(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase AI/ML Top Startups Building Foundation Model Infrastructure. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem to talk about data and analytics. I'm your host, Lisa Martin and today we're excited to be joined by two guests from Astronomer. Steven Hillion joins us, it's Chief Data Officer and Jeff Fletcher, it's director of ML. They're here to talk about machine learning and data orchestration. Guys, thank you so much for joining us today. >> Thank you. >> It's great to be here. >> Before we get into machine learning let's give the audience an overview of Astronomer. Talk about what that is, Steven. Talk about what you mean by data orchestration. >> Yeah, let's start with Astronomer. We're the Airflow company basically. The commercial developer behind the open-source project, Apache Airflow. I don't know if you've heard of Airflow. It's sort of de-facto standard these days for orchestrating data pipelines, data engineering pipelines, and as we'll talk about later, machine learning pipelines. It's really is the de-facto standard. I think we're up to about 12 million downloads a month. That's actually as a open-source project. I think at this point it's more popular by some measures than Slack. Airflow was created by Airbnb some years ago to manage all of their data pipelines and manage all of their workflows and now it powers the data ecosystem for organizations as diverse as Electronic Arts, Conde Nast is one of our big customers, a big user of Airflow. And also not to mention the biggest banks on Wall Street use Airflow and Astronomer to power the flow of data throughout their organizations. >> Talk about that a little bit more, Steven, in terms of the business impact. You mentioned some great customer names there. What is the business impact or outcomes that a data orchestration strategy enables businesses to achieve? >> Yeah, I mean, at the heart of it is quite simply, scheduling and managing data pipelines. And so if you have some enormous retailer who's managing the flow of information throughout their organization they may literally have thousands or even tens of thousands of data pipelines that need to execute every day to do things as simple as delivering metrics for the executives to consume at the end of the day, to producing on a weekly basis new machine learning models that can be used to drive product recommendations. One of our customers, for example, is a British food delivery service. And you get those recommendations in your application that says, "Well, maybe you want to have samosas with your curry." That sort of thing is powered by machine learning models that they train on a regular basis to reflect changing conditions in the market. And those are produced through Airflow and through the Astronomer platform, which is essentially a managed platform for running airflow. So at its simplest it really is just scheduling and managing those workflows. But that's easier said than done of course. I mean if you have 10 thousands of those things then you need to make sure that they all run that they all have sufficient compute resources. If things fail, how do you track those down across those 10,000 workflows? How easy is it for an average data scientist or data engineer to contribute their code, their Python notebooks or their SQL code into a production environment? And then you've got reproducibility, governance, auditing, like managing data flows across an organization which we think of as orchestrating them is much more than just scheduling. It becomes really complicated pretty quickly. >> I imagine there's a fair amount of complexity there. Jeff, let's bring you into the conversation. Talk a little bit about Astronomer through your lens, data orchestration and how it applies to MLOps. >> So I come from a machine learning background and for me the interesting part is that machine learning requires the expansion into orchestration. A lot of the same things that you're using to go and develop and build pipelines in a standard data orchestration space applies equally well in a machine learning orchestration space. What you're doing is you're moving data between different locations, between different tools, and then tasking different types of tools to act on that data. So extending it made logical sense from a implementation perspective. And a lot of my focus at Astronomer is really to explain how Airflow can be used well in a machine learning context. It is being used well, it is being used a lot by the customers that we have and also by users of the open source version. But it's really being able to explain to people why it's a natural extension for it and how well it fits into that. And a lot of it is also extending some of the infrastructure capabilities that Astronomer provides to those customers for them to be able to run some of the more platform specific requirements that come with doing machine learning pipelines. >> Let's get into some of the things that make Astronomer unique. Jeff, sticking with you, when you're in customer conversations, what are some of the key differentiators that you articulate to customers? >> So a lot of it is that we are not specific to one cloud provider. So we have the ability to operate across all of the big cloud providers. I know, I'm certain we have the best developers that understand how best practices implementations for data orchestration works. So we spend a lot of time talking to not just the business outcomes and the business users of the product, but also also for the technical people, how to help them better implement things that they may have come across on a Stack Overflow article or not necessarily just grown with how the product has migrated. So it's the ability to run it wherever you need to run it and also our ability to help you, the customer, better implement and understand those workflows that I think are two of the primary differentiators that we have. >> Lisa: Got it. >> I'll add another one if you don't mind. >> You can go ahead, Steven. >> Is lineage and dependencies between workflows. One thing we've done is to augment core Airflow with Lineage services. So using the Open Lineage framework, another open source framework for tracking datasets as they move from one workflow to another one, team to another, one data source to another is a really key component of what we do and we bundle that within the service so that as a developer or as a production engineer, you really don't have to worry about lineage, it just happens. Jeff, may show us some of this later that you can actually see as data flows from source through to a data warehouse out through a Python notebook to produce a predictive model or a dashboard. Can you see how those data products relate to each other? And when something goes wrong, figure out what upstream maybe caused the problem, or if you're about to change something, figure out what the impact is going to be on the rest of the organization. So Lineage is a big deal for us. >> Got it. >> And just to add on to that, the other thing to think about is that traditional Airflow is actually a complicated implementation. It required quite a lot of time spent understanding or was almost a bespoke language that you needed to be able to develop in two write these DAGs, which is like fundamental pipelines. So part of what we are focusing on is tooling that makes it more accessible to say a data analyst or a data scientist who doesn't have or really needs to gain the necessary background in how the semantics of Airflow DAGs works to still be able to get the benefit of what Airflow can do. So there is new features and capabilities built into the astronomer cloud platform that effectively obfuscates and removes the need to understand some of the deep work that goes on. But you can still do it, you still have that capability, but we are expanding it to be able to have orchestrated and repeatable processes accessible to more teams within the business. >> In terms of accessibility to more teams in the business. You talked about data scientists, data analysts, developers. Steven, I want to talk to you, as the chief data officer, are you having more and more conversations with that role and how is it emerging and evolving within your customer base? >> Hmm. That's a good question, and it is evolving because I think if you look historically at the way that Airflow has been used it's often from the ground up. You have individual data engineers or maybe single data engineering teams who adopt Airflow 'cause it's very popular. Lots of people know how to use it and they bring it into an organization and say, "Hey, let's use this to run our data pipelines." But then increasingly as you turn from pure workflow management and job scheduling to the larger topic of orchestration you realize it gets pretty complicated, you want to have coordination across teams, and you want to have standardization for the way that you manage your data pipelines. And so having a managed service for Airflow that exists in the cloud is easy to spin up as you expand usage across the organization. And thinking long term about that in the context of orchestration that's where I think the chief data officer or the head of analytics tends to get involved because they really want to think of this as a strategic investment that they're making. Not just per team individual Airflow deployments, but a network of data orchestrators. >> That network is key. Every company these days has to be a data company. We talk about companies being data driven. It's a common word, but it's true. It's whether it is a grocer or a bank or a hospital, they've got to be data companies. So talk to me a little bit about Astronomer's business model. How is this available? How do customers get their hands on it? >> Jeff, go ahead. >> Yeah, yeah. So we have a managed cloud service and we have two modes of operation. One, you can bring your own cloud infrastructure. So you can say here is an account in say, AWS or Azure and we can go and deploy the necessary infrastructure into that, or alternatively we can host everything for you. So it becomes a full SaaS offering. But we then provide a platform that connects at the backend to your internal IDP process. So however you are authenticating users to make sure that the correct people are accessing the services that they need with role-based access control. From there we are deploying through Kubernetes, the different services and capabilities into either your cloud account or into an account that we host. And from there Airflow does what Airflow does, which is its ability to then reach to different data systems and data platforms and to then run the orchestration. We make sure we do it securely, we have all the necessary compliance certifications required for GDPR in Europe and HIPAA based out of the US, and a whole bunch host of others. So it is a secure platform that can run in a place that you need it to run, but it is a managed Airflow that includes a lot of the extra capabilities like the cloud developer environment and the open lineage services to enhance the overall airflow experience. >> Enhance the overall experience. So Steven, going back to you, if I'm a Conde Nast or another organization, what are some of the key business outcomes that I can expect? As one of the things I think we've learned during the pandemic is access to realtime data is no longer a nice to have for organizations. It's really an imperative. It's that demanding consumer that wants to have that personalized, customized, instant access to a product or a service. So if I'm a Conde Nast or I'm one of your customers, what can I expect my business to be able to achieve as a result of data orchestration? >> Yeah, I think in a nutshell it's about providing a reliable, scalable, and easy to use service for developing and running data workflows. And talking of demanding customers, I mean, I'm actually a customer myself, as you mentioned, I'm the head of data for Astronomer. You won't be surprised to hear that we actually use Astronomer and Airflow to run all of our data pipelines. And so I can actually talk about my experience. When I started I was of course familiar with Airflow, but it always seemed a little bit unapproachable to me if I was introducing that to a new team of data scientists. They don't necessarily want to have to think about learning something new. But I think because of the layers that Astronomer has provided with our Astro service around Airflow it was pretty easy for me to get up and running. Of course I've got an incentive for doing that. I work for the Airflow company, but we went from about, at the beginning of last year, about 500 data tasks that we were running on a daily basis to about 15,000 every day. We run something like a million data operations every month within my team. And so as one outcome, just the ability to spin up new production workflows essentially in a single day you go from an idea in the morning to a new dashboard or a new model in the afternoon, that's really the business outcome is just removing that friction to operationalizing your machine learning and data workflows. >> And I imagine too, oh, go ahead, Jeff. >> Yeah, I think to add to that, one of the things that becomes part of the business cycle is a repeatable capabilities for things like reporting, for things like new machine learning models. And the impediment that has existed is that it's difficult to take that from a team that's an analyst team who then provide that or a data science team that then provide that to the data engineering team who have to work the workflow all the way through. What we're trying to unlock is the ability for those teams to directly get access to scheduling and orchestrating capabilities so that a business analyst can have a new report for C-suite execs that needs to be done once a week, but the time to repeatability for that report is much shorter. So it is then immediately in the hands of the person that needs to see it. It doesn't have to go into a long list of to-dos for a data engineering team that's already overworked that they eventually get it to it in a month's time. So that is also a part of it is that the realizing, orchestration I think is fairly well and a lot of people get the benefit of being able to orchestrate things within a business, but it's having more people be able to do it and shorten the time that that repeatability is there is one of the main benefits from good managed orchestration. >> So a lot of workforce productivity improvements in what you're doing to simplify things, giving more people access to data to be able to make those faster decisions, which ultimately helps the end user on the other end to get that product or the service that they're expecting like that. Jeff, I understand you have a demo that you can share so we can kind of dig into this. >> Yeah, let me take you through a quick look of how the whole thing works. So our starting point is our cloud infrastructure. This is the login. You go to the portal. You can see there's a a bunch of workspaces that are available. Workspaces are like individual places for people to operate in. I'm not going to delve into all the deep technical details here, but starting point for a lot of our data science customers is we have what we call our Cloud IDE, which is a web-based development environment for writing and building out DAGs without actually having to know how the underpinnings of Airflow work. This is an internal one, something that we use. You have a notebook-like interface that lets you write python code and SQL code and a bunch of specific bespoke type of blocks if you want. They all get pulled together and create a workflow. So this is a workflow, which gets compiled to something that looks like a complicated set of Python code, which is the DAG. I then have a CICD process pipeline where I commit this through to my GitHub repo. So this comes to a repo here, which is where these DAGs that I created in the previous step exist. I can then go and say, all right, I want to see how those particular DAGs have been running. We then get to the actual Airflow part. So this is the managed Airflow component. So we add the ability for teams to fairly easily bring up an Airflow instance and write code inside our notebook-like environment to get it into that instance. So you can see it's been running. That same process that we built here that graph ends up here inside this, but you don't need to know how the fundamentals of Airflow work in order to get this going. Then we can run one of these, it runs in the background and we can manage how it goes. And from there, every time this runs, it's emitting to a process underneath, which is the open lineage service, which is the lineage integration that allows me to come in here and have a look and see this was that actual, that same graph that we built, but now it's the historic version. So I know where things started, where things are going, and how it ran. And then I can also do a comparison. So if I want to see how this particular run worked compared to one historically, I can grab one from a previous date and it will show me the comparison between the two. So that combination of managed Airflow, getting Airflow up and running very quickly, but the Cloud IDE that lets you write code and know how to get something into a repeatable format get that into Airflow and have that attached to the lineage process adds what is a complete end-to-end orchestration process for any business looking to get the benefit from orchestration. >> Outstanding. Thank you so much Jeff for digging into that. So one of my last questions, Steven is for you. This is exciting. There's a lot that you guys are enabling organizations to achieve here to really become data-driven companies. So where can folks go to get their hands on this? >> Yeah, just go to astronomer.io and we have plenty of resources. If you're new to Airflow, you can read our documentation, our guides to getting started. We have a CLI that you can download that is really I think the easiest way to get started with Airflow. But you can actually sign up for a trial. You can sign up for a guided trial where our teams, we have a team of experts, really the world experts on getting Airflow up and running. And they'll take you through that trial and allow you to actually kick the tires and see how this works with your data. And I think you'll see pretty quickly that it's very easy to get started with Airflow, whether you're doing that from the command line or doing that in our cloud service. And all of that is available on our website >> astronomer.io. Jeff, last question for you. What are you excited about? There's so much going on here. What are some of the things, maybe you can give us a sneak peek coming down the road here that prospects and existing customers should be excited about? >> I think a lot of the development around the data awareness components, so one of the things that's traditionally been complicated with orchestration is you leave your data in the place that you're operating on and we're starting to have more data processing capability being built into Airflow. And from a Astronomer perspective, we are adding more capabilities around working with larger datasets, doing bigger data manipulation with inside the Airflow process itself. And that lends itself to better machine learning implementation. So as we start to grow and as we start to get better in the machine learning context, well, in the data awareness context, it unlocks a lot more capability to do and implement proper machine learning pipelines. >> Awesome guys. Exciting stuff. Thank you so much for talking to me about Astronomer, machine learning, data orchestration, and really the value in it for your customers. Steve and Jeff, we appreciate your time. >> Thank you. >> My pleasure, thanks. >> And we thank you for watching. This is season three, episode one of our ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. You're watching theCUBE, the leader in live tech coverage. (upbeat music)

