guys played Fortnite? Or do you have kids that play? >> I look over my kid's shoulder. I might play it. >> I've played, but-- >> They run all their analytics on us. They've got about 14 petabytes in S3 using S3 as their data lake, with EMR and Athena for analytics. >> We got a season-- >> I mean, think about that F1 example on keynotes today. Great example of insights. We apply that kind of concept to Fortnite, by the way, Fortnite has theCUBE in there. It's always a popular term. We noticed that, the hastag, #wherestheCUBEtoday. (Rahul chuckling) I couldn't resist. But the analytics you could get out of all that data, every interaction, all that gesture data. I mean, what are some of the things they're doing? Can you share how they're using the new tech to scale up and get these insights? >> Yeah, absolutely. So they're doing a bunch of things. I mean, one is just the health of the systems when you've got hundreds of millions of players. You need to know if you're up and it's working. The second is around engagement. What games, what collection of people work well together. And then it's what incentives they create in the game, what power ups people buy that lead to continued engagement, 'cause that defines success over the long term. What gets people coming back? And then they have an offline analytics process where they're looking at reporting, and history, and telemetry, so it's very comprehensive. So you're exactly right about gaming and analytics being a huge consumer of databases. >> Now, Shawn, didn't you guys have hard news today on DynamoDB, or? >> Yeah today we announce DynamoDB On-Demand, so customers that basically have workloads that could spike up and then all of a sudden drop off, a lot of these customers basically don't even want to think about capacity planning. They don't want to guess. They just want to basically pay only for what they're using. So we announced DynamoDB On-Demand. The developer experience is simple. You create a table and you putyour read/write capacity in the on-demand mode, and you literally only pay for the request that your workload puts through system. >> It's a great service actually. Again, making life easier for customers. Lower the bill, manage capacity, make things go better, faster, enables value. >> It's all about improving the customer experience. >> Alright, guys, I really appreciate you coming in. I'm really interested in following what you guys do in the future. I'm sure a lot of people watching will be as well, as analytics and AI become a real part of, as you guys move the stack and create that API model for, what you did for infrastructure, for apps. A total game changer, we believe. We're interested in following you guys, I'm sure others are. Where are you going to be this year? What's your focus? Where can people find out more besides going to Amazon site? Is there certain events you're going to be at? How do people get more information and what's the plans? >> There's actually some sessions on lake formation, blockchain that we're doing here. We'll have a continuous stream of summits, so as the AWS Summit calendar for 2019 gets published that's a great place to go for more information. And then just engage with us either on social media or through the web and we'll be happy to follow up. >> Alright, well, we'll do a good job on amplifying. A lot of people are interested, certainly blockchain, super hot. But people want better, stronger, more stable, but they want the decentralized immutable database model. >> Cryptographically verifiable! >> And see as everyone knows. >> Scalable! >> Anyone who wants to keep those, they talk about CUBE coins but I haven't said CUBE coin once on this episode. Wait for those tokens to be released soon. More coverage after this short break, stay with us. I'm John Furrier, and Dave Vellante, we'll be right back. (futuristic buzzing) (futuristic electronic music)

>> Announcer: Live from Nashville, Tennessee, it's theCUBE, covering Commvault GO 2018. Brought to you by Commvault. >> Welcome back to Nashville, Tennessee, the home of hot chicken and Commvault GO this week. I'm Stu Miniman with my co-host, Keith Townsend. Keith wasn't expecting that one. >> I'm looking forward to the hot chicken. >> Absolutely. And happy to welcome to the program first-time guest, Rahul Pawar, who is the head of R&D, research and development, at Commvault. Thank you so much for joining us. >> Thanks for having me on this one. >> Alright, we said, like the hot chicken, I said we need to roll up our sleeves and really get into the sauce-- >> Rahul: Yes, yes. of what we're talking. Alright, enough of the puns on my standpoint. But tell us a little bit about R&D inside, what's your role, what's your team, what's your charter? >> So, we have a team of about 650 very dynamic, young engineers. And what my role, and I'm very excited about that role, is I get to talk with a lot of our customers and partners and understand their pain points. And the majority of my research comes from what the customer is really looking to do and what is hurting them, and trying to solve that and describe. And once I have a problem defined, the team is very, very intelligent at solving them and they come up with various ways to solve it. And then getting that customer satisfaction high is what gives me the high and that's really what's kept me at Commvault for over 17 years now. >> Yeah, 17 years, Rahul. I think back so, 17 years ago, I was working for a storage company. And we talked about data, but it was usually about storing data or protecting data. Now we're talking about how we can get more value out of data. One of the things I was looking at coming into this show is like, okay, you talk about the AI and the ML. Well, how does that fit into this environment? Maybe you can explain why is it different now in 2018? What can you do now that you wouldn't have been able to do 10 years or even five years ago? >> So Stu, you made a good point. Back up, especially, was make a copy, put it on tape, send it to somewhere. Iron mountain, typically. And that has changed now. Everything is available online all the time. And even our thermostat is much smarter than what it was five years back, so we really are expecting, everybody's expecting, a lot more from the retail that is available from all the information that is there and they want to make use of that. So backup can no longer be, hey, I'm backing up these five servers and go figure it out. Backup is now getting tons of VM's, tons of new application swapping in various cloud applications that are coming in. So the IT team is really, really in the middle of this data revolution and getting so much information thrown their way. So that data, and that data is the liquid gold, like Bob and I like to call it, and that has a lot of valuable information. It has information about your patterns, it has information about who is accessing what files, and should they really be accessing it, what data is really, really not needed, and what is the sensitive data that is lurking behind and it could become a problem for you? So that data is a goldmine and the systems