foreign to the AWS re invent Cube coverage I'm John Furrier here with thecube got a great guest line up here talking about computer vision at the edge and saramba product lead AWS events mobile app and Steven White solution architect for Edge ml thanks for joining me today computer vision at the edge with adios Panorama thanks for coming on happy to be here so what is Ada's Panorama let's get that out there right away what's the focus of that let's define what that is and we'll get into this computer vision at the Edge Story yeah so thanks Sean uh AWS Panorama is our managed uh computer vision at the Ed service and so to put that perspective you know imagine with me the last time that you've been into a restaurant or maybe your favorite retail store or even office building and didn't notice a camera and so we were talking to customers and trying to understand you know what is it that they do with all of this uh video content that they're collecting and surprisingly we found out that large part of this data just sits on a hard drive somewhere and never gets used and so as we dug in a little deeper to better understand you know why this data is just sitting there I think there were three main themes that continue to come up across the board uh one is you know around privacy right privacy security a lot of the data that's being captured with these cameras tend to be either intellectual property that is you know focused on kind of the Manifest factoring process or maybe about their products that they don't want to get out there you know or and or it could just be a private pii data privacy data related to their employee Workforce and and maybe even customers so you know privacy is is a big concern second was just the amount of bandwidth that cameras create and produce tend to be uh prohibitive from for you know sending back to a centralized location for processing uh each camera stream tends to generate about a couple of megabytes of data so it could get very voluminous as you've got tons of cameras at your location and the other issue was around just the latency required to take action on the data so a lot of times especially in the manufacturing space um you know as as you've got a manufacturing line of products that are coming through and you need to take action in milliseconds and so latency is extremely important from process processing time to taking action so those three uh main drivers you know we ended up developing this AWS service called Panorama that addressed these three main challenges with uh you know with analyzing video content and database Panorama in particular there's there's two main components right we've got the compute platform that is about the size of a sheet of paper your standard you know eight and a half by eleven size sheet of paper so the platform itself is extremely compact it's a it's a video and and deep learning algorithms it sits at the customer premise and directly interfaces with video cameras using the standard IP protocols collects that data uh processes it and then immediately deletes the data so there isn't any any information that's actually stored at the location and you know basically the only thing that's left over is just metadata that describes that data and then the other key component here is the cloud um you know service component which helps manage the fleet of devices that are existing so all of these Panorama appliances that are sitting at your premise there's a cloud component that helps you configure you know operationalize check the health as well as deploy applications and configure cameras so that's uh basically you know the the service is really hopefully optimal or you know is focused on um helping customers really make use of all of their video data at the edge you know the theme here at re invent this year is applications we've seen things like connect add value to customers this is one of those situations where everyone's got cameras it's easy to connect to an IP address and Cloud kind of gives you all those Services there are a lot of real world applications that people can can Implement with this because with the cloud you kind of have this ability to kind of stand it up and get value out of that data what are some of the real world applications that it was because they're implementing with the camera because I mean I can see a lot of use cases here where I can you don't have to build the clouds there for me I can stand it up and start getting value what kind of use cases do you see implementing from your customers yeah so our customers are really amazing with the different types of problems um and opportunities that they bring to us for uh using computer vision at the edge in their data um you know we've got everything from animal Warfare use cases to being able to use you know video to uh to to make sure that you know food processing and just you know the health of animals is uh is uh sufficient we've got cases in manufacturing doing visual inspection and anomaly detection so looking at products that are on the conveyor belt as they're being manufactured and put together to make sure that obviously they're they're put together in the right in the right way um and then we've got different port authority and airports that use uh for you know security and cargo tracking to make sure that the products get to where they're supposed to go in a timely and efficient manner manager manage and then finally one of the use cases that really show facing a re invent this year is a part of our retail analytics portfolio which is line counting and so in particular we see a lot of customers in the retail space such as quick service restaurants even you know Peril retail and convenience stores where they want to better understand um you know whether their product is being made to the customer specification we've got like french fry use cases to see how the quality of that french fry is um you know over time and if they need to make a new batch when they've got a influx of customers coming in and to understanding employee to customer ratio maybe they need to put somebody on the cash register you know at busy time so there's really just a big number of customers you know opportunities that we've really solving with the computer vision service looks like a great service Panorama looking good and I want to get your thoughts you have the events happy the product lead take us through with your app I know you have decided to use it was Panorama I was a fit for you this year at re invent 2022 but you know you've been doing this event app for a while now take us through the app when it started how it's evolved and kind of what's the focus this year of course Sean app started in 26 4 re invent and since we've really expanded this year we've actually supported up to 34 events for AWS and continue to expand that for future years for this year though specifically we wanted to contribute to the overall event experience at re invent by helping people go through the process of checking in and picking up their badge in a more formed and efficient way so we decided that the AWS Panorama team and their computer vision and Edge capabilities were the best fit to analyze the lines and the registration kiosks that we have on site at both the Venetian and MGM at the airport we'll have digital signage showcasing our bad pickup wait times that will help attendees select which badge pickup location that they want to go to and see the current wait times live on those signs as well as through the mobile app so I can basically um get the feel for the line size when to come in does it give me a little recognition of who I am and kind of when I get there there's a TIA pull up my records as I do a little intelligence behind the scenes give us a little peek under the covers what's the solution look like so you do have to sign into the mobile app with your registration and so with that we will have your QR code specific for your check-in experience available to you you'll see that at the top of the screen and we'll know once you've checked in that will disappear but if you haven't checked in that Banner is at the top of the event screen and when you tap that that's when you can see all the different options where you can go and pick up your badge we do have five locations this year for badge pickup and the app will help you kind of navigate which one of those options will be best for you given you know maybe you want to pick it up right away at the airport or you may want to go even to one of your other Hotel options that we'll have um to pick it up at foreign okay now I gotta get I got to ask you on the app what's the coolest thing you got going on this year what's new every year there seems to be a new feature what's the focus this year so can you share a a peek on some of the key features yeah so our biggest and most popular features are always around the session catalog and calendar as you can utilize both to of course organize your event schedule and really stay on top of what you want to do on site and get the most out of your reinvent experience this year we have a few new exciting features of course badge pickup line counting is is one of our biggest but we also will have a one-way calendar sync so you can sync all of your calendar activities to your native device calendar as well as pure talk which is our newest feature that we launched at the start of November where you can interact with other attendees who have opted in and even set up time on site to meet one-on-one with them we've also filled that experience with peer talk experts that include AWS experts that are ready to meet and interact with attendees who have interest on site you know I love this topic it's a very cool video we love video we're doing this remote video I'm getting ready for you know all the action and and analyzing it video's cool and so to me if we could look at the video and say hey we haven't soon that might have body cams in the future um video is great people love videos very engaging but always people that say what about my privacy so how do you guys put in place uh mechanisms to preserve attendee privacy yeah I think so I'm not I think you know you and our customers share the same concern and so we have built uh foundationally that AWS Panorama to address you know both privacy and security concerns with uh associated with all this video content and so in particular the AWS Panorama Appliance is something that sits at the customer premise it interface directly with video cameras uh the data all the video that's processed is immediately deleted nothing stored um and you know the outcome of the processing is just simple metadata so it's Text data that you know as an example in the case of the AWS uh line counting solution that we're demoing this year at Panorama along with you know the events team uh it's simply a count of the number of people in the video at any given time so so you know we we do take privacy uh at heart and have made every effort to address them and what are some of the things that you're doing at the event app I mean I'm imagining you're probably looking at space I mean there's a fire marshal issues around you know people do you take it to that level I mean what's how far are you pushing the envelope on on Panorama what are some of the things that you guys are doing besides check-ins or anything you can share on what's Happening the area where we're utilizing you know Anonymous attendee data otherwise other things in the app are very Anonymous just in nature I mean you do sign in but besides that everything we collect is anonymous and we don't collect unless you consent with the cookie consent that appears right when you first launch the app experience besides that we do have as I mentioned peer talk and and that's just where you're sharing information that you want to share with other attendees on site and then we do have session surveys where you can provide information that you wish about how this survey or how the sessions rather went that you attended on-site yeah Stephen you're you're uh your title has you the solution architect for Edge ml this is the Ultimate Edge use case you're seeing here I mean it's a big part of the future of how companies are going to use video and data just what's your reaction to all this I mean we're at a time it's very kind of an interesting time in the history of the industry as you look at this this is a really big part of of the future with video and Edge like I mentioned users are involved people are involved spaces are involved kind of a fun area what's your reaction to where this is right now so personally I'm very passionate about this uh particular solution and service I've been doing computer vision now for 12 years I started doing in the cloud but when I heard about you know customers really looking for an edge component solution and this you know AWS was still in the early stages I knew I had to be a part of it and so I I you know work with some amazing talented engineers and scientists putting this solution together and of course you know our customers continue to bring us these amazing use cases that you know that just I wouldn't get an opportunity to um you know witness without without you know the support of our customers and so we've got some amazing opportunity amazing projects and you know I just love the love to uh experience that with our customers and partners yeah and and Stephen this is like one of those times where the industry has always had this everyone's scratching the niche somewhere but then you get cloud and scale and data come in and just it accelerates some of these areas that were you know I won't say not growing fast but very interesting like computer vision video events technology in the cloud is changing in a good way some of these areas uh and we're seeing that like computer vision as you mentioned Stephen so Ann event same thing I can imagine this event app will blow up to probably be all things Amazon events and and be the touch Touchstone for all customers and attendees I'm probably thinking the road map there's looking pretty interesting with all the vision you have there what's your what's your reaction to the cloud scale meets events absolutely yeah I know we we have a lot of events that happen at AWS and our goal is to have as many of them in the app as possible where it makes sense right we have a lot of partial Day events to multi-day events and the multi-day events are definitely the area where it's harder for an attendee to organize all that they have to do going on on site as well as everything surrounding the event pre-event uh topics and sessions looking up what they want to do to make sure that they're getting the most of their time on site so we really want to make sure that that's something that an attendee can do with our app as well as it showcase as many of the AWS Services as we have like we are doing here with Panorama we have a few other services in the app as well Amazon location service and Amazon connect to name a couple and we hope to just include more and more with each year as well as more events as the time goes on I'm sure your roadmaps looking great the computer vision is awesome I mean this is a mashup integration apis are going to come around the corner so much excitement after re invent love to follow up with you guys and find out more I think this is a super interesting area the convergence of what you guys are working on to kind of wrap up where do you guys see um AWS Panorama going and where can people learn more about how to get involved how to use the service how to test it out where's this going and how do people learn more but first off you can get customers can get more information about panorama from our website aws.amazon.com Panorama and you know I think where we're going is super exciting you know we continue to improve the product to add support for as an example containers we've added support for Hardware acceleration to improve the number of cameras that we can support so we've you know we've got um you know we can support now with a single device up to 30 40 cameras we've got the ability now to support many different uh we continue to expand the interface types that we support um you know and the different types of even adding sensors and you know expanding to Sensor Fusion so not just computer vision but we've learned from customers that they actually want to incorporate other uh other sensor types and other interfaces so we're bringing in the ability to handle you know computer vision and video but also many other data types as well all right and and Stephen thank you for sharing great stuff computer vision at the edge with Panorama thanks for coming on thecube appreciate it thanks for coming on thank you okay AWS coverage here in the cube I'm John for your host thanks for watching

>>Okay, welcome back to everyone. Cube's live coverage of VMware Explorer, 2022. I'm John fur, host of the cube. Two sets three days of live coverage. Dave Ante's here. Lisa Martin, Dave Nicholson, all host of the cube 12 interviews today, just we're with Rocklin and rolling, getting down to the end of the show. As we wind down and look back and look at the future. We've got Steven Jones. Here's the general manager of the VMware cloud on AWS. He's with Amazon web service. Steven Jones. Welcome to the cube. >>Thanks John. >>Welcome back cube alumni. I've been on many times going back to 2015. Yeah. >>Pleasure to be here. Great >>To see you again. Thanks for coming on. Obviously 10 years at AWS, what a ride is that's been, come on. That's fantastic. Tell me it's been crazy. >>Wow. Learned a lot of stuff along the way, right? I mean, we, we, we knew that there was a lot of opportunity, right? Customers wanting the agility and flexibility of, of the cloud and, and we, we still think it's early days, right? I mean, you'll hear Andy say that animals say that, but it really is. Right. If you look at even just the amount of spend that's being spent on, on clouds, it's in the billions, right. And the amount of, of spend in it is still in the trillion. So there's, there's a long way to go and customers are pushing us hard. Obviously >>It's been interesting a lot going on with VM. We're obviously around with them, obviously changing the strategy with their, their third generation and their narrative. Obviously the Broadcom thing is going on around them. And 10 years at abs, we've been, we've been, this'll be our ninth year, no 10th year at reinvent coming up for us. So, but it's 10 years of everything at Amazon, 10 years of S three, 10 years of C two. So if you look at the, the marks of time, now, the history books are starting to be written about Amazon web services. You know, it's about 10 years of full throttle cube hyperscaler in action. I mean, I'm talking about real growth, like >>Hardcore, for sure. I'll give you just one anecdote. So when I first joined, I think we had maybe two EC two instances back in the day and the maximum amount of memory you could conversion into one of these machines was I think 128 gig of Ram fast forward to today. You literally can get a machine with 24 terabytes of Ram just in insane amounts. Right? My, my son who's a gamer tells me he's got 16 gig in his, in his PC. You need to, he thinks that's a lot. >>Yeah. >>That's >>Excited about that. That's not even on his graphics card. I mean, he's, I know it's coming next. The GPU, I mean, just all >>The it's like, right? >>I mean, all the hardware innovation that you guys have done, I mean, look at every it's changed. Everyone's changed their strategy to copy AWS nitro, Dave ante. And I talk about this all the time, especially with James Hamilton and the team over there, Peter DeSantos, these guys have, are constantly going at the atoms and innovating at the, at the level. I mean that, that's how hardcore it is over there right now. I mean, and the advances on the Silicon graviton performance wise is crazy. I mean, so what does that enabling? So given that's continuing, you guys are continuing to do great work there on the CapEx side, we think that's enabling another set of new net new applications because we're starting to see new things emerge. We saw snowflake come on, customer of AWS refactor, the data warehouse, they call it a data cloud. You're starting to see Goldman Sachs. You see capital one, you see enterprise customers building on top of AWS and building a cloud business without spending the CapEx >>Is exactly right. And Ziggy mentioned graviton. So graviton is one of our fastest growing compute families now. And you know, you mentioned a couple of ISVs and partners of ours who are leaning in heavily on porting their own software. Every event Adam announced that we're working with SAP to, to help them port their HANA cloud, which is a, a database of service offering HANA flagship to graviton as well. So it's, it's definitely changing. >>And I think, you know, one of the, and we're gonna circle back to VMware is kind of a point to this. This conversation is that, is that if you look at the trends, right, okay. VMware really tried hard to do cloud and they had a good shot at it V cloud air, but it just, they didn't have the momentum that you guys had at AWS. We saw a lot, lot of other stragglers try to do cloud. They fell off the road, OpenStack, HP, and the list goes on and on. I don't wanna get into that, but the point is, as you guys become more powerful and you're open, right? So you have open ecosystem, you have people now coming back, taking advantage and refactoring and picking up where they left off. VMware was the one of the first companies that actually said, you know what pat Gelsinger said? And I was there, let's clear up the positioning. Let's go all in with AWS. That's >>Right >>At that time, 2016. >>Yeah. This was new for us, for >>Sure. And then now that's set the standard. Now everybody else is kind of doing it. Where is the VMware cloud relationship right now? How is that going out? State's worked. >>It's working well very well. It's I mean, we're celebrating, I think we made the announcement what, five years ago at this conference. Yeah. 2016. So, I mean, it's, it's been a tremendous ride. The best part are the customers who were coming and adopting and proving to us that our vision back then was the right vision. And, and, and what's been different. I think about this relationship. And it was new for us was that we, we purposely went after a jointly engineered solution. This wasn't a, we've got a, a customer or a partner