and the hard disks are becoming so much cheaper. Storage has become so much cheaper, so having that data accessible all the time, we take it for granted. >> So Rahul, I'd like to say scale breaks things. When I was a young administrator, I literally had a spreadsheet to keep track of my tapes, of where my tapes were, what systems were backed up. So even if I lost my index and my software backup product, I could know where my tapes were at. Now, with organizations with petabytes and petabytes of data, how important is ML, AI to knowing where your data is at and how important is the index to that relationship? >> I really want to say that ML and AI has become what deduplication was five years back, and pretty much everybody is expecting you to have it. Like I said, if my car knows it, if my home knows it, my thermostat knows it, even my phone knows it, like where I'm going, like every week if I travel to a certain place and it knows it, it is something that is expected to be known. And our backup environment has become so dynamic. There's network failures and there's tons of things beyond the control of the backup admin, even the storage admin or the DB-ers or the app developers who are putting in there, that just come in place. And with all of that happening, you need a system that is learning from what is happening and being very smart about doing stuff. So, we learned from yesterday's failures or the failures that were on the backups, we look at the network load that is on right now, the disk load that is on right now, and adapt our backup schedules accordingly. So we know your SLAs. You're trying to get an SLA of a certain number of hours versus minutes, and based on that, we prioritize certain servers over others, or certain VM's that we see brand new over other VM's, and then VM's around certain data stores over others because we want to keep the load on the data storage server or even your network and the proxies minimum, but at the same time we know we are racing against the clock because we want everything to be backed up and even have a secondary copy and all of that. So there we are prioritizing and re-prioritizing our backups and schedules and everything. >> One of the challenges when you talk about automation is there's the technology and then there's the people and in the open to the keynote this morning, the poet was using the GPS analogy >> Yes. and talked about, okay, you have arrived. Well, the admins today, they kind of have their turf that they control versus do I trust that it's doing the job and can automate some of those things and I shouldn't have to worry about it. Does your team get involved in that dynamic? Because I know you listen to the customers how do you help bridge that gap and help? I think of autonomous cars, we said we will soon get to the point, sometime hopefully in the not-too-distant future, where it's not that I don't trust the computers, it's really that I trust them more than I do the people. >> Okay so I'll tell you, trust develops as you use it more. There's a reason why autonomous driving cars still have a steering wheel and a break because, I'm not sure whether I can trust it. But on the other hand, as time passes by, you really see the software in action and you want to see that its really doing the smart thing, and you yield control to it more and more. Like today, I'm like old era, so when I have something important I make an extra copy. Versus my kids, they are on Google files system or cloud files systems. They never even think about making an extra copy. The same thing is going to happen. We do have people who can take control and they can put on their priorities and all of that but we are saying, hey guys, you shouldn't be doing it we are here to help you and we are going to show you and in case you don't like it you can always put your brake on that self driving car or the self driving backup. >> So Rahul would we be remised if we had a researcher on theCUBE and we didn't talk about the art of the possible looking a few years ahead, or even a couple of years ahead. If you've ever been a backup administrator, nothing beats bandwidth. The bandwidth of a station wagon full of tapes. However in this modern digital transformative environment, we have to get data to the cloud as soon as possible. What are some of the unique ways Commvault is tackling getting Big Data from where it's ingested and to the cloud provider so that we can take advantage of stuff like AI, ML, base workloads, and Amazon or Google? >> One thing we have done with the cloud or anything is we have always kept data independent of where it is going. So even if I am taking data from on-print to a cloud provider we will play to their full strength, but we will still keep the data independent where, in case you want to move from one cloud window to another you have that flexibility with Commvault. As for us taking the cloud and its efficiency and using its efficiency what we have done is we always only send re-duplicated encrypted data to the cloud and we have various ways of consuming the cloud. So the cloud is where your storage has become so cheap that you don't have to think about it. In fact, I had a customer who got rid of their whole secondary DR data center, and now they are using the cloud as their DR location and every three months they do the DR test with Commvault, wherein they bring the infrastructure machines up, and its all scripted and orchestrated, they bring the infrastructure machines up, followed by all the VM's and the applications in a certain order. Like database has to come up before AD has to come before exchange anytime it has to come before web server. So all of that happens after their testing is done they have SLA's of four hours and 24 hours on certain servers. After all of that is done they power it off, they get rid of the infrastructure, and then they are back to paying only the storage bill on the cloud. That's just one usage but the cloud has made life so flexible that I don't have to think about my rack space and where does the server go and when do I order it and when does it ship, If I need something I experiment with it, I give it more memory and size and do stuff. Protecting that data and the cloud, and protecting it well, is what we do. We have taken use of all the technologies, like replicating across regions, taking it and replicating it across clouds we have done all of that. >> Keith: Well let's talk about the importance of metadata in all of that. So if I have bits and pieces of data distributed across cloud providers on-prem, how do I keep track of that data? >> That's where our furi index comes in play key because all that is happening is the data is spreading faster than some of the cloud growth because you have data with so many copies and people have made extra copies just to be safe that keeping track of everything, and knowing what is where, and who has access to what, and people change roles, some people leave, who has access after all of that is done? It's very vital and critical for an organization to function So our furi index is keeping track of not just the bare minimum of who has the files and what the files are what we have done is we have worked with several customers where we have allowed them to insert their own custom tags and custom information along with the data. So it's not just the file and file information or the file content awareness. They are able to keep third party extra data along with every piece that is automatically queried from their other databases and inserted in that file. So those are the custom properties that are tagged a lot. >> Stu: Yeah its interesting, you think about metadata I remember five or 10 years ago we were talking about the importance of metadata, but it seems like it's the convergence of the intelligence and the AI paired with that, because it used to be, oh, make sure you tag your files or set up your ontologies or things like that, and now, on our phones, it does a lot of that for us and therefore the enterprise is following a similar methodology. Did we hit a certain kind of tipping-point recently, or is it just some of these technologies coming together? >> I think a lot of that was in the making. We used to have this technology called index cards, where we were keeping track of things, who ever thinks of that, right? Now everything is by search, and that's the new normal. Searching for your thing, thinking that somebody will know what I'm trying to do and telling me ahead of time is where the future is. That's what we are trying to keep up with. >> You're saying my kids don't know the Dewey decimal system because they have Amazon and you know, and now we have a similar thing in business. >> It really to strikes you, for a calculator on a Windows desktop when the kids go and search on the web for a calculator instead of using the calculator app on the desktop, you really know that things have changed and shifted a lot. >> Keith: So thinking about that change and shift before I'm able to add these custom tags to net new data, I'm going to throw you a softball from a use case perspective, but it's a hard technical challenge is, I have 20 years of Commvault data that are data I've backed up with Commvault. Wouldn't it be great if I could teach an ML or AI algorithm to go back and tag that data based on how I tag new data, any requests for that or roadmaps to add that type of capability? >> Alright so if you are a 20 year old Commvault veteran customer, first of all, thank you. (laughing) >> Secondly, the fact that you're index is there and we have built on our existing index and added a lot more attributes to it, we already know a lot about you. If you are starting to beam to our cloud, we know a lot more about how your backups are, and how much you are backing up, and how your licensing is, and what are the typical workloads, and the top error rates, and how the health conditions are, and a lot of that. That is even on your own server dashboard. You don't have to beam it to any public cloud. You could see it on your own dashboard, all those statistics. So we already know all of that information. What we have come and started doing is we are inserting even more and more pieces of intelligence that we are finding because things have changed over the last 20 years. So what used to be just file metadata, user and all of that, now we have a lot more attributes that the file has. >> One of the biggest challenges we see is, I'm a networking person, and when I go to like the Cisco show this year, the network administrator, most of the network that they are responsible for isn't under their purview, and I think we have the same thing in data, a lot of the data that I'm concerned about in my business it's no longer in my four walls and it's spread out in so many different environments. Opportunity? Challenge? Both? >> For us it's very exciting and opportunistic. For our customers and a lot of IT admins if you are dealing with multiple tools to handle that kind of thing its a big challenge. I have met several customers and they wouldn't admit it, but they know that even though their company policy is not to use certain clouds, the people are using it. If their company policy is not to use some doc sharing, people are using it. So, there are two ways you can look at it. You could forget it and then risk. Or you could accept it and analyze everything with Commvault and go ahead. >> So let's talk about Commvault and this ability to know where your data is at with adjacent technology you know data protection is about protecting the data not just from 'oops I lost my data' or even ran somewhere specifically, but security. What is the role of the index or metadata In protecting your data from intruders? >> So as far as 'ran somewhere' is concerned, we have taken a few things. One is, and we are not a 'ran somewhere' production per se, but what we have done is because we are in there and we look at your backup, how often they happen, how much data is changing, adjusted that to seasonality we know per quarter if you have a lot of files changing versus weekends and how things change, adjusted to seasonality if we something that is out of the norm, we are going to alert you. At that point that alert is an actionable alert where you could say, hey, I want to disable data edging on this particular client, or I want to take away access of someone on that. So even data risks like a rogue admin or an accidental admin what we did is we have added almost a two-signature kind of stuff. So if somebody accidentally deletes a client or a storage policy, one admin won't be able to do that. The business workflow says: 'do you also have authentication from Stu?' That 'hey, Keith is trying to delete this'. That's to approve of this and it's and email to which you reply 'yes' or 'no'. The moment it is done, it goes ahead and it deletes it versus it may stop and 'oops' that was an accident Keith didn't really want to do that. So there's that aspect, the second thing is our own media, what we have done is it is completely protected with our drivers, wherein you can't get to it. Only Commvault authenticated processes are able to write to write to our media. When the customer came in this morning and was talking about it, all their infrastructure was affected, but Commvault really hasn't because we had it secured and the ransomer couldn't attack that because they simply were unable to write to it. >> Stu: Alright well Rahul Pawar we really appreciate you giving us an update. Look forward to catching up in the future where we'll see exactly where the research is going. Alright, for Keith Townsend I'm Stu Miniman, we'll be back with lots more coverage here from Commvault GO, in Nashville, Tenessee. Thanks for watching theCUBE. >> Rahul: Thank you Keith, thank you Stu. >> Keith: Thank you.