that's just going to run and build something on us. This is something where we both bring muscle and we actually build a, a joint offering together. Talk about, about the main difference. >>Yeah. And that, and that's been working, but now here at this show, if you look at, if you squint through the multi-cloud thing, which is like just, I think positioning for, you know, what could happen in, in a post broad Broadcom world, the cloud native has traction they're Tansu where, where customers were leaning in. So their enterprise customer is what I call the classic. It, you know, mainstream enterprise, which you guys have been doing a lot of business with. They're now thinking, okay, I'm gonna go on continu, accelerate on, in the public cloud, but I'm gonna have hybrid on premise as well. You guys have that solution. Now they're gonna need cloud native. And we were speculating that VMware is probably not gonna be able to get 'em all of it. And, and that there's a lot more cloud native options as customers want more cloud native. How do you see that piece on Amazon side? Because there's a lot of benefits between the VMware cloud on AWS and the services that you guys have natively in your cloud. So we see customers really taking advantage of the AWS goodness, as well as expanding the cloud side at VMware cloud on AWS. >>Yeah. There's probably two ways I would look at this. Right? So, so one is the combination of VMware cloud on AWS. And then both native services just generally brings more options to customers. And so typically what we're seeing now is customers are just able to move much faster, especially as it comes to data center, evacuations, migrating all their assets, right? So it used to be that, and still some customers they're like, I I've gotta think through my entire portfolio of applications and decide what to refactor. And the only way I can move it to cloud is to actually refactor it into some net new application, more and more. We're actually seeing customers. They've got their assets. A lot of them are still on premises in a VMware state, right. They can move those super quick and then modernize those. And so I think where you'll see VMware and AWS very aligned is on this, this idea of migrate. Now you need to get the benefits of TCO and, and the agility that comes with being in the cloud and then modernize. We took a step further, which is, and I think VMware would agree here too, but all of the, the myriad of services, I think it's 200 plus now AWS native services are for use right alongside any that a customer wants to run in VMware. And so we have examples of customers that are doing just, >>And that's, that's how you guys see the native and, and VMware cloud integrating in. Yeah, that's, that's important because this, I mean, if I always joke about, you know, we've been here 12 years listening in the hallways and stuff, you know, on the bus to the event last night, walking the parties and whatnot, listening in the streets, there's kind of two conversations that rise right to the top. And I wanna get your reaction to this Steven, because this seems to be representative of this demographic here at VMware conference, there's conversations around ransomware and storage and D dub and recovery. It's all, a lot of those happen. Yeah. Clearly a big crowd here that care about, you know, Veeam and NetApp and storage and like making sure stuff's secure and air gapped. And a lot of that kind of, I call nerdy conversations and then the other one is, okay, I gotta get the cloud story. >>Right. So there's kind of the operational security. And then there's like, okay, what's my path to true cloud. I need to get this moving. I need to have better applications. My company is the application now not it serves some sort of back office function. Yeah. It's like, my company is completely using technology as its business. So the app is the business. So that means everything's technology driven, not departmental siloed. So there's a, that's what I call the true cloud conversation. How do you, how do you see that evolving because VMware customers are now going there. And I won't say, I won't say they're behind, but they're certainly going there faster than ever before. >>I think, I think, I mean, it's an interesting con it's an interesting way to put it and I, I would completely agree. I think it's, it's very clear that I think a lot of customer companies are actually being disrupted. Right. And they have to move fast and reinvent themselves. You said the app is now becoming the company. Right. I mean, if, if you look at where not too many years back, there were, you know, big companies like Netflix that were born in the cloud. Right. Airbnb they're disruptors. >>There's, that's the >>App, right? That's the app. Yeah. So I, I would exactly agree. And, and that's who other companies are competing with. And so they have to move quickly. You talked about some, some technology that allows them to do that, right? So this week we announced the general availability of a NetApp on tap solution. It's been available on AWS for some time as a fully managed FSX storage solution. But now customers can actually leverage it with, with VMC. Now, why is that important? Well, there's tens of thousands of customers running VMware. On-premises still, there's thousands of them that are actually using NetApp filers, right? NetApp, NetApp filers, and the same enterprise features like replication. D do you were talking about and Snapp and clone. Those types of things can be done. Now within the V VMware state on AWS, what's even better is they can actually move faster. So consider replicating all this, you know, petabytes and petabytes of data that are in these S from on-premises into AWS, this, this NetApp service, and then connected connecting that up to the BMC option. So it just allows customers much, much. >>You guys, you guys have always been customer focus. Every time I sat down with the Andy jazzy and then last year with Adam, same thing we worked back from, I know it's kind of a canned answer on some of the questions from media, but, but they do really care. I've had those conversations. You guys do work backwards from the customer, actually have documents called working backwards. But one of the things that I observed, we talked about here yesterday on the cube was the observations of reinvent versus say, VM world. Now explore is VM world's ecosystem was very partner-centric in the sense of the partners needed to rely on VMware. And the customers came here for both more of the partners, not so much VMware in the sense there wasn't as much, many, many announcements can compare that to the past, say eight years of reinvent, where there's so much Amazon action going on the partners, I won't say take as a second, has a backseat to Amazon, but the, the attendees go there generally for what's going on with AWS, because there's always new stuff coming out. >>And it's, it's amazing. But this year it starts to see that there's an overlap or, or change between like the VMware ecosystem. And now Amazon there's, a lot of our interviews are like, they're on both ecosystems. They're at Amazon's show they're here. So you start to see what I call the naturalization of partners. You guys are continuing to grow, and you'll probably still have thousands of announcements at the event this year, as you always do, but the partners are much more part of the AWS equation, not just we're leasing all these new services and, and oh, for sure. Look at us, look at Amazon. We're growing. Cause you guys were building out and look, the growth has been great. But now as you guys get to this next level, the partners are integral to the ecosystem. How do you look at that? How has Amazon thinking about that? I know there's been some, some, a lot of active reorgs around AWS around solving this problem or no solve the problem, addressing the need and this next level of growth. What's your reaction to >>That? Well, I mean, it's, it's a, it's a good point. So I have to be honest with you, John. I, I, I spent eight of my 10 years so far at AWS within the partner organization. So partners are very near and dear to my heart. We've got tens of thousands of partners and you are you're right. You're starting to see some overlap now between the VMware partner ecosystem and what we've built now in AWS and partners are big >>By the way, you sell out every reinvent. So it's, you have a lot of partners. I'm not suggesting that you, that there's no partner network there, but >>Partners are critical. I mean, absolutely naturally we want a relationship with a customer, but in order to scale the way we need to do to meet the, the needs of customers, we need partners. Right. We, we can't, we can't interact with every single customer as much as we would like to. Right. And so partners have long built teams and expertise that, that caters to even niche workloads or opportunity areas. And, and we love partners >>For that. Yeah. I know you guys do. And also we'll point out just to kind of give props to you guys on the partner side, you don't, you keep that top of the stack open on Amazon. You've done some stuff for end to end where customers want all Amazon, but for the most part, you let competition come in, even on, so you guys are definitely partner friendly. I'm just observing more the maturization of partners within the reinvent ecosystem, cuz we're there every year. I mean, it's, I mean, first of all, they're all buzzing. I mean, it's not like there's no action. There's a lot of customers there it's sold out as big numbers, but it just seems that the partners are much more integrated into the value proposition of at a AWS because of the, the rising tide and, and now their enablement, cuz now they're part of the, of the value proposition. Even more than ever before >>They, they really are. And they, and they're building a lot of capabilities and services on us. And so their customers are our customers. And like you say, it's rising tide, right. We, we all do better together. >>Okay. So let's talk about the VMware cloud here. What's the update here in terms of the show, what's your, what's your main focus cuz a lot of people here are doing, doing sessions. What's been some of the con content that you guys are producing here. >>Yeah. So the best part obviously is a always the customer conversations to partner conversations. So a, a lot of, a lot of sessions there, we did keynote yesterday in Ryan and I, where we talked about a number of announcements that are, I think pretty material now to the offering a joint announcement with NetApp yesterday as well around the storage solution I was talking about. And then some, some really good technical deep dives on how the offering works. Customers are still interested in like how, how do I take what I've got on premises and easily move into AWS and technology like HSX H CX solution with VMware makes it really easy without having to re IP applications. I mean, you know, it is super difficult sometimes to, to move an application. If you've got figure out where all the firewall rules are and re iPing those, those things source. But yeah, it's, it's been fantastic. >>A lot of migrations to the cloud too. A lot of cloud action, new cloud action. You guys have probably seen an uptake on services right on the native side. >>Yes. Yes. For sure. So maybe I just outlined some of the, some of the assets we made this week. So absolutely >>Go ahead. >>We, we announced a new instance family as a, a major workhorse underneath the VMware cloud offering called I, I, you mentioned nitro earlier, this is on, based on our latest generation of nitro, which allows us to offer as you know, bare metal instances, which is, which is what VMware actually VMware was our first partnership and customer that I would say actually drove us to really get Nira done and out the door. And we've continued to iterate on that. And so this I four, I instance, it's based on the, the latest Intel isolate processor with more than double the Ram double the compute, a whopping 75 gigabytes per second network. So it's a real powerhouse. The cool thing is that with the, with the NetApp storage solution that we, we discussed, we're now disaggregating the need to provision, compute and storage at the same time. It used to be, if you wanted to add more storage to your VSAN array, that was on a V VMware cloud. Yeah. You'd add another note. You might not need more compute for memory. You'd have to add another note. And so now customers can simply start adding chunks of storage. And so this opens up customers. I had a customer come to me yesterday and said, there's no reason for us not to move. Now. We were waiting for something that like this, that allowed us to move our data heavy workloads yeah. Into VMware cloud. It's >>Like, it's like the, the alignment. You mentioned alignment earlier. You know, I would say that VMware customers are lined up now almost perfectly with the hybrid story that's that's seamless or somewhat seems it's never truly seamless. But if you look at like what Deepak's doing with Kubernetes and open source, you, you guys have that there talking that big here, you got vs a eight vSphere, eight out it's all cloud native. So that's lined up with what you guys are doing on your services and the horsepower. They have their stuff, you have yours that works better together. So it seems like it's more lined up than ever before. What's your take on that? Do you agree? And, and if so, what folks watching here that are VMware customers, what's, what's the motivation now to go faster? >>Look, it is, it is absolutely lined up. We are, as, as I mentioned earlier, we are jointly engineering and developing this thing together. And so that includes not just the nuts and bolts underneath, but kind of the vision of where it's going. And so we're, we're collectively bringing in customer feedback. >>What is that vision real quick? >>So that vision has to actually help an under help meet even the most demanding customer workloads. Okay. So you've got customer workloads that are still locked in on premises. And why is that? Well, it used to be, there was big for data and migration, right? And the speed. And so we continue to iterate this and that again is a joint thing. Instead of say, VMware, just building on AWS, it really is a, a tight partnership. >>Yeah. The lift and shift is a, an easy thing to do. And, and, and by the way, that could be a hassle too. But I hear most people say the reason holding us back on the workloads is it's just a lot of work, a hassle making it easier is what they want. And you guys are doing that. >>We are doing that. Absolutely. And by the way, we've got not just engineering teams, but we've got customer support teams on both sides working together. We also have flexible commercial options, right? If a customer wants to buy from AWS because they've negotiated some kind of deal with us, they can do that. They wanna buy from VMware for a similar reason. They could buy from VMware. So are >>They in the marketplace? >>They are in the market. There, there are some things in the marketplace. So you talked about Tansu, there's a Tansu offering in the marketplace. So yes. Customers can >>Contract. Yeah. Marketplaces. I'm telling you that's very disruptive. I'm Billy bullish on the market AIOS marketplace. I think that's gonna be a transformative way. People have what they procure and fully agree, deploy and how, and channel relationships are gonna shift. I think that's gonna be a disruptive enabler to the partner equation and, and we haven't even seen it yet. We're gonna be up there in September for their inaugural event. I think it's a small group, but we're gonna be documenting that. So even final question for you, what's next for you? What's on the agenda. You got reinvent right around the corner. Your P ones are done. Right? I know. Assuming all that, I turn that general joke. That's an internal Amazon joke. FYI. You've got your plan. What's next for the world. Obviously they're gonna go this, take this, explore global. No matter what happens with Broadcom, this is gonna be a growth wave with hybrid. What's next for you and your team with AWS and VMware's relationship? >>Yeah. So both of us are hyper focused on adding additional options, both from a, an instance compute perspective. You know, VMware announced some, some, some additional offerings that we've got. We've got a fully complete, like, so they're, they announce things like VMware flex compute V VMware flex storage. You mentioned earlier, there was a conversation around ransomware. There's a new ransomware based offering. So we're hyper focused on rounding out, continuing to round out the offering and giving customers even more choice >>Real quick. Jonathan made me think about the ransomware we were at reinforce Steven Schmidtz now the CSO. Now you got a CSO. AJ's the CSO. You got a whole focus, huge emphasis on security right now. I know you always have, but now it's much more public. It's PO more positive, I think, than some of the other events I've been to. It's been more Lum and doom. What's the security tie in here with VMware. Can you share a little bit real quick on the security piece update around this relationship? >>Yeah, you bet. So as you know, security for us is job zero. Like you don't have anything of security. And so what are the things that, that we're excited about specifically with VMware is, is the latest offering that, that we put together and it's called this, this ransomware offering. And it's, it's a little bit different than other ransomware. I mean, a lot of people have ransomware offerings today, just >>Air gap. >>Right, right, right. Exactly. No, that's easy. No, this one is different. So on the back end, so within VMC, there's this, this option where CU we can be to be taking iterative snapshots of a customer environment. Now, if an event were to occur, right. And a customer is like, I have to know if I'm compromised, we can actually spin up super easy. This is cloud. Remember? Yeah. We can spin up a, a copy of this environment, throw a switch, pick a snapshot with NSX. So VMware NSX firewall it off and then use some custom tooling from VMware to actually see if it's been compromised or not. And then iterate through that until you actually know you're clean. And that's different than just tools that do maybe a >>Little bit of scam. We had Tom gills on yesterday and, and one of the things Dave ante had to leave is taking the sun to college is last one in the house and B nester now, but Tom Gill was on. We were talking about how good their security story is ware. And they really weren't showboating it as much as they could have here. I thought they could have done a better job, but this is an example of kind of them really leaning in with you guys. That's the key part of the relationship. >>Yeah, it really is. And I think this is something is materially different than what you can get elsewhere. And it's exciting for, >>Okay. Now the, the real question I want to know is what's your plans for AWS reinvent the blockbuster end of the year, Amazon surf show that gets bigger and bigger. I know it's still hybrid now, but it's looking be hybrid, but people are back in person last year. You guys were the first event really come back and still had massive numbers. AWS summit, New York at 19,000. I heard last week in Chicago, big numbers. So we're expecting reinvent to be pretty large this year. What are you, what are you gonna do there? What's your role there? >>We are expecting, well, I'll be there. I cover multiple businesses. Obviously. We're, we're planning on some additional announcements, obviously in the VMware space as well. And one of the other businesses I run is around SAP. And you should look for some things there as well. Yeah. Really looking forward to reinvent, except for the fact that it's right after Thanksgiving. But I think it >>Always ruins my, I always get an article out. I like, why are you we're having, we're having Thanksgiving dinner. I gotta write this article. It's gotta get Adam, Adam. Leski exclusive. We, every year we do a, a CEO sit down with Andy was the CEO and then now Adam. But yeah, it's a great event to me. I think it sets the tone. And it's gonna be very interesting to see the big clouds are coming to the big cloud. You guys, and you guys are now called hyperscalers. Now, multiple words. It's interesting. You guys are providing the CapEx goodness for everybody else now. And that relationship seems to be the new, the new industry standard of you guys provide the enablement and then everyone you get paid, cuz it's a service. A whole nother level of cloud is emerging in the partner network, GSI other companies. Yeah. >>Yeah. I mean we're really scaling. I mean we continue to iterate and release regions at a fast clip. We just announced support for VMware in Hong Kong. Yeah. So now we're up to 21 regions for this service, >>The sovereign clouds right around the corner. Let's we'll talk about that soon. Steven. Thanks for coming. I know you gotta go. Thank you for your valuable time. Coming in. Put Steven Jones. Who's the general manager of the VMware cloud on AWS business. Four AWS here inside the cube day. Three of cube coverage. I'm John furrier. Thanks for watching. We'll be right back.