>> Live, from Washington, D.C., it's theCUBE! Covering Inforum DC 2018. Brought to you by Infor. >> Well, welcome back. We are here on theCUBE. Thanks for joining us here as we continue our coverage here at Inforum 18. We're in Washington D.C., at the Walter Washington Convention Center. I'm John Walls, with Dave Vellante and we're joined now by Rahul Pathak, who is the G.M. at Amazon Athena and Amazon EMR. >> Hey there. Rahul, nice to see you, sir. >> Nice to see you as well. Thanks for having me. >> Thank you for being with us, um, now you spoke earlier, at the executive forum, and, um, wanted to talk to you about the title of the presentation. It was Datalinks and Analytics: the Coming Wave of Brilliance. Alright, so tell me about the title, but more about the talk, too. >> Sure. Uh, so the talk was really about a set of components and a set of transdriving data lake adoption and then how we partner with Infor to allow Infor to provide a data lake that's customized for their vertical lines of business to their customers. And I think part of the notion is that we're coming from a world where customers had to decide what data they could keep, because their systems were expensive. Now, moving to a world of data lakes where storage and analytics is a much lower cost and so customers don't have to make decisions about what data to throw away. They can keep it all and then decide what's valuable later. So we believe we're in this transition, an inflection point where you'll see a lot more insights possible, with a lot of novel types of analytics, much more so than we could do, uh, to this point. >> That's the brilliance. That's the brilliance of it. >> Right. >> Right? Opportunity to leverage... >> To do more. >> Like, that you never could before. >> Exactly. >> I'm sorry, Dave. >> No, no. That's okay. So, if you think about the phases of so called 'big data,' you know, the.... We went from, sort of, EDW to cheaper... >> (laughs) Sure. >> Data warehouses that were distributed, right? And this guy always joked that the ROI of a dupe was reduction of investment, and that's what it became. And as a result, a lot of the so-called data lakes just became stagnant, and so then you had a whole slew of companies that emerged trying to get, sort of, clean up the swamp, so to speak. Um, you guys provide services and tools, so you're like "Okay guys, here it is. We're going to make it easier for you." One of the challenges that Hadoop and big data generally had was the complexity, and so, what we noticed was the cloud guys--not just AWS, but in particular AWS really started to bring in tooling that simplified the effort around big data. >> Right. >> So fast-forward to today, and now we're at the point of trying to get insights-- data's plentiful,insights aren't. Um, bring us up to speed on Amazon's big data strategy, the status, what customers are doing. Where are we at in those waves? >> Uh, it's a big question, but yeah, absolutely. So... >> It's a John Furrier question. (laughter) So what we're seeing is this transition from sort of classic EDW to S3 based data lakes. S3's our Amazon storage service, and it's really been foundational for customers. And what customers are doing is they're bringing their data to S3 and open data formats. EDWs still have a role to play. And then we offer services that make it easy to catalog and transform the data in S3, as well as the data in customer databases and data warehouses, and then make that available for systems to drive insight. And, when I talk about that, what I mean is, we have the classic reporting and visualization use cases, but increasingly we're seeing a lot more real time event processing, and so we have services like Kinesis Analytics that makes it easy to run real time queries on data as it's moving. And then we're seeing the integration of machine learning into the stacks. Once you've got data in S3, it's available to all of these different analytic services simultaneously, and so now you're able to run your reporting, your real time processing, but also now use machine learning to make predictive analytics and decisions. And then I would say a fourth piece of this is there's really been, with machine learning and deep learning and embedding them in developer services, there's now been a way to get at data that was historically opaque. So, if you had an audio recording of a social support call, you can now put it through a service that will actually transcribe it, tell you the sentiment in the call and that becomes data that you can then track and measure and report against. So, there's been this real explosion in capability and flexibility. And what we've tried to do at AWS is provide managed services to customers, so that they can assemble sophisticated applications out of building blocks that make each of these components easier, and, that focus on being best of breed in their particular use case. >> And you're responsible for EMR, correct? >> Uh, so I own a few of these, EMR, Athena and Glue. And, uh, really these are... EMR's Open Source, Spark and Hadoop, um, with customized clusters that upbraid directly against S3 data lakes, so no need to load in HDFS, so you avoid that staleness point that you mentioned. And then, Athena is a serverless sequel NS3, so you can let any analyst log in, just get a sequel prompt and run a query. And then Glue is for cataloging the data in your data lake and databases, and for running transformations to get data from raw form into an efficient form for querying, typically. >> So, EMR is really the first service, if I recall, right? The sort of first big data service-- >> That's right. >> -that you offered, right? And, as you say, you really begin to simplify for customers, because the dupe complexity was just unwieldy, and the momentum is still there with EMR? Are people looking for alternatives? Sounds like it's still a linchpin of the strategy? >> No, absolutely. I mean, I think what we've seen is, um, customers bring data to S3, they will then use a service, like Redshift, for petabyte scale data warehousing, they'll use EMR for really arbitrary analytics, using opensource technologies, and then they'll use Athena for broad data lake query and access. So these things are all very much complimentary, uh, to each other. >> How do you define, just the concept of data lakes, uh, versus other approaches to clients? And trying to explain to them, you know, the value and the use for them, uh, I guess ultimately how they can best leverage it for their purposes? How do you walk them through that? >> Yeah, absolutely. So, there's, um. You know, that starts from the principles around how data is changing. So before we used to have, typically, tabular data coming out of ERP systems, or CRM systems, going into data warehouses. Now we're seeing a lot more variety of data. So, you might have tweets, you might have JSON events, you might have log events, real time data. And these don't fit traditional... well into the traditional relational tabular model, ah, so what data lakes allow you to do is, you can actually keep both types of the data. You can keep your tabular data indirectly in your data lake and you can bring in these new types of data, the semi-structured or the unstructured data sets. And they can all live in the data lake. And the key is to catalog that all so you know what you have and then figure out how to get that catalog visible to the analytic layer. And so the value becomes you can actually now keep all your data. You don't have to make decisions