>>Hey everyone. Welcome back to the Cube's day one coverage of VMware Explorer, 2022 live from San Francisco. I'm Lisa Martin, and I'm basically sitting with the cloud. I got a power panel here with me. You are not gonna wanna miss the segment, please. Welcome, nor Barage I probably did. I do. Okay on that. Great, thank you. VP and GM of cloud solutions at VMware. Thanks for joining us. Field brother tune is back our alumni VP solutions and alliances at NetApp bill. Great to see you in person. Thank you. And Steve Jones, GM SAP, and VMware cloud at Amazon. Welcome guys. Thank you. Pleasure. So we got VMware, NetApp and Amazon. I was telling Phil before we went live, I was snooping around on the NetApp website the other day. And I saw a tagline that said two is the company three is a cloud, but I get to sit with the cloud. This is fantastic. Nora, talk to us about the big news that came out just about 24 hours ago. These three powerhouse, we >>Were super excited. We are celebrating five years of VMware cloud this week. And with three powerhouses here, we're announcing the general availability of VMware cloud and AWS with NetApp on tap. We have AWS FSX. And so this solution is now generally available across all global regions. We are super excited with all our joint customers and partners to bring this to the market. >>So Steve, give us your perspective as AWS as the biggest hyperscaler. Talk about the importance of the partnership and the longstanding partnerships that you've had with both NetApp and VMware. >>Yeah, you bet. So first all, maybe I'll start with Ryan and VMware. So we've had a very long standing partnership with VMware for over five years now. One thing that we've heard consistently from customers is they, they want help in reducing the heavy lifting or the, the friction that typically comes with cloud adoption. And VMware's been right in the trenches with us and helping with that over the years with the VMware cloud on AWS offering. And, and now that we've got NetApp, right, the FSX on tap solution, a managed storage solution that is, is been known and trusted in the on-premises world. Now available since September on AWS, but now available for use with VMware cloud is just amazing for customers who are looking for that agility, >>Right? Phil talk about NetApp has done a phenomenal job in its own digital transformation journey. Talk about that as an enabler for what you announced yesterday and the, and the capabilities that NetApp is able to bring to its customers with VMware and with AWS. >>Yeah. You know, it started, it's interesting because we NetApp's always been a company that works very closely with our partners. VMware has been a huge partner of ours since gosh, 2005 probably, or sometime like that. I started working with Amazon back in about 20 13, 20 14, when we first took on tap and brought it to the Amazon platform in the marketplace ahead of what's. Now FSX ends like a dream to bring a fully managed ONAP onto the world's biggest cloud. So that work you you're really looking at about. I mean, it depends how you look at it, 15 years of work. And then as Ryan was saying that VMware was working in parallel with us on being a first party service on Amazon, we came together and, or Ryan and I came together and VMware and NetApp came together about probably about two years ago now with this vision of what we're announcing today and to have so to have GA of this combination for meaning global availability, anybody can try it today. It's just an amazing day. It's really a great day. >>Yeah. It's unbelievable how we have sort of partnered together and hard engineering problems to create a very simple outcome for customers and partners. One of the things, you know, VMware cloud is a very successful service offering with a lot of great consumption and different verticals. Things like cloud migration, you know, transforming your entire, you know, data center and moving to the cloud. Things like, you know, modernizing our apps, disaster recovery now ransomware this week. So really, really exciting uptake and innovation in that whole service. One thing customers always told us that they want more options for storage decouple from compute. And so that really helped customers to lower their total cost of ownership and get to, you know, get even more workloads into VMware cloud. And this partnership really creates that opportunity for us to provide customers with those options. >>Let me give you an example, just I was walking over here just before I walked over here. We were with a customer talking about exactly what Orion's talking about. We were modeling using a TCO calculator that we all put together as well on what we call data intensive workloads, which is in this case, it was a 500 gigabytes per VM. So not a huge amount of data per VM. The, the case study modeled out of 38% cost savings or reduction in total cost, which in the case was like 1.2 million per year of total cost down to 700 million. And just, you could do the, just depends on how many VMs you have and how big odes you have, but that's the kind of cost savings we're talking about. So the, this is a really easy value to talk about. You save a lot of money in it's exactly as nor Ryan said, because we can separate the compute and the storage. Yep. >>Yep. I was just gonna say the reason for that is it used to be with VMware cloud on AWS. If you wanted more storage for your workload, you would have to add another node. So with another node, you would get another compute node. You would get the compute, you'd get the memory and the storage, but now we've actually decoupled the ability to expand the storage footprint from the compute, allowing customers to really expand as their needs grow. And so it's, it's just a lot more flexibility. Yep. That customers had. Yeah. >>Flexibility is key. Every customer needs that they need to be agile. There's always a competitor waiting in the rear view mirror behind any business, waiting to take over. If, if they can't innovate fast enough, if they can't partner with the best of the best to deliver the infrastructure that's needed to enable those business outcomes, I wanna get your perspective, Steve, what are some of the outcomes that when you're talking to customers, you talked about fill the TCO. Those are huge numbers, very compelling. What are some of the other outcomes that customers can expect to achieve from this solution? >>That's a great question. I think customers want the flexibility. We talked about customers absolutely wanna be able to move fast. They're also very demanding customers who have had an experience with solutions like NetApp on tap on premises, right? So they've come to expect enterprise features like thin provisioning, snapshoting cloning, rapid cloning, right? And even replication of data given that customers now can leverage this type of functionality as well through the NetApp solution with VMC, they're getting all those enterprise class features from, from the storage in combination with what they already had with vs a and, and VMC. >>Steve earlier mentioned the word we used, we kind of took it from VMware or from Amazon was friction is so many workloads run in VMware VMs today to be able to just simply pick them up as is move them to Amazon makes cloud adoption. Just, I mean, frictionless is an extreme word, but it's really lowers the friction to cloud adoption. And as Steve said, then you've get all these enterprise features wherever you need to run. >>Just brings speed. >>I was just about to say, it's gotta be the speed. It has to be a huge factor here. Yep, >>Yep. Yeah. >>Sure. One of the things that we've seen with VMware cloud is operational consistency as, as a customer value because when customers are thinking about, you know, complex enterprise apps, moving that to the cloud, they need that operational consistency, which drives down their costs. They don't have to relearn new skills. They're used to VMware, they're used to NetApp. And so this partnership really fosters that operational consistency as a big customer value, and they can reuse those skills and really reapply them in this cloud model. The other thing is the cloud model here is super completely managed. If you think about that, right, customers have to do less VMware, AWS and NetApp is doing more for them. That's true in this model. >>So you're able to really deliver a lot of workforce efficiency, workforce productivity across the stack. >>Absolutely. >>And that's definitely true that it just, as it gets more complex, how do you manage it? Just continue, hear everybody talking about this, right. So when a completely managed service by VMware and Amazon is such a savings in com in management complexity, which then gets back to speed. How do I grow my plant faster? >>I mean, and really at the end of the day, customers are actually able to focus on what differentiate differentiates them, obviously versus the management of the underlying infrastructure and storage and all those, those things that are still critical, but exactly, but >>For, for the customer to be able to have to abstract the underlying underlying technology layer and focus on what differentiates them from the competition. That's like I said, right back here, right. That's especially if there's anything we've learned in the last couple of years, it's that it, that is critical for businesses across every industry, no industry exempt from this. >>None. One other thing, just an example of what you're talking about is we all work a lot on modernization techniques like using Kubernetes and container technologies. So with this, if you think about this, you, this solution, you can move an app as is modernize on the cloud. You can modernize, you can modernize and then move. You can, the flexibility that this enables like. So it's sort of like move to the cloud at your rate is a really big benefit. >>And we've seen so many customer examples of migrating modernize is how we like to summarize it, where customers are, you know, migrating, modernizing at their own pace. Yep. And the good, good thing about the platform and the service is that it is the home for all applications, virtual machines containers with Kubernetes backed by local storage, external storage options. The level of flexibility for all applications is really immense. And that drives down your TCO even more. >>What, from a target customer perspective, Noran, talk about that. Who, who is the target? Obviously I imagine VMware customers, it's NetApp customers, it's AWS, but is there, are there any targets kind of within that, that are really prime candidates for this solution? >>Yeah. A great question. First of all, the, the easy sort of overlap between all of us is our shared customer pool. And so VMware and NetApp have been partners for what, 20 years, something like that. And we have thousands of customers using our joint solutions in the data center. And so that's a very clear target for this solution, as they're considering use cases such as, you know, cloud migration, disaster recovery, virtual desktops, application modernization. So that's a very clear target and we see this day in and day out, obviously there are many other customers that would be interested in this solution, as well as they're considering, you know, AWS and we provide a whole range of consumption options for them. Right. And I think that's one of the, sort of the, the good things about our partnership, including with AWS, where customers can purchase this from VMware can purchase this from AWS and all of these different options, including from our partners really makes it very, very compelling. >>Talk a little bit about from each of your perspectives about the what's in it. For me as a partner of these companies, Steve, we'll start with you. >>I mean, what's in it for me is that it's what my customers have been asking for. And we, we have a long history, I think of providing managed services again, to remove that heavy lifting that customers often just don't want to have to do. Having seen the, the adoption of managed storage offerings, including the, the NetApp solution here and now being able to bring that into the VMware space where they're already using it in an on-premises world, and now they're moving those, those workloads being able to satisfy that need that a customer's asking for is awesome. >>We, every time we're at an AWS event, we are always talking about it's absolute customer obsession, and I know NetApp and VMware well, and know that that is a shared obsession across the three companies. >>Hey, Lisa, let me add one more thing. It's interesting, not everybody sees this, but it's really obvious that the NetApp on-prem installed base with VMware, which is tens of thousands of customers. This is an awesome solution. Not quite as obvious is that every on-prem VMware customer gets that TCO benefit. I mentioned that's not limited to the NetApp on-prem installed base. So we're really excited to be able to expose all the market that hasn't used our products on-prem to this cloud solution. And, and it's really clear customers are adopting the cloud, right? So we're, that's one of the reasons we're so excited about this is it opens up a huge new opportunity to work with new customers for us. Talk >>About those customer conversations, Phil, how, where are they happening at? What level are you talking with customers about migration to cloud? Has it changed in the last couple >>Of years? Oh yeah. You know, I've been working on this for years and a lot of the on-prem conversation, it's been a little bifurcated that on-prem is on-prem and cloud developers or cloud developers. And Amazon's done a huge amount to break that down. VMware getting in the game, a lot of it's networking complexities, those have gone down. A lot of people are cross connected and set up today, which that wasn't so true five years ago. So now it's a lot of conversations about, I hear carbon footprint reduction. I hear data all in around data center reduction. The cloud guys are super efficient operators of data center infrastructure. We were talking about different use cases like disaster recovery. It's it's everybody though. It's small companies, it's big companies. They're all sort of moving into this, it call it at least hybrid world. And that's why when I say we're get really excited about this, because it does get rid of a lot of friction for moving loads in those directions, at the rate, the customer wants to do it. >>And that one last really quick thing is I was using NetApp as an example, we have about 300 enterprise workloads. We wanna move to the cloud two, right? And so they're all running VMware, like most, most of the world. And so this solution is, looks really good to us and we're gonna do the exact, I was just out with our CIO. We're going, looking at those 300, which do we just lift and move? Which do we refactor? And how do we do that? In fact, that Ryan was out to dinner with us last night, talking about >>This it's more and more it's being driven top down. So in the early days, and I've been with Amazon for 10 years now. Yep. Early days, it was kind of developer oriented, often initiated projects. Now it's top level CIOs. Exactly. I >>Are two mandates today talking to customers. >>I think of reinvent as an it conference. Now in the way, some of these top down mandates are driven, but listen, I mean, we got great customer interest. We have been in preview for three to six months now, and we've seen a lot of customers were not able to drag their entire data center workloads because of different reasons of PCO data, intensive workloads, et cetera. And we've seen tremendous amounts of interest from them. And we're also seeing a lot of new customers in the pipeline that want to consider VMware cloud now that we have these great storage options. >>So there's a pretty healthy Tam I'm hearing. >>Absolutely. >>I think so. Yeah. It's interesting. Another, just both like WWT and Presidio, channel partners, big, huge channel partners. It takes no selling to explain. We, we just say, Hey, we're doing this. And they start building services. Presidio is here with us talking about a customer win that they got. So this is it. It's easy for people to see why this is a cool, a cool solution. >>The value prop is there >>Definitely >>There's no having appeal the onion to >>Find it. No, the money savings. It's just in what or Ryan said, a lot of people have seen the, the seen an obstacle of cost. Yeah. So the TCO benefit, I mentioned removes that obstacle. And then that opens the door to all the features Steve was talking about of the advanced storage features and things on the platform. >>So is there a customer that's been in beta on this solution that you can talk about in, in terms of what they were looking for, the challenges that you helped them erase and the outcomes they're achieving? >>Yeah, sure. I can. I can provide one example. A large financial customer was looking at this during the preview phase and you know, for, for, for reasons before that were already a customer, but they were not able to attract a lot of their other workloads from other business units. And with this solution, now the service is a much better candidate for those workloads and those business units that had not considered VMware cloud. So we're really excited to see new workloads coming from that particular customer, given this particular solution and the whole TCO math for them was very, very straightforward and simple. And this became a more attractive option for that particular customer. >>Is there a shadow it elimination factor here in this technology and who you're selling to? >>Not real, I, don't not intent. Wouldn't intentionally. I wouldn't say yeah, not intentionally. I, it was funny with the customers I was thinking is yes. The question, the customers that are in the preview are seeing the benefits that we're talking about. The, one of the reasons we started the project on our side a number of years ago was this very large cement company was looking for carbon CO2 reduction. Part of that was moving disaster recovery to the cloud. There was a lot of friction in the solution prior to this, the, the customers have done some of the things we're talking about, but there's a, it takes a lot of skill. And we were looking at working with that customer going, how could we simplify this? And that was from our point of NetApp's point of view, it, it drove us to VMware and to AWS saying, can't we pull some of the friction of this out. And I think that that's what we've seen in the, in the previews. And it's, that's what I meant. It's so exciting to go from having say, I know we have about 20 previews right now, going to the globe today is the, is the exciting news today. >>And is the solution here in booze that it can be demoed and folks can kind of get their hands on it. >>Yeah. Yeah. They can go to the VMware cloud booth at the expo and they can get their hands on their demo and they can take it for a test drive. >>Excellent. >>You can run TCO calculators and do your own math and see what you're gonna all this, the all that's integrated today. We >>Also have pilots where we can help walk customers through a scenario of their own. >>Yep. Excellent. Is there, is there a, a joint website that you guys have, we should drive folks to? >>Yeah, it's >>Actually talk about the press release. It's >>It's yours. So >>It's it's prominently on our website. Okay. VMware cloud. It is onc.vmware.com where we also have the other, you know, our corporate marketing websites that have this vmware.com is a great starting point. Yeah. And we feature the solution. Prominently customers can get started today and they can even participate in the hands on labs here and take the solution for a test drive. >>All right. Last question, nor Ryan, we'll start with you on this. Here we are. I love the theme of this event, the center of the multicloud universe. Does it not sound like a Marvel movie? I feel like there should be some, is there any superheroes running around? Cause I really feel like there should be, how is this solution an enabler of allowing customers to really extract the most of value from their multi-cloud world that they're living in? >>Yeah. I mean, look, I mean, our mission is to build, run, managed, secure applications in any cloud, right. And regu has been talking about this with the keynote this morning as well. You know, at least with NetApp, we share a very good joint vision of enabling customers to, you know, place applications with really good TCO across clouds. And so it's really good story I feel. And I think this is a really good step in that direction where customers have choice and flexibility in terms of where they put their applications in the TCO value that they get. >>Awesome. Guys, you gotta come back next with a customer would love to dig. Maybe at reinvent sounds, we can dig into more and to see a great story of how a customer came together and is really leveraging that the power that is sitting next to me here. Thank you all so much for joining me and having this great conversation. Congratulations on the announcement and it being GA. >>Thank you. Awesome. >>Thank you. Thanks Lisa. All right. Fun conversation. I told you power panel for my guests. I'm Lisa Martin. You're watching the cube, keep it right here for more live coverage of VMware Explorer, 2022 from downtown San Francisco. We'll be right back with our next guest.

(bright music) >> Hey everyone, welcome to this CUBE conversation featuring Ahana, I'm your host Lisa Martin. I've got two guests here with me today. Steven Mih joins us, the Presto Foundation governing board member, co-founder and CEO of Ahana, and Girish Baliga Presto Foundation governing board chair and senior engineering manager at Uber. Guys thanks for joining us. >> Thanks for having us. >> Thanks for having us. >> So Steven we're going to dig into and unpack Presto in the next few minutes or so, but Steven let's go ahead and start with you. Talk to us about some of the challenges with the open data lake house market. What are some of those key challenges that organizations are facing? >> Yeah, just pulling up the slide you know, what we see is that many organizations are dealing with a lot more data and very different data types and putting that all into, traditionally as the data warehouse, which has been the workhorse for BI and analytics traditionally, it becomes very, very expensive, and there's a lot of lock in associated with that. And so what's happening is that people are putting the data semistructured and unstructured data for example, in cloud data lakes or other data lakes, and they find that they can query directly with a SQL query engine like Presto. And that lets you have a much more approach to dealing with getting insights out of your data. And that's what this is all about, and that's why companies are moving to a modern architecture. Girish maybe you can share some of your thoughts on how Uber uses Presto for this. >> Yeah, at Uber we use Presto in our internal deployments. So at Uber we have our own data centers, we store data locally in our data centers, but we have made the conscious choice to go with an open data stack. Our entire data stack is built around open source technologies like Hadoop, Hive, Spark and Presto. And so Presto is an invaluable engine that is able to connect to all these different storage and data formats and allow us to have a single entry point for our users, to run their SQL engines and get insights rather quickly compared to some of the other engines that we have at Uber. >> So let's talk a little bit about Presto so that the audience gets a good overview of that. Steven starting with you, you talked about the challenges of the traditional data warehouse application. Talk to us about why Presto was founded the open, the project, give us that background information if you will. >> Absolutely, so Presto was originally developed out of the biggest hyperscaler out there which is Facebook now known as Meta. And they donated that project to the, and open sourced it and donated it to the Linux Foundation. And so Presto is a SQL query engine, it's a storage SQL query engine, that runs directly on open data lakes, so you can put your data into open formats like 4K or C, and get insights directly from that at a very good price performance ratio. The Presto Foundation of which Girish and I are part of, we're all working together as a consortium of companies that all want to see Presto continue to get bigger and bigger. Kind of like Kubernetes has a, has an organization called CNCF, Presto has Presto Foundation all under the umbrella of the Linux Foundation. And so there's a lot of exciting things that are coming on the roadmap that make Presto very unique. You know, RaptorX is a multilevel caching system that it's been fantastic, Aria optimizations are another area, we Ahana have developed some security features with donating the integrations with Apache Ranger and that's the type of things that we do to help the community. But maybe Girish can talk about some of the exciting items on the roadmap that you're looking forward to. >> Absolutely, I think from Uber's point of view just a sheer scale of data and our volume of query traffic. So we run about half a million Presto queries a day, right? And we have thousands of machines in our Presto deployments. So at that scale in addition to functionality you really want a system that can handle traffic reliably, that can scale, and that is backed by a strong community which guarantees that if you pull in the new version of Presto, you won't break anything, right? So all of those things are very important to us. So I think that's where we are relying on our partners particularly folks like Facebook and Twitter and Ahana to build and maintain this ecosystem that gives us those guarantees. So that is on the reliability front, but on the roadmap side we are also excited to see where Presto is extending. So in addition to the projects that Steven talked about, we are also looking at things like Presto and Spark, right? So take the Presto SQL and run it as a Spark job for instance, or running Presto on real-time analytics applications something that we built and contributed from Uber side. So we are all taking it in very different directions, we all have different use cases to support, and that's the exciting thing about the foundation. That it allows us all to work together to get Presto to a bigger and better and more flexible engine. >> You guys mentioned Facebook and I saw on the slide I think Twitter as well. Talk to me about some of the organizations that are leveraging the Presto engine and some of the business benefits. I think Steve you talked about insights, Steven obviously being able to get insights from data is critical for every business these days. >> Yeah, a major, major use case is finding the ad hoc and interactive queries, and being able to drive insights from doing so. And so, as I mentioned there's so much data that's being generated and stored, and to be able to query that data in place, at a, with very, very high performance, meaning that you can get answers back in seconds of time. That lets you have the interactive ability to drill into data and innovate your business. And so this is fantastic because it's been developed at hyperscalers like Uber that allow you to have open source technology, pick that up, and just download it right from prestodb.io, and then start to run with this and join the community. I think from an open source perspective this project under the governance of Linux Foundation gives you the confidence that it's fully transparent and you'll never see any licensing changes by the Linux Foundation charter. And therefore that means the technology remains free forever without later on limitations occurring, which then would perhaps favor commercialization of any one vendor. That's not the case. So maybe Girish your thoughts on how we've been able to attract industry giants to collaborate, to innovate further, and your thoughts on that. >> Yeah, so of the interesting I've seen in the space is that there is a bifurcation of companies in this ecosystem. So there are these large internet scale companies like Facebook, and Uber, and Twitter, which basically want to use something like Presto for their internal use cases. And then there is the second set of companies, enterprise companies like Ahana which basically wanted to take Presto and provide it as a service for other companies to use as an alternative to things like Snowflake and other systems right? So, and the foundation is a great place for both sets of companies to come together and work. The internet scale companies bring in the scale, the reliability, the different kind of ways in which you can challenge the system, optimize it, and so forth, and then companies like Ahana bring in the flexibility and the extensibility. So you can work with different clouds, different storage formats, different engines, and I think it's a great partnership that we can see happening primarily through the foundational spaces. Which you would be hard pressed to find in a single vendor or a, you know, a single-source system that is there on the market today. >> How long ago was the Presto Foundation initiated? >> It's been over three years now and it's been going strong, we're over a dozen members and it's open to everyone. And it's all governed like the Linux Foundation so we use best practices from that and you can just check it out at prestodb.io where you can get the software, or you can hear about how to join the foundation. So it includes members like Intel, and HPE as well, and we're really excited for new members to come, and contribute in and participate. >> Sounds like you've got good momentum there in the foundation. Steven talk a little bit about the last two years. Have you seen the acceleration in use cases in the number of users as we've been in such an interesting environment where the need for real-time insights is essential for every business initially a few couple of years ago to survive but now to be, to really thrive, is it, have you seen the acceleration in Presto in that timeframe? >> Absolutely, we see there's acceleration of being more data-driven and especially moving to cloud and having more data in the cloud, we think that innovation is happening, digital innovation is happening very fast and Presto is a major enabler of that, again, being able to get, drive insights from the data this is not just your typical business data, it's now getting into really clickstream data, knowing about how customers are operating today, Uber is a great example of all the different types of innovations they can drive, whether it be, you know, knowing in real time what's happening with rides, or offering you a subscription for special deals to use the service more. So, you know, Ahana we really love Presto, and we provide a SaaS manage service of the open source and provide free trials, and help people get up to speed that may not have the same type of skills as Uber or Facebook does. And we work with all companies in that way. >> Think about the consumers these days, we're very demanding, right? When I think one of the things that was in short supply during the last two years was patience. And if I think of Uber as a great example, I want to know if I'm asking for a ride I want to know exactly in real time what's coming for me? Where is it now? How many more minutes is it going to take? I mean, that need to fulfill real-time insights is critical across every industry but have you seen anything in the last couple years that's been more leading edge, like e-commerce or retail for example? I'm just curious. >> Girish you want to take that one or? >> Yeah, sure. So I can speak from the Uber point of view. So real-time insights has really exploded as an area, particularly as you mentioned with this just-in-time economy, right? Just to talk about it a little bit from Uber side, so some of the insights that you mentioned about when is your ride coming, and things of that nature, right? Look at it from the driver's point of view who are, now we have Uber Eats, so look at it from the restaurant manager's point of view, right? They also want to know how is their business coming? How many customer orders are coming for instance? what is the conversion rate? And so forth, right? And today these are all insights that are powered by a system which has a Presto as an front-end interface at Uber. And these queries run like, you have like tens of thousands of queries every single second, and the queries run in like a second and so forth. So you are really talking about production systems running on top of Presto, production serving systems. So coming to other use cases like eCommerce, we definitely have seen some of that uptake happen as well, so in the broader community for instance, we have companies like Stripe, and other folks who are also using this hashtag which is very similar to us based on another open source technology called Pino, using Presto as an interface. And so we are seeing this whole open data lakehouse more from just being, you know, about interactive analytics to driving all different kinds of analytics. Having anything to do with data and insights in this space. >> Yeah, sounds like the evolution has been kind of on a rocket ship the last couple years. Steven, one more time we're out of time, but can you mention that URL where folks can go to learn more? >> Yeah, prestodb.io and that's the Presto Foundation. And you know, just want to say that we'll be sharing the use case at the Startup Showcase coming up with theCUBE. We're excited about that and really welcome everyone to join the community, it's a real vibrant, expanding community and look forward to seeing you online. >> Sounds great guys. Thank you so much for sharing with us what Presto Foundation is doing, all of the things that it is catalyzing, great stuff, we look forward to hearing that customer use case, thanks for your time. >> Thank you. >> Thanks Lisa, thank you. >> Thanks everyone. >> For Steven and Girish, I'm Lisa Martin, you're watching theCUBE the leader in live tech coverage. (bright music)

(upbeat soft intro music) >> Hey everyone. Welcome back to theCube's live coverage from Los Angeles of KubeCon and CloudNativeCon 2021. Lisa Martin with Dave Nicholson, Dave and I are pleased to welcome our next guest remotely. Steven Huels joins us, the senior director of Cloud Services at Red Hat. Steven, welcome to the program. >> Steven: Thanks, Lisa. Good to be here with you and Dave. >> Talk to me about where you're seeing traction from an AI/ML perspective? Like where are you seeing that traction? What are you seeing? Like it. >> It's a great starter question here, right? Like AI/ML is really being employed everywhere, right? Regardless of industry. So financial services, telco, governments, manufacturing, retail. Everyone at this point is finding a use for AI/ML. They're looking for ways to better take advantage of the data that they've been collecting for these years. It really, it wasn't all that long ago when we were talking to customers about Kubernetes and containers, you know, AI/ML really wasn't a core topic where they were looking to use a Kubernetes platform to address those types of workloads. But in the last couple of years, that's really skyrocketed. We're seeing a lot of interest from existing customers that are using Red Hat open shift, which is a Kubernetes based platform to take those AI/ML workloads and take them from what they've been doing for additionally, for experimentation, and really get them into production and start getting value out of them at the end of it. >> Is there a common theme, you mentioned a number of different verticals, telco, healthcare, financial services. Is there a common theme, that you're seeing among these organizations across verticals? >> ^There is. I mean, everyone has their own approach, like the type of technique that they're going to get the most value out of. But the common theme is really that everyone seems to have a really good handle on experimentation. They have a lot of very brig data scientists, model developers that are able to take their data and out of it, but where they're all looking to get, get our help or looking for help, is to put those models into production. So ML ops, right. So how do I take what's been built on, on somebody's machine and put that into production in a repeatable way. And then once it's in production, how do I monitor it? What am I looking for as triggers to indicate that I need to retrain and how do I iterate on this sequentially and rapidly applying what would really be traditional dev ops software development, life cycle methodologies to ML and AI models. >> So Steve, we're joining you from KubeCon live at the moment. What's, what's the connection with Kubernetes and how does Kubernetes enable machine learning artificial intelligence? How does it enable it and what are some of the special considerations to in mind? >> So the immediate connection for Red Hat, is Red Hat's open shift is basically an enterprise grade Kubernetics. And so the connection there is, is really how we're working with customers and how customers in general are looking to take advantage of all the benefits that you can get from the Kubernetes platform that they've been applying to their traditional software development over the years, right? The, the agility, the ability to scale up on demand, the ability to have shared resources, to make specialized hardware available to the individual communities. And they want to start applying those foundational elements to their AI/Ml practices. A lot of data science work traditionally was done with high powered monolithic machines and systems. They weren't necessarily shared across development communities. So connecting something that was built by a data scientist, to something that then a software developer was going to put into production was challenging. There wasn't a lot of repeatability in there. There wasn't a lot of scalability, there wasn't a lot of auditability and these are all things that we know we need when talking about analytics and AI/ML. There's a lot of scrutiny put on the auditability of what you put into production, something that's making decisions that impact on whether or not somebody gets a loan or whether or not somebody is granted access to systems or decisions that are made. And so that the connection there is really around taking advantage of what has proven itself in kubernetes to be a very effective development model and applying that to AI/ML and getting the benefits in being able to put these things into production. >> Dave: So, so Red Hat has been involved in enterprises for a long time. Are you seeing most of this from a Kubernetes perspective, being net new application environments or are these extensions of what we would call legacy or traditional environments. >> They tend to be net new, I guess, you know, it's, it's sort of, it's transitioned a little bit over time. When we first started talking to customers, there was desire to try to do all of this in a single Kubernetes cluster, right? How can I take the same environment that had been doing our, our software development, beef it up a little bit and have it applied to our data science environment. And over time, like Kubernetes advanced rights. So now you can actually add labels to different nodes and target workloads based on specialized machinery and hardware accelerators. And so that has shifted now toward coming up with specialized data science environments, but still connecting the clusters in that's something that's being built on that data science environment is essentially being deployed then through, through a model pipeline, into a software artifact that then makes its way into an application that that goes live. And, and really, I think that that's sensible, right? Because we're constantly seeing a lot of evolution in, in the types of accelerators, the