about it a priori about what's going to be valuable or what format it's going to be useful in. And you don't have to throw away data, because it's expensive to store it in traditional systems. And this gives you the ability then to replay the past when you develop better ideas in the future about how to leverage that data. Ah, so there's a benefit to being able to store everything. And then I would say the third big benefit is around um, by placing data and data lakes in open data formats, whether that's CSV or JSON or a more efficient formats, that allows customers to take advantage of best of breed analytics technology at any point in time without having to replatform their data. So you get this technical agility that's really powerful for customers, because capabilities evolve over time, constantly, and so, being in a position to take advantage of them easily is a real competitive advantage for customers. >> I want to get to Infor, but this is so much fun, I have some other questions, because Amazon's such a force in this space. Um, when you think about things like Redshift, S3, Pedisys, DynamoDB...we're a customer, these are all tools we're using. Aurora. Um, the data pipeline starts to get very complex, and the great thing about AWS is I get, you know, API access to each of those and Primitive access. The drawback is, it starts to get complicated, my data pipeline gets elongated and I'm not sure whether I should run it on this service or that service until I get my bill at the end of the month. So, are there things you're doing to help... First of all, is that a valid concern of customers and what are you doing to help customers in that regard? >> Yeah, so, we do provide a lot of capability and I think our core idea is to provide the best tool for the job, with APIs to access them and combine them and compose them. So, what we're trying to do to help simplify this is A) build in more proscriptive guidance into our services about look, if you're trying to do x, here's the right way to do x, at least the right way to start with x and then we can evolve and adapt. Uh, we're also working hard with things like blogs and solution templates and cloud formation templates to automatically stand up environments, and then, the third piece is we're trying to bring in automation and machine learning to simplify the creation of these data pipelines. So, Glue for example. When you put data in S3, it will actually crawl it on your behalf and infer its structure and store that structure in a catalog and then once you've got a source table, and a destination table, you can point those out and Glue will then automatically generate a pipeline for you to go from A to B, that you can then edit or store in version control. So we're trying to make these capabilities easier to access and provide more guidance, so that you can actually get up and running more quickly, without giving up the power that comes from having the granular access. >> That's a great answer. Because the granularity's critical, because it allows you, as the market changes, it allows you... >> To adapt. To move fast, right? And so you don't want to give that up, but at the same time, you're bringing in complexity and you just, I think, answered it well, in terms of how you're trying to simplify that. The strategy's obviously worked very well. Okay, let's talk about Infor now. Here's a big ISP partner. They've got the engineering resources to deal with all this stuff, and they really seem to have taken advantage of it. We were talking earlier, that, I don't know if you heard Charles's keynote this morning, but he said, when we were an on prem software company, we didn't manage customer servers for them. Back then, the server was the server, uh software companies didn't care about the server infrastructure. Today it's different. It's like the cloud is giving Infor strategic advantage. The flywheel effect that you guys talk about spins off innovation that they can exploit in new ways. So talk about your relationship with Infor, and kind of the history of where it's come and where it's going. >> Sure. So, Infor's a great partner. We've been a partner for over four years, they're one of our first all-in partners, and we have a great working relationship with them. They're sophisticated. They understand our services well. And we collaborate on identifying ways that we can make our services better for their use cases. And what they've been able to do is take all of the years of industry and domain expertise that they've gained over time in their vertical segments, and with their customers, and bring that to bear by using the components that we provide in the cloud. So all these services that I mentioned, the global footprint, the security capabilities, the, um, all of the various compliance certifications that we offer act as accelerators for what Infor's trying to do, and then they're able to leverage their intellectual property and their relationships and experience they've built up over time to get this global footprint that they can deploy for their customers, that gets better over time as we add new capabilities, they can build that into the Infor platform, and then that rolls out to all of their customers much more quickly than it could before. >> And they seem to be really driving hard, I have not heard an enterprise software company talk so much about data, and how they're exploiting data, the way that I've heard Infor talk about it. So, data's obviously key, it's the lifeblood-- people say it's the new oil--I'm not sure that's the best analogy. I can only put oil in my house or my car, I can't put it in both. Data--I can do so many things with it, so, um... >> I suspect that analogy will evolve. >> I think it should. >> I'm already thinking about it now. >> You heard it here first in the Cube. >> You keep going, I'll come up with something >> Don't use that anymore. >> Scratch the oil. >> Okay, so, your perspectives on Infor, it's sort of use of data and what Amazon's role is in terms of facilitating that. >> So what we're providing is a platform, a set of services with powerful building blocks, that Infor can then combine into their applications that match the needs of their customers. And so what we're looking to do is give them a broad set of capabilities, that they can build into their offerings. So, CloudSuite is built entirely on us, and then Infor OS is a shared set of services and part of that is their data lake, which uses a number of our analytic services underneath. And so, what Infor's able to do for their customers is break down data silos within their customer organizations and provide a common way to think about data and machine learning and IoT applications across data in the data lake. And we view our role as really a supporting partner for them in providing a set of capabilities that they can then use to scale and grow and deploy their applications. >> I want to ask you about--I mean, security-- I've always been comfortable with cloud security, maybe I'm naive--but compliance is something that's interesting and something you said before... I think you said cataloging Glue allows you to essentially keep all the data, right? And my concern about that is, from a governance perspective, the legal counsel might say, "Well, I don't "want to keep all my data, if it's work in process, "I want to get rid of it "or if there's a smoking gun in there, "I want to get rid of it as soon as I can." Keep data as long as possible but no longer, to sort of paraphrase Einstein. So, what do you say to that? Do you have customers in the legal office that say, "Hey, we don't want to keep data forever, "and how can you help?" >> Yeah, so, just to refine the point on Glue. What Glue does is it gives you essentially a catalog, which is a map of all your data. Whether you choose to keep that data or not keep that data, that's a function of the application. So, absolutely >> Sure. Right. We have customers that say, "Look, here are my data sets for "whether it's new regulations, or I just don't want this "set of data to exist anymore, or this customer's no longer with us and we need to delete that," we provide all of those capabilities. So, our goal is to really give customers the set of features, functionality, and compliance certifications they need to express the enterprise security policies that they have, and ensure that they're complying with them. And, so, then if you have data sets that need to be deleted, we provide capabilities to do that. And then the other side of that is you want the audit capabilities, so we actually log every API access in the environment in a service called CloudTrail and then you can actually verify by going back and looking at CloudTrail that only the things that you wanted to have happen, actually did happen. >> So, you seem very relaxed. I have to ask you what life is like at Amazon, because when I was down at AWS's D.C. offices, and you walk in there, and there's this huge-- I don't know if you've seen it-- there's this giant graph of the services launched and announced, from 2006, when EC2 first came out, til today. And it's just this ridiculous set of services. I mean the line, the graph is amazing. So you're moving at this super, hyper pace. What's life like at AWS? >> You know, I've been there almost seven years. I love it. It's been fantastic. I was an entrepreneur and came out of startups before AWS, and when I joined, I found an environment where you can continue to be entrepreneurial and active on behalf of you customers, but you have the ability to have impact at a global scale. So it's been super fun. The pace is fast, but exhilarating. We're working on things we're excited about, and we're working on things that we believe matter, and make a difference to our customers. So, it's been really fun. >> Well, so you got--I mean, you're right at the heart of what I like to call the innovation sandwich. You've got data, tons of data, obviously, in the cloud. You're a leader and increasingly becoming sophisticated in machine intelligence. So you've got data, machine intelligence, or AI, applied to that data, and you've got cloud for scale, cloud for economics, cloud for innovation, you're able to attract startups--that's probably how you found AWS to begin with, right? >> That's right. >> All the startups, including ours, we want to be on AWS. That's where the developers want to be. And so, again, it's an overused word, but that flywheel of innovation occurs. And that to us is the innovation sandwich, it's not Moore's Law anymore, right? For decades this industry marched to the cadence of Moore's Law. Now it's a much more multi-dimensional matrix and it's exciting and sometimes scary. >> Yeah. No, I think you touched on a lot of great points. It's really fun. I mean, I think, for us, the core is, we want to put things together the customers want. We want to make them broadly available. We want to partner with our customers to understand what's working and what's not. We want to pass on efficiencies when we can and then that helps us speed up the cycle of learning. >> Well, Rahul, I actually was going to say, I think he's so relaxed because he's on theCUBE. >> Ah, could be. >> Right, that's it. We just like to do that with people. >> No, you're fantastic. >> Thanks for being with us. >> It's a pleasure. >> We appreciate the insights, and we certainly wish you well with the rest of the show here. >> Excellent. Thank you very much, it was great to be here. >> Thank you, sir. >> You're welcome. >> You're watching theCUBE. We are live here in Washington, D.C. at Inforum 18. (techno music)

>> Announcer: Live from Boston, Massachusetts. It's theCUBE, covering Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to Boston. Here on theCUBE we continue our coverage of Red Hat Summit 2019. We just had Jim Whitehurst on, President and CEO, along with Stu Miniman, I'm John Walls. And now, we turn to the IBM side of the equation. Arvind Krishna is with us, the SVP of Cloud and Cognitive Software at IBM. Arvind, good to see you this morning. >> My pleasure to be here, what a great show. >> Yeah, absolutely, it has been. I was telling Jim he couldn't have a better week, right? Monday had good news, Tuesday great kick off, today again following through great key notes. We were talking briefly, a year ago you were with us on theCUBE and talking about IBM and its forward plans, so on and so forth. What a difference a year makes, right? (laughs) >> We couldn't predict that you'd be in the position that you are in now, so just if you can summarize the last year and maybe the last six months for you. >> Sure, and I think it's more building on what I talked to you about a year ago, I remember last May, May of 2018, in San Francisco. So I was exposing very heavily, look the world's going to move towards containers, the world has already embraced Linux, this is the time to have a new architecture that enables hybrid, much along the lines that Jim and all of the clients as well as Ginni and Satya were talking about on stage yesterday. So you put all that together and you say that is what we mentioned last year and we were clear, that is where the world is gonna go. Now you step forward a few months from there into October of 2018 and on the 29th October we announced that IBM intends to acquire Red Hat, so then you say wow, we put actually our money where our mouth was. We were talking about the strategy, we were talking about Linux containers, OpenShift, the partnership we announced last May was IBM software products together with OpenShift. We already believed in that. But now this allows us coming together, it's more like a marriage than sort of loose partners passing each other in the middle of the night. >> Right. >> And that then goes forward, you mention the news on Monday so for our viewers that don't know it, that's the news that the United States Department of Justice approved merger with no conditions. So now we've got to wait on a few other jurisdictions and then hopefully we can get together really soon. >> John: Right, right. >> So, I think back to looking at IBM over my career. I think the first time I heard the word coopetition it was related to IBM because IBM, big ego system, lots of innovation