types of frameworks, the types of libraries that are being made available to data scientists. And so you want the ability to extend your data science cluster to take advantage of those things and to give data scientists access to that those specialized environments. So they can try things out, determine if there's a better way to, to do what they're doing. And then when they find out there is, be able to rapidly roll that into your production environment. >> You mentioned the word acceleration, and that's one of the words that we talk about when we talk about 2020, and even 2021, the acceleration in digital transformation that was necessary really a year and a half ago, for companies to survive. And now to be able to pivot and thrive. What are you seeing in terms of customers appetites for, for adopting AI/ML based solutions? Has it accelerated as the pandemic has accelerated digital transformation. >> It's definitely accelerated. And I think, you know, the pandemic probably put more of a focus for businesses on where can they start to drive more value? How can they start to do more with less? And when you look at systems that are used for customer interactions, whether they're deflecting customer cases or providing next best action type recommendations, AI/ML fits the bill there perfectly. So when they were looking to optimize, Hey, where do we put our spend? What can help us accelerate and grow? Even in this virtual world we're living in, AI/ML really floated to the top there, that's definitely a theme that we've seen. >> Lisa: Is there a customer example that you think that you could mention that really articulates the value over that? >> You know, I think a lot of it, you know, we've published one specifically around HCA health care, and this had started actually before the pandemic, but I think especially, it's applicable because of the nature of what a pandemic is, where HCA was using AI/ML to essentially accelerate diagnosis of sepsis, right. They were using it for, for disease diagnoses. That same type of, of diagnosis was being applied to looking at COVID cases as well. And so there was one that we did in Canada with, it's called 'how's your flattening', which was basically being able to track and do some predictions around COVID cases in the Canadian provinces. And so that one's particularly, I guess, kind of close to home, given the nature of the pandemic, but even within Red Hat, we started applying a lot more attention to how we could help with customer support cases, right. Knowing that if folks were going to be out with any type of illness. We needed to be able to be able to handle that case, you know, workload without negatively impacting work-life balance for, for other associates. So we looked at how can we apply AI/ML to help, you know, maintain and increase the quality of customer service we were providing. >> it's a great use case. Did you have a keynote or a session, here at KubeCon CloudNative? >> I did. I did. And it really focused specifically on that whole ML ops and model ops pipeline. It was called involving Kubernetes and bracing model ops. It was for a Kubernetes AI day. I believe it aired on Wednesday of this week. Tuesday, maybe. It all kind of condenses in the virtual world. >> Doesn't it? It does. >> So one of the questions that Lisa and I have for folks where we sit here, I don't know, was it year seven or so of the Dawn of Kubernetes, if I have that, right. Where do you think we are, in this, in this wave of adoption, coming from a Red Hat perspective, you have insight into, what's been going on in enterprises for the last 20 plus years. Where are we in this wave? >> That's a great question. Every time, like you, it's sort of that cresting wave sort of, of analogy, right? That when you get to top one wave, you notice the next wave it's even bigger. I think we've certainly gotten to the point where, where organizations have accepted that Kubernetes can, is applicable across all the workloads that they're looking to put in production. Now, the focus has shifted on optimizing those workloads, right? So what are the things that we need to run in our in-house data centers? What are things that we need, or can benefit from using commodity hardware from one of the hyperscalers, how do we connect those environments and more effectively target workloads? So if I look at where things are going to the future, right now, we see a lot of things being targeted based on cluster, right? We say, Hey, we have a data science cluster. It has characteristics because of X, Y, and Z. And we put all of our data science workloads into that cluster. In the future, I think we want to see more workload specific, type of categorization of workloads so that we're able to match available hardware with workloads rather than targeting a workload at a specific cluster. So a developer or data scientist can say, Hey, my particular algorithm here needs access to GPU acceleration and the following frameworks. And then it, the Kubernetes scheduler is able to determine of the available environments. What's the capacity, what are the available resources and match it up accordingly. So we get into a more dynamic environment where the developers and those that are actually building on top of these platforms actually have to know less and less about the clusters they're running on. It just have to know what types of resources they need access to. >> Lisa: So sort of democratizing that. Steve, thank you for joining Dave and me on the program tonight, talking about the traction that you're seeing with AI/ML, Kubernetes as an enabler, we appreciate your time. >> Thank you. >> Thanks Steve. >> For Dave Nicholson. I'm Lisa Martin. You're watching theCube live from Los Angeles KubeCon and CloudNativeCon 21. We'll be right back with our next guest. (subtle music playing) >> Lisa: I have been in the software and technology industry for over 12 years now. So I've had the opportunity as a marketer to really understand and interact with customers across the entire buyer's journey. Hi, I'm Lisa Martin and I'm a host of theCube. Being a host on the cube has been a dream of mine for the last few years. I had the opportunity to meet Jeff and Dave and John at EMC World a few years ago and got the courage up to say, Hey, I'm really interested in this. I love talking with customers...

>>Welcome to the cubes coverage of VMworld 2021. I'm Lisa Martin, two guests joining me next. Matt Morgan is here. Vice-president cloud infrastructure business group at VMware and Steven Jones joins us as well. Director of services at AWS gentlemen. That's great to have you on the program. >>Thank you, Lisa. >>Glad to see everyone's doing well. Here we are virtual. So we are just around the four year anniversary of VMware cloud on AWS. Can't believe it's been 20 17, 4 years. Matt talked to us about VMware AWS partnership and how it's progressed over that time. >>The partnership has been fantastic and it's evolved. We announced VM-ware cloud on AWS general availability all the way back at VMworld, 2017, we've been releasing new features and capabilities every other week with 16 major platform releases and 300 features as customers have requested. So it's been an incredible co-engineering relationship with AWS. We've also expanded our go to market by announcing a resale program in which AWS can resell VMware cloud on AWS. We did that back in 2019 and in 2020, we've announced that AWS is VMware's preferred public cloud partner for vSphere based workloads. And VMware is AWS's preferred service for vSphere based workloads. >>So as you said, Matt, a tremendous amount of evolution and just a short four year timeframe. Stephen talked to me about the partnership through AWS, this lens. >>Yeah. You bet. Look, I agree with Matt that the partnership has been fantastic and it's just amazing to see how fast four years has gone. I really think that AWS and VMware really are a really good example of how two technology companies can work together for them. The benefit of our mutual customers, um, as Matt indicated, VM-ware is our preferred service for vSphere based workloads. And we're broadly working together as a single team across both engineering and go-to-market functions to help customers drive business value from the, the, the investments they made over the years. And then also as they work to transform their businesses into the future with cloud technology, >>Let's talk about digital transformation. That is a term we've been, we've been talking about that for many years on this program. And at every event we've all been at, right. What we've seen in the last year and a half is a massive acceleration. Now talk to me about how VMware and AWS are helping customers facilitate that digital transformation. >>So our customers see modern it infrastructure as the core pillar of a digital transformation strategy and public cloud has been a digital transformation enabler for organizations. And that's because they have so many benefits when they embraced the public cloud, including the ability to elastically consume infrastructure. That's required the ability to employ a pay as you go financial model and the ability to reduce operational overhead, which helps save both monetary costs, but also provides more flexibility. But the big driver now is the ability to embrace innovative cloud services and those services help accelerate application development, deployment and management VMware cloud on AWS is a prime example of such an offering, which not only provides these benefits, but enhances them with operational consistency working the same way their it architecture works today, giving them familiarity and enterprise robustness that VMware technologies are known for, but being able to maximize the power of the global AWS cloud >>And every year from a customer adoption perspective, that's doubling Steven walked through a couple of customer examples that really highlight the value of VMC on AWS. >>Yeah, I've got a couple here. I think, uh, Kiko Milano is a good one. There a then our Italian company, they sell cosmetics and beauty products through about 900 retail stores in 27 different markets. So quite large, but they found that their on premises data center and outsourcing partner was just too inflexible for the changing needs of their company. And within four months, uh, Kiko actually migrated all of their core workloads to Amazon. Is he too, and particularly surprised how easy it was to migrate over 300 servers to the VMware cloud on AWS offering. And this is, this is key because the actually leveraging the same platform that they were used to, which was BMR. Uh, the Kiko team actually didn't have to perform any testing or modify any other existing applications. They also, they didn't have to actually train their teams again, because again, they were already up-skilled with being able to leverage the BMR technology. >>So again, we think it's the best of both worlds customers like Kiko can come and use VMware cloud on AWS, consolidate their server footprint and also take advantage of, of a hyperscale platform. That's pretty cool. Another customer, uh, SAP global ratings that our company provides a high quality market intelligence in the form of credit ratings, research, and thought leadership to help educate market participants to make better financial decisions who doesn't want to make a better financial decision. Right? So in order to accelerate their business growth and globalization really meet new business capabilities, they knew they needed to move a hundred percent to the cloud and wanted to know how they're actually going to do that. Now they also have an aging data center system outages, which are becoming more frequent, which to them actually concerned that they actually might, um, uh, face in the future, some penalties from the sec. >>So they didn't want to do that. So over the period of about eight months, think about this eight months, they moved to 150 financial apps to AWS leveraging VMware on AWS. Uh, pretty impressive. They reduce technical debt, uh, from legacy systems that were hosted on sun Solaris, Oracle excavator, and a X. And then now actually able to meet the goal demands of their business. The fun part here is they're actually meeting their uptime, uh, needs a hundred percent of the time since it actually moves these workloads to the VMware cloud on AWS. So pretty exciting. See customers link this kind of journey, >>Absolutely impressive journeys. Also short time periods to do a massive change there. It sounds like the familiarity with VMware in the console is a huge facilitator of the speed of migration and folks being able to get up and running. Stephen talked to me about some of the trends that you were seeing in organizations like the customers that you just mentioned. >>Yeah. So there are some emergency transfer store and a lot of customers want to leverage the same cloud operating models, but also in their own data centers. So they can take advantage of agility and innovation of cloud will also meeting requirements that they sometimes have that keep them from adopting cloud. Uh, you can think of workloads that sometimes have low latency requirements, right? Or they need to process large volumes of data locally. Uh, other times customers tell us they really need the flexibility to run data workloads, um, in a particular area that has data sovereignty or residency requirements. So when, as we talk about customers, um, they tell us that not only do they want to minimize their, their need to actually manage and operate infrastructure, um, and focus on business innovation is sometimes need to do this, um, in a, in a data center this close to them, if that makes sense. So they're looking for the best again of both worlds. >>Got it. The best of both worlds and Matt, you have some breaking news to share. What is it? >>So today we're announcing the general availability of VMware cloud on AWS outposts. >>Awesome. Congratulations. Tell me about that. Let's dig into it. >>So for customers looking to extend their AWS centric model to an on-premise location, that data center edge location via more cloud on AWS, outposts delivers the agility and innovation of AWS cloud, but on premises and VMware cloud on AWS outpost is based on VMware cloud, a jointly engineered service. So together we're delivering this service on premises as a service. This gives us the capability to integrate VMware's enterprise class architecture and platform with next generation dedicated Amazon nitro based ECE to bare metal instances. It provides a deeply integrated hybrid cloud operating environment that extends from a customer's data center to these particular services running on premises in the data center, the edge, or to the public cloud and having a unified control plane between all of it. >>A unified control plan is absolutely critical. Uh, Stephen eight, >>We have a detailed plan to offer integrated AWS services, and that capability really enhances the innovation angle for customers as they embraced the modernization of their applications. >>Another great example of how deep the partnership is Steven AWS outpost was announced at reinvent, I think 2019, which was the last time I was at an event in person. So coming up on a couple of years here, when GA talked to me about some of the key use cases that you're seeing, where it really excels. >>Yeah. So Matt, Matt highlighted a number of these, right. And you're right. It was 2019. Uh, we were all together back then and hopefully we can do that, uh, very soon here, um, quickly on apple. So overall, since, since we're talking about outposts, uh, VMware cloud on a post as well. So the thing here and Matt highlighted this is that without posts, we actually live we've leveraged, leveraged literally the same hardware and control plane technology that we leverage in our own data centers so that the customers will come to know and love and expect about the AWS platform and VMC on AWS, uh, uh, is, is, is the exact same thing that we'll be able to get with the Apple's technology. I'll give you a couple of customer examples. I think that that actually speaks to the use cases best. So, um, you remember, I talked a little bit about data locality and residency requirements. >>So first ABI Dhabi bank, uh, is the largest bank in the United Arab Emirates, right? And they were offering corporate investment and personal banking service, and they wanted to deliver a digital banking service, including email and mobile payments, but they had to follow a specific residency and data retention requirements and they had to do it in the UAE. And so what they've done is they've actually leveraged multiple AWS outposts in the UAE to allow them to provide business continuity while also leveraging the same API APIs that they had to come to know about, uh, and love about the AWS services in region, right? Phillips healthcare is another really good example. Um, you can imagine that, uh, what they do every day is, is, uh, very important things like predictive analytics for preventative treatments. And so outposts Phillips has actually taken those and that developed cloud applications, again, deployed on the same infrastructure they were used to within region. Now they can actually do this in clinics at hospitals, and they're in managing that the same tools providing, uh, same end-to-end, um, view and to their own providers, 19 administrators. And so they actually estimate they have over 70,000 servers now distributed across 12,000 locations or 1200 locations. Excuse me. So that's an example of, again, just two use cases that really broadened the reach and the flexibility of customers to run workloads in the cloud, but in a on-premise fashion. Does that make sense? >>Yes, it does. And you mentioned two great stories there. One in financial services, the other one healthcare, two industries that have had to massively pivot in the last 18 months amongst many others, but let's talk a little bit more Steven, about some of the things that you're hearing from some of the early customers of BMC on outpost. What are some of the near term opportunities that you're uncovering? >>Yeah, I've got to say here too, that, uh, customers are VMware customers have been asking us for this for quite some time. I'm sure Matt would agree. Um, so look from, uh, go back to some of the use cases we've discussed low latency compute requirements. So one of our higher education customers today who has migrated workloads to be more cloud on AWS, um, is looking at, uh, extending the same capability to an on-premise experience specifically for, um, uh, school applications that