over its long history but as we know the bigger you get, the more chance that your partners are also going to overlap with you. Seeing Ginni up on stage and a little bit later seeing Satya up on stage is really interesting. You look at the public, multicloud environment, everybody doesn't need to work together, you talk to your customers, and I'm sure you find today it's not the future is hybrid and multicloud, that's where they are today even if they're trying to get their arms around all of it. So I'd love to hear your, with the mega trend of Cloud, what you're seeing that competitive but partnering dynamic. >> Look, I want to step back to just give it a little bit of context. So when you talk about companies, let's go back to the beginning of computing, of PC. The PC came from IBM operating system, DOS came from Microsoft. Then you had Windows setting up the IBM PC. So that's coopetition or is that pure partnership? Right, I mean you can take your pick of those words. Our value has always been that we, IBM, come to clients and we try to service problems that actually help them in their business outcomes. Then whoever they have inside their IT shops, that they depend upon, has to be a part of that answer. You cannot say oh, so and so is bad, they're out. So it always had to be coopetition from the lengths that we came to with our clients. We always build originally computers, other people's software are on those computers, other people provided services around it. As we went into certain software space, ISVs and so on came together. So now that you come to the world of Cloud, we hold a very fundamental belief and I think we heard a number of the clients talk about this. They are going to be on multiple public Clouds. If they are going to be on multiple public Clouds, they are also going to have traditional IT and they are also going to have private Clouds. That's the world to live in if I look at it from the viewpoint of that infrastructure. To now come to your direct question, so if that's the world they're going to live in hopefully one of those public Clouds is ours but the others are from other people. The private Cloud, we believe the standard for that should be OpenShift and should be containers. So as we go down that path, then you say if you want to take that environment and also run it on the other publics. That's good for the client, that's good for the publics, that's good for us. It's really a win, win, win. And so I think the ability to go do this and to make that play out, it really goes back to my thesis from more than a year ago where we talk about this is a new set of standards and a new set of technical protocols emerging. >> I want you to take us inside the conversations you're having with CIOs when you talk about Cloud because when Cloud first came out, it was well, the sins of IT is this heterogeneous mess and it's complex and expensive. Cloud's going to be simple, homogeneous and cheap. I look at Cloud of 2019 and I don't think I would use any of those adjectives to define what most people have for Cloud. Where are they today? Where do we need to go as an industry? >> Glass house computing, all centralized, all homogeneous, not all at heterogeneous. Oops, 15 flavors of Unix, all different, none of them really talk to each other. Oops let's go to desktop computing, we begin with a pure architecture, maybe Novell which doesn't exist, maybe it does, I don't even know. Oops, back to this complete sprawl of client server. Okay let's go to Cloud back to centralized glass house. >> You're making me dizzy. >> Oops, let's go to-- (laughing) >> Let's go to lots of public, lots of SaaS, lots of private, back to this thing. So, in each of these a different answer came on how to unite them. I think when we look at that Unix and client server sprawl, I think TCP/IP and the internet came together so that you could have all these islands talk to each other and be able to communicate. All right, great, we've got 20 years of victory on that. Now you're getting these things, how do you begin to workload across because that becomes the next level of values. Not enough to communicate. Can I really take a workload? A workload is not just a VM or just one container, it's a collection of these things integrated together in a pretty tight and complex way. And can we take it from one place and move it to the other? Because that goes to the write once, run anywhere mantra which by the way also we come to about every 20 years. I think that's the magic of this moment and if we succeed in making that happen, which I have complete conviction we will, especially together, then I think we give a huge value back and we give freedom to every CTO and every CIO. >> You paint this really interesting whoops picture, I love that, it's really a back and forth, right, we're swinging and almost there's a cyclical nature to this is what you're I think implying. What's to say in your mind that this isn't just another whoops as opposed to this being a permanent shift in the paradigm? >> I think it's, the reason I think that it's going to be cyclical is we tend to, you know whether you go to construction and real estate, you talk about capacity and factories. You see an opportunity and people tend to go one way. The only way to correct culture if you're sitting in one place is to sort of over-correct the other way, now you're over-corrected. Now you have to come back. And always when you over-correct one way, then suddenly all those other benefits you've lost, so then you've got to come back to get those benefits. After about 10 years, probably, you can debate 10 or 15, you're done. You've exploited all those benefits, now you need to go get those benefits. Because the technologies have changed, it's not just that you're going back to what was. We're going very conceptually from centralized to distributed, to centralized to distributed. And by the way, another one that's getting out from pure centralized is also Edge. Edge in effect is another distributed, so you put those together and you say I went there, but then I lost all this stuff, now I need to get back to that stuff. If you've got too much there, you'll say, no, no, no, I need to get some of this back. So it's going to go that way I think for every, if you look at it, the big arcs are back, the pendulum, what do you call it, the pendulum swing, is I think about 20 years it looks like, right? 1960, centralized, 1980, PC, 2000, you could say was the peak of the internet. Hey, 2020, we're in Cloud. So looks like about 20 years, looks like. >> All right, so, I like what you were saying when you talk about that multicloud environment, the application is really central there. IBM, of course, has a strong history, not just in middleware but in applications. What do you think will differentiate this kind of next wave of multicloud, how will the leaders emerge? >> Right, so if you look at it today, you run infrastructure. I think OpenShift has done a great job of how you help run their infrastructure. The value in our eyes in putting the services on top, both coming from open source as well as other companies that are running like an integrated package. This is all about taking the cost out of how do you deploy and develop. And if we can take the cost out of that, you're not talking about that five to 10 X as we heard a couple of the clients up on stage yesterday with Jim talk about. If we give that to everybody, you can sort of say that 70% which goes into managing your current and only 30% on innovation. Can you shift that paradigm completely? That's the big business outcome that you get. As you begin to deliver these towers of function on top of the base. You need to start at base, without one base, you don't know how to say, I can't deal with these towers of function on thirty different things underneath. That engineering answer is a terrible one. >> In terms of the infrastructure market, things keep changing, right? Consolidating, EMC doing what they're, you know what happened there. How do you see your play in that market? First off, how do you see infrastructure evolving? And then how do you see your play in that going forward? >> Infrastructure has always been big, in the end all the stuff you talk about has to run on infrastructure. I'd say the consumption model of how you get infrastructure is changing. So it used to be that many years ago, people bought all their own infrastructures. They bought boxes, they put in boxes, they did all the integration. And what came from the vendor was just a box. Then you went to, all right you can get it as a managed service or you can get it in Cloud which is also a pay by the drink but you can now turn it up and down also. So it's not a either or, people want all of these models. And so our role in infrastructure, certain things we will provide. When it comes to running really high mission critical workloads, think mainframe, think big Unix, think storage, of that ilk; we'll keep providing that. We believe there's a lot of value in that. We see the value, our clients appreciate that value. That workload turns up, but it's the mission critical part of the workload. Then in turn we also provide the more commodity infrastructure but as a service. We supply a large amount of it to our clients. It comes sometimes wrapped in a managed service, it sometimes comes wrapped as a Cloud. And we will also consume infrastructure from other Cloud providers because if people are providing base computer, network and storage, there is no reason to presume that our capabilities wouldn't run on top. If I go back to just February, we announced that Watson will now run. We said we used the moniker Watson Anywhere to make the assertion that we will run Watson anywhere that we can run the correct containerized infrastructure. >> So, Arvind, what's the single most pressing issue that you hear from organizations with respect to their technology strategy and how's IBM helping there? >> I think modernizing applications is the biggest one. So people have, typically a large enterprise will have anywhere from 3,000 to 15,000 applications. That's what runs the enterprise. We talk about everyone's becoming a software company, right, I mean that was one of the quotes and everybody is becoming a tech company that was I think what one of the clients said, hey, we think you're a bank, you're actually a tech company. What that says is that you're capturing the essence of all the business processes. You're capturing the essence of the experiences. The essence of what regulators need, the essence of how you maintain customer and customer of our clients, trust, back to them. It's maintained through this collection of applications. Now if you say I want to go change, I want to become even more client centric, I want to insert AI into the middle of my business process, I want to become more digital. All of that is modernizing applications. The big pinpoint they all have is how do I modernize them? What becomes that fabric in which I modernize? How do I know I'm not locked into yet another spaghetti mess if I go down this path? Because we've seen that movie also. So they're interested in, hey, I want to be clean at the end of this. I want freedom to be able to move it. And that is why I'm so passionate about, the fabric is based on open source, the fabric's got to be based on open standards. If you go there, there is no lock-in, and it's not a spaghetti mess, it is actually clean. Much cleaner than any other option that we can dream of is going to be. And so if we go down this path, now you can open yourself up to a much faster velocity of how you deliver innovation and value back to the business. >> Okay, so, I'd agree first of all when you talk about modernization, the applications that they have, that's the long pole in the tent. We understand compared to all the other digitization, modernization, this is the toughest challenge here. I'm a little surprised though that I didn't hear the word data because they don't necessarily articulate it but the biggest opportunity that they have has to be tied to data. >> Well to me, when I use the word application here, and you heard me use the word AI, can I insert AI in the context of an application? Now, why is it not being done today? To get the value out of AI, the data that powers the AI is stuck in all the silos, all over the place. So you've got to have, as you do this modernization, it's imperative to put the correct data architecture so that now you can do the governance, so that you can choose to unlock the appropriate parts of the data. It's really important to say the appropriate parts because neither do you want data sort of free floating around the globe, because that is the value of a company at the end of the day. And so that unlocking of that value is a huge part of this. So you're absolutely right to ask me to express it more strongly when I use the word application, I'm inclusive of not just runtime but always of the data that powers that application. >> Arvind, it was again a year ago that we were talking to you out in San Francisco and you made some rather strong thematic predictions that turned out well. I'm not going to put you on the spot here, but I can't wait to see next year. And see how this turns out. >> I can't let him go before, we had the CIO of Delta who we had on our program. >> Oh, right, right. >> In the key note, made a question about licensing, of course Jim Whitehurst said we don't have licensing but what's your answer? >> I'm willing to offer a deal to Samant. So I think that both IBM and Red Hat do a fair amount of air travel. We'll give him a common license if he can just include Red Hat for whatever IBM pays, just include all the Red Hat travel that is needed on Delta. (laughing) You know just so that the business models become clear and we can go have a robust discussion. >> Out of Raleigh that's a good deal. >> For us. >> That's what I'm saying. That is a good deal. All right, the ball is in your court, or on your runway. Whatever the case may be. Arvind, thanks for being with us. >> My pleasure. >> We appreciate it. And we'll let you know if we hear back from Rahul on that good deal. TheCUBE continues live from Boston right after this. (upbeat music)