require a low latency, um, uh, integration, um, from a local data processing perspective. Again, one of our VMware on AWS top biopharmaceutical companies, uh, here again in the U S um, is planning to use VMware cloud on AWS outposts for health management applications with patient records that need to be retained locally at the hospital hospital sites. And then finally you can kind of going back to the story around data residency. We have a large telco provider in Europe that is planning to use this particular offering for their applications that need to remain on premises to meet regulatory requirements. So again, you know, we're just super pleased with the amount of interest, not only in VMware cloud on AWS, but also in this new run that we're announcing today. And we're really excited to be able to support the VMware cloud experience really on the AWS Apple's platform for a of these use cases. >>One of the things we've talked about for many years with both VMware and AWS is the dedication to listening to the voice of the customer. Not obviously this is a great example, Steven, as you said, VMware customers have been asking for this for awhile. So while customers have a ton of choice, I want you guys to unpack what the differentiators are of this service. And Matt, if we can start with you to bring you back into the conversation, we'd love to get your, your input on those differentiators. >>Yeah, absolutely. So people have to look at this for the service that's delivered and on the VMware side of the equation, we're delivering the full VMware cloud infrastructure capability. This is delivered as a service as a cloud service on premises. So why is this valuable? Well, it relieves the it burden of infrastructure management and fully maximizes the value of a fully managed cloud service, giving an organization, the capability to unlock the renovation, budgets, and start to invest truly an innovation. This is all about continuous life cycle management, ongoing service monitoring, automated processes to ensure the health and security the infrastructure. And of course, this is backed by expert VMware site recovery and reliability engineers, to ensure that everything works perfectly. We also enable organizations to leverage best in class enterprise grade capabilities that we've talked about in our compute storage and networking for best-in-class resiliency auto-scaling and intrinsic availability. >>So there's no long procurement cycles to set up these environments. And that means it's developer ready right out of the box. We're also deeply integrated with what customers do today. So end to end hybrid cloud usually requires end-to-end hybrid processes. And with this integration into those processes is instant, no reconfiguration, no conversion, no refactoring, no rearchitecture of existing applications using VMware HDX or B motion organizations can move applications to leverage this cloud service instantly. It allows you to use established on premises governance, security, and operational policies, and ensures that that workload portability I mentioned goes both ways. It's bi-directional as customers need to have portability to meet their business requirements. As we mentioned earlier, there's a unified hybrid control plane with a single pane of glass to manage resources across the end-to-end hybrid cloud environment. And we're giving direct access to 200 plus native AWS services. And that enables an organization to truly modernize their applications, starting where they are today. And so that gives you the real capability to deliver a unique service. One that gives you an organization, the ability to migrate without any downtime have fast, fast cost effective capabilities and a low risk to their hybrid cloud strategy. >>Excellent. That's a pretty jam packed list of differentiators there, but one of the things that it really sounds like not from what you said is how much work has gone on to make the transition smooth for customers, give them that flexibility and that portability that they need. Those are marketing terms you and I know are used very frequently, but it really seems like the work that you've done here will be done straight to that. I want to ask you Stephen, that same question from AWS's perspective, what really differentiates the solution. >>It is a good question. I'll just, uh, I'll agree that there has been a ton of work first that is, has gone, gone into actually making this happen. Right. Um, and to, to all the points that Matt made. And I would just add that again. 80 was outpost is built on the same AWS nitro system and infrastructure. The customers have already come to love in the cloud. And so gone really are the days where customers have to worry about procuring and racking and stacking their own gear layer on all the benefits, the map outline from a VMware perspective. And again, we, we really believe the customers are getting the best of both worlds here. Um, with, with specifically with the compute that comes in the outpost rack, um, customers actually get getting kind of built in redundancy and resiliency, hard security, all those things that customers don't know, they need certain things. >>The customers know they need to pay attention to, but also want some help with. And so we've, we, we put a lot of thought and effort into this. Um, but could I just, uh, explain a little bit about the customer experience, um, when a customer orders and AWS outposts rack, right? AWS actually signs up, uh, to do a fully managed experience here. Like we'll bring people in to actually do site assessments. Um, we'll manage the hardware, setup, the installation and the maintenance of that gear over time. Well, VM-ware also manages the, the software defined data center construct as well as, um, the, the single point for, uh, for support questions. And so together, we really thought through how customers is met, but it get an end to end experience from hardware all the way up through application modernization. It's pretty exciting, >>Very deep partnership there. And we're out of time, but I do want to ask you guys, where can customers go, who are interested in learning more about this new service? >>So at VM world, there are a collection of DMR cloud, AWS sessions, including sessions, dedicated to VMware cloud on AWS outpost. We encourage everyone who's attending VMworld to look up those sessions and you'll learn all about the hardware, the service, the capabilities, the procurement, and how to get started. In addition, on vmware.com, we have a web portal for you to gain additional knowledge through a digital consumption. That's vmware.com/vmc-outposts. >>Awesome. Matt, thank you. I'm sure folks will be just drinking up all of this information at the sessions at VMworld 2021. And I hope to see you in person at next year's VM. I'm crossing my fingers. Great to see you guys Format Morgan and Steve Jones. I'm Lisa Martin, and you're watching the cubes coverage of the em world to 2021.

>> Voiceover: From theCUBE's Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is theCUBE Conversation. >> Welcome back to theCUBE's coverage of the AWS Startup Showcase. Next Big Thing in AI, Security and Life Sciences featuring Ahana for the AI Trek. I'm your host, John Furrier. Today, we're joined by two great guests, Steven Mih, Ahana CEO, and Sachin Nayyar, Securonix CEO. Gentlemen, thanks for coming on theCUBE. We're talking about the Next-Gen technologies on AI, Open Data Lakes, et cetera. Thanks for coming on. >> Thanks for having us, John. >> Thanks, John. >> What a great line up here. >> Sachin: Thanks, Steven. >> Great, great stuff. Sachin, let's get in and talk about your company, Securonix. What do you guys do? Take us through, I know you've got a slide to help us through this, I want to introduce your stuff first then jump in with Steven. >> Absolutely. Thanks again, Steven. Ahana team for having us on the show. So Securonix, we started the company in 2010. We are the leader in security analytics and response capability for the cybermarket. So basically, this is a category of solutions called SIEM, Security Incident and Event Management. We are the quadrant leaders in Gartner, we now have about 500 customers today and have been plugging away since 2010. Started the company just really focused on analytics using machine learning and an advanced analytics to really find the needle in the haystack, then moved from there to needle in the needle stack using more algorithms, analysis of analysis. And then kind of, I evolved the company to run on cloud and become sort of the biggest security data lake on cloud and provide all the analytics to help companies with their insider threat, cyber threat, cloud solutions, application threats, emerging internally and externally, and then response and have a great partnership with Ahana as well as with AWS. So looking forward to this session, thank you. >> Awesome. I can't wait to hear the news on that Next-Gen SIEM leadership. Steven, Ahana, talk about what's going on with you guys, give us the update, a lot of stuff happening. >> Yeah. Great to be here and thanks for that such, and we appreciate the partnership as well with both Securonix and AWS. Ahana is the open source company based on PrestoDB, which is a project that came out of Facebook and is widely used, one of the fastest growing projects in data analytics today. And we make a managed service for Presto easily on AWS, all cloud native. And we'll be talking about that more during the show. Really excited to be here. We believe in open source. We believe in all the challenges of having data in the cloud and making it easy to use. So thanks for having us again. >> And looking forward to digging into that managed service and why that's been so successful. Looking forward to that. Let's get into the Securonix Next-Gen SIEM leadership first. Let's share the journey towards what you guys are doing here. As the Open Data Lakes on AWS has been a hot topic, the success of data in the cloud, no doubt is on everyone's mind especially with the edge coming. It's just, I mean, just incredible growth. Take us through Sachin, what do you guys got going on? >> Absolutely. Thanks, John. We are hearing about cyber threats every day. No question about it. So in the past, what was happening is companies, what we have done as enterprise is put all of our eggs in the basket of solutions that were evaluating the network data. With cloud, obviously there is no more network data. Now we have moved into focusing on EDR, right thing to do on endpoint detection. But with that, we also need security analytics across on-premise and cloud. And your other solutions like your OT, IOT, your mobile, bringing it all together into a security data lake and then running purpose built analytics on top of that, and then having a response so we can prevent some of these things from happening or detect them in real time versus innovating for hours or weeks and months, which is is obviously too late. So with some of the recent events happening around colonial and others, we all know cybersecurity is on top of everybody's mind. First and foremost, I also want to. >> Steven: (indistinct) slide one and that's all based off on top of the data lake, right? >> Sachin: Yes, absolutely. Absolutely. So before we go into on Securonix, I also want to congratulate everything going on with the new cyber initiatives with our government and just really excited to see some of the things that the government is also doing in this space to bring, to have stronger regulation and bring together the government and the private sector. From a Securonix perspective, today, we have one third of the fortune 500 companies using our technology. In addition, there are hundreds of small and medium sized companies that rely on Securonix for their cyber protection. So what we do is, again, we are running the solution on cloud, and that is very important. It is not just important for hosting, but in the space of cybersecurity, you need to have a solution, which is not, so where we can update the threat models and we can use the intelligence or the Intel that we gather from our customers, partners, and industry experts and roll it out to our customers within seconds and minutes, because the game is real time in cybersecurity. And that you can only do in cloud where you have the complete telemetry and access to these environments. When we go on-premise traditionally, what you will see is customers are even thinking about pushing the threat models through their standard Dev test life cycle management, and which is just completely defeating the purpose. So in any event, Securonix on the cloud brings together all the data, then runs purpose-built analytics on it. Helps you find very few, we are today pulling in several million events per second from our customers, and we provide just a very small handful of events and reduce the false positives so that people can focus on them. Their security command center can focus on that and then configure response actions on top of that. So we can take action for known issues and have intelligence in all the layers. So that's kind of what the Securonix is focused on. >> Steven, he just brought up, probably the most important story in technology right now. That's ransomware more than, first of all, cybersecurity in general, but ransomware, he mentioned some of the government efforts. Some are saying that the ransomware marketplace is bigger than some governments, nation state governments. There's a business model behind it. It's highly active. It's dominating the scene and it's a real threat. This is the new world we're living in, cloud creates the refactoring capabilities. We're hearing that story here with Securonix. How does Presto and Securonix work together? Because I'm connecting the dots here in real time. I think you're going to go there. So take us through because this is like the most important topic happening. >> Yeah. So as Sachin said, there's all this data that needs to go into the cloud and it's all moving to the cloud. And there's a massive amounts of data and hundreds of terabytes, petabytes of data that's moving into the data lakes and that's the S3-based data lakes, which are the easiest, cheapest, commodified place to put all this data. But in order to deliver the results that Sachin's company is driving, which is intelligence on when there's a ransomware or possibility, you need to have analytics on them. And so Presto is the open source project that is a open source SQL query engine for data lakes and other data sources. It was created by Facebook as part of the Linux foundation, something called Presto foundation. And it was built to replace the complicated Hadoop stack in order to then drive analytics at very lightning fast queries on large, large sets of data. And so Presto fits in with this Open Data Lake analytics movement, which has made Presto one of the fastest growing projects out there. >> What is an Open Data Lake? Real quick for the audience who wants to learn on what it means. Does is it means it's open source in the Linux foundation or open meaning it's open to multiple applications? What does that even mean? >> Yeah. Open Data Lake analytics means that you're, first of all, your data lake has open formats. So it is made up of say something called the ORC or Parquet. And these are formats that any engine can be used against. That's really great, instead of having locked in data types. Data lakes can have all different types of data. It can have unstructured, semi-structured data. It's not just the structured data, which is typically in your data warehouses. There's a lot more data going into the Open Data Lake. And then you can, based on what workload you're looking to get benefit from, the insights come from that, and actually slide two covers this pictorially. If you look on the left here on slide two, the Open Data Lake is where all the data is pulling. And Presto is the layer in between that and the insights which are driven by the visualization, reporting, dashboarding, BI tools or applications like in Securonix case. And so analytics are now being driven by every company for not just industries of security, but it's also for every industry out there, retail, e-commerce, you name it. There's a healthcare, financials, all are looking at driving more analytics for their SaaSified applications as well as for their own internal analysts, data scientists, and folks that are trying to be more data-driven. >> All right. Let's talk about the relationship now with where Presto fits in with Securonix because I get the open data layer. I see value in that. I get also what we're talking about the cloud and being faster with the datasets. So how does, Sachin' Securonix and Ahana fit in together? >> Yeah. Great question. So I'll tell you, we have two customers. I'll give you an example. We have two fortune 10 customers. One has moved most of their operations to the cloud and another customer which is in the process, early stage. The data, the amount of data that we are getting from the customer who's moved fully to the cloud is 20 times, 20 times more than the customer who's in the early stages of moving to the cloud. That is because the ability to add this level of telemetry in the cloud, in this case, it happens to be AWS, Office 365, Salesforce and several other rescalers across several other cloud technologies. But the level of logging that we are able to get the telemetry is unbelievable. So what it does is it allows us to analyze more, protect the customers better, protect them in real time, but there is a cost and scale factor to that. So like I said, when you are trying to pull in billions of events per day from a customer billions of events per day, what the customers are looking for is all of that data goes in, all of data gets enriched so that it makes sense to a normal analyst and all of that data is available for search, sometimes 90 days, sometimes 12 months. And then all of that data is available to be brought back into a searchable format for up to seven years. So think about the amount of data we are dealing with here and we have to provide a solution for this problem at a price that is affordable to the customer and that a medium-sized company as well as a large organization can afford. So after a lot of our analysis on this and again, Securonix is focused on cyber, bringing in the data, analyzing it, so after a lot of our analysis, we zeroed in on S3 as the core bucket where this data needs to be stored because the price point, the reliability, and all the other functions available on top of that. And with that, with S3, we've created a great partnership with AWS as well as with Snowflake that is providing this, from a data lake perspective, a bigger data lake, enterprise data lake perspective. So now for us to be able to provide customers the ability to search that data. So data comes in, we are enriching it. We are putting it in S3 in real time. Now, this is where Presto comes in. In our research, Presto came out as the best search engine to sit on top of S3. The engine is supported by companies like Facebook and Uber, and it is open source. So open source, like you asked the question. So for companies like us, we cannot depend on a very small technology company to offer mission critical capabilities because what if that company gets acquired, et cetera. In the case of open source, we are able to adopt it. We know there is a community behind it and it will be kind of available for us to use and we will be able to contribute in it for the longterm. Number two, from an open source perspective, we have a strong belief that customers own their own data. Traditionally, like Steven used the word locked in, it's a key term, customers have been locked in into proprietary formats in the past and those days are over. You should be, you own the data and you should be able to use it with us and with other systems of choice. So now you get into a data search engine like Presto, which scales independently of the storage. And then when we start looking at Presto, we came across Ahana. So for every open source system, you definitely need a sort of a for-profit company that invests in the community and then that takes the community forward. Because without a company like this, the community will die. So we are very excited about the partnership with Presto and Ahana. And Ahana provides us the ability to take Presto and cloudify it, or make the cloud operations work plus be our conduit to the Ahana community. Help us speed up certain items on the roadmap, help our team contribute to the community as well. And then you have to take a solution like Presto, you have to put it in the cloud, you have to make it scale, you have to put it on Kubernetes. Standard thing that you need to do in today's world to offer it as sort of a micro service into our architecture. So in all of those areas, that's where our partnership is with Ahana and Presto and S3 and we think, this is the search solution for the future. And with something like this, very soon, we will be able to offer our customers 12 months of data, searchable at extremely fast speeds at very reasonable price points and you will own your own data. So it has very significant business benefits for our customers with the technology partnership that we have set up here. So very excited about this. >> Sachin, it's very inspiring, a couple things there. One, decentralize on your own data, having a democratized, that piece is killer. Open source, great point. >> Absolutely. >> Company goes out of business, you don't want to lose the source code or get acquired or whatever. That's a key enabler. And then three, a fast managed service that has a commercial backing behind it. So, a great, and by the way, Snowflake wasn't around a couple of years ago. So like, so this is what we're talking about. This is the cloud scale. Steven, take us home with this point because this is what innovation looks like. Could you share why it's working? What's some of the things that people could walk away with and learn from as the new architecture for the new NextGen cloud is here, so this is a big part of and share how this works? >> That's right. As you heard from Sachin, every company is becoming data-driven and analytics are central to their business. There's more data and it needs to be analyzed at lower cost without the locked in and people want that flexibility. And so a slide three talks about what Ahana cloud for Presto does. It's the best Presto out of the box. It gives you very easy to use for your operations team. So it can be one or two people just managing this and they can get up to speed very quickly in 30 minutes, be up and running. And that jump starts their movement into an Open Data Lake analytics architecture. That architecture is going to be, it is the one that is at Facebook, Uber, Twitter, other large web scale, internet scale companies. And with the amount of data that's occurring, that's now becoming the standard architecture for everyone else in the future. And so just to wrap, we're really excited about making that easy, giving an open source solution because the open source data stack based off of data lake analytics is really happening. >> I got to ask you, you've seen many waves on the industry. Certainly, you've been through the big data waves, Steven. Sachin, you're on the cutting edge and just the cutting edge billions of signals from one client alone is pretty amazing scale and refactoring that value proposition is super important. What's different from 10 years ago when the Hadoop, you mentioned Hadoop earlier, which is RIP, obviously the cloud killed it. We all know that. Everyone kind of knows that. But like, what's different now? I mean, skeptics might say, I don't believe you, but it's just crazy. There's no way it works. S3 costs way too much. Why is this now so much more of an attractive proposition? What do you say the naysayers out there? With Steve, we'll start with you and then Sachin, I want you to like weigh in too. >> Yeah. Well, if you think about the Hadoop era and if you look at slide three, it was a very complicated system that was done mainly on-prem. And you'd have to go and set up a big data team and a rack and stack a bunch of servers and then try to put all this stuff together and candidly, the results and the outcomes of that were very hard to get unless you had the best possible teams and invested a lot of money in this. What you saw in this slide was that, that right hand side which shows the stack. Now you have a separate compute, which is based off of Intel based instances in the cloud. We run the best in that and they're part of the Presto foundation. And that's now data lakes. Now the distributed compute engines are the ones that have become very much easier. So the big difference in what I see is no longer called big data. It's just called data analytics because it's now become commodified as being easy and the bar is much, much lower, so everyone can get the benefit of this across industries, across organizations. I mean, that's good for the world, reduces the security threats, the ransomware, in the case of Securonix and Sachin here. But every company can benefit from this. >> Sachin, this is really as an example in my mind and you can comment too on if you'd believe or not, but replatform with the cloud, that's a no brainer. People do that. They did it. But the value is refactoring in the cloud. It's thinking differently with the assets you have and making sure you're using the right pieces. I mean, there's no brainer, you know it's good. If it costs more money to stand up something than to like get value out of something that's operating at scale, much easier equation. What's your thoughts on this? Go back 10 years and where we are now, what's different? I mean, replatforming, refactoring, all kinds of happening. What's your take on all this? >> Agreed, John. So we have been in business now for about 10 to 11 years. And when we started my hair was all black. Okay. >> John: You're so silly. >> Okay. So this, everything has happened here is the transition from Hadoop to cloud. Okay. This is what the result has been. So people can see it for themselves. So when we started off with deep partnerships with the Hadoop providers and again, Hadoop is the foundation, which has now become EMR and everything else that AWS and other companies have picked up. But when you start with some basic premise, first, the racking and stacking of hardware, companies having to project their entire data volume upfront, bringing the servers and have 50, 100, 500 servers sitting in their data centers. And then when there are spikes in data, or like I said, as you move to the cloud, your data volume will increase between five to 20x and projecting for that. And then think about the agility that it will take you three to six months to bring in new servers and then bring them into the architecture. So big issue. Number two big issue is that the backend of that was built for HDFS. So Hadoop in my mind was built to ingest large amounts of data in batches and then perform some spark jobs on it, some analytics. But we are talking in security about real time, high velocity, high variety data, which has to be available in real time. It wasn't built for that, to be honest. So what was happening is, again, even if you look at the Hadoop companies today as they have kind of figured, kind of define their next generation, they have moved from HDFS to now kind of a cloud based platform capability and have discarded the traditional HDFS architecture because it just wasn't scaling, wasn't searching fast enough, wasn't searching fast enough for hundreds of analysts at the same time. And then obviously, the servers, et cetera wasn't working. Then when we worked with the Hadoop companies, they were always two to three versions behind for the individual services that they had brought together. And again, when you're talking about this kind of a volume, you need to be on the cutting edge always of the technologies underneath that. So even while we were working with them, we had to support our own versions of Kafka, Solr, Zookeeper, et cetera to really bring it together and provide our customers this capability. So now when we have moved to the cloud with solutions like EMR behind us, AWS has invested in in solutions like EMR to make them scalable, to have scale and then scale out, which traditional Hadoop did not provide because they missed the cloud wave. And then on top of that, again, rather than throwing data in that traditional older HDFS format, we are now taking the same format, the parquet format that it supports, putting it in S3 and now making it available and using all the capabilities like you said, the refactoring of that is critical. That rather than on-prem having servers and redundancies with S3, we get built in redundancy. We get built in life cycle management, high degree of confidence data reliability. And then we get all this innovation from companies like, from groups like Presto, companies like Ahana sitting on double that S3. And the last item I would say is in the cloud we are now able to offer multiple, have multiple resilient options on our side. So for example, with us, we still have some premium searching going on with solutions like Solr and Elasticsearch, then you have Presto and Ahana providing majority of our searching, but we still have Athena as a backup in case something goes down in the architecture. Our queries will spin back up to Athena, AWS service on Presto and customers will still get served. So all of these options, but what it doesn't cost us anything, Athena, if we don't use it, but all of these options are not available on-prem. So in my mind, I mean, it's a whole new world we are living in. It is a world where now we have made it possible for companies to even enterprises to even think about having true security data lakes, which are useful and having real-time analytics. From my perspective, I don't even sign up today for a large enterprise that wants to build a data lake on-prem because I know that is not, that is going to be a very difficult project to make it successful. So we've come a long way and there are several details around this that we've kind of endured through the process, but very excited where we are today. >> Well, we certainly follow up with theCUBE on all your your endeavors. Quickly on Ahana, why them, why their solution? In your words, what would be the advice you'd give me if I'm like, okay, I'm looking at this, why do I want to use it, and what's your experience? >> Right. So the standard SQL query engine for data lake analytics, more and more people have more data, want to have something that's based on open source, based on open formats, gives you that flexibility, pay as you go. You only pay for what you use. And so it proved to be the best option for Securonix to create a self-service system that has all the speed and performance and scalability that they need, which is based off of the innovation from the large companies like Facebook, Uber, Twitter. They've all invested heavily. We contribute to the open source project. It's a vibrant community. We encourage people to join the community and even Securonix, we'll be having engineers that are contributing to the project as well. I think, is that right Sachin? Maybe you could share a little bit about your thoughts on being part of the community. >> Yeah. So also why we chose Ahana, like John said. The first reason is you see Steven is always smiling. Okay. >> That's for sure. >> That is very important. I mean, jokes apart, you need a great partner. You need a great partner. You need a partner with a great attitude because this is not a sprint, this is a marathon. So the Ahana founders, Steven, the whole team, they're world-class, they're world-class. The depth that the CTO has, his experience, the depth that Dipti has, who's running the cloud solution. These guys are world-class. They are very involved in the community. We evaluated them from a community perspective. They are very involved. They have the depth of really commercializing an open source solution without making it too commercial. The right balance, where the founding companies like Facebook and Uber, and hopefully Securonix in the future as we contribute more and more will have our say and they act like the right stewards in this journey and then contribute as well. So and then they have chosen the right niche rather than taking portions of the product and making it proprietary. They have put in the effort towards the cloud infrastructure of making that product available easily on the cloud. So I think it's sort of a no-brainer from our side. Once we chose Presto, Ahana was the no-brainer and just the partnership so far has been very exciting and I'm looking forward to great things together. >> Likewise Sachin, thanks so much for that. And we've only found your team, you're world-class as well, and working together and we look forward to working in the community also in the Presto foundation. So thanks for that. >> Guys, great partnership. Great insight and really, this is a great example of cloud scale, cloud value proposition as it unlocks new benefits. Open source, managed services, refactoring the opportunities to create more value. Stephen, Sachin, thank you so much for sharing your story here on open data lakes. Can open always wins in my mind. This is theCUBE we're always open and we're showcasing all the hot startups coming out of the AWS ecosystem for the AWS Startup Showcase. I'm John Furrier, your host. Thanks for watching. (bright music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Worldwide Public sector. >>Welcome to the Cube. Virtual. This is our coverage of aws reinvent 2020. Specialized programming for worldwide public sector. I'm Lisa Martin. Got a couple of guests here from No. Veta, please welcome Steven Adelman, principal computer scientists, and Kevin Healed, vice president of Information Exploitation. Gentlemen, welcome to the Cube. >>Thank you. >>Thank you for having us. >>Alright, guys. So? So, Kevin, we're going to start with you. Give our audience an introduction to Nevada. What do you What do you guys do? Who are you? How do you play in the public sector Government space, >>right? Yeah. Thank you, Lisa. Eso, Nevada Nevada is a technology services company focused on government solutions. So primarily national security solutions. Eso think customers such as Doody, the intelligence community, FBI, law enforcement and things like that about 13 1300 employees worldwide, primarily in our in our field. Clear resource is, um, that really focused on cloud for solutions for our customers. So solving the tough mission challenges our customers have, so that could be in technology solutions such as Data Analytics A I M L i O T. Secure Workloads, full spectrum cyber Cobb video processing. Really anything that's a high end technology solution or something we do for the government. We have been a privilege. We have. It's a privilege to be a partner with AWS for for some time now. In fact, I think the first reinvent we may have been to Stephen was six years ago. Five years ago, two >>1012 or 13 >>s So we've we've we've been around for a while, really kind of enjoying it and certainly sad that we're missing an in person reinvent this year, but looking forward to doing it virtually so, we're actually advanced your partner with AWS with a machine learning and government competency. Andi really kind of thio pump the m l side of that. That was one of our first companies with compasses with AWS and led by a center of excellence that I have in my division that really focuses on machine learning and how we applied for the Michigan. And so, um, really, we focus on protecting the nation and protecting our activities in the country >>and on behalf of the country. We thank you, Steven. Give me a little bit of information from a double click perspective as computer scientists. What are some of the key challenges that no, that helps its customers to solve. And how do you do that with a W s? >>Yeah, Thank you. So really as, ah, company, that is is data first. So our initial love and and still are kind of strongest competency is in applying solutions to large data sets. And as you can imagine, uh, the bigger the data set them or compute you need the the more resource is you need and the flexibility from those resource is is truly important, which led us very early, as especially in the government space and public sector space to be in early. A doctor of cloud resource is because of the fact that, you know, rather than standing up a 200 node cluster at at many millions of dollars, we could we could spend up a W s resource is process a big data set, and then and then get the answers an analyst or on operator needed and then spin down. Those resource is when When when that kind of compute wasn't needed. And that is really, uh, kind of informed how we do our work Azaz Nevadans that that cloud infrastructure and now pushing into the edge compute space. Still kind of keeping those cloud best practices in play to get access to more data. That the two, the two biggest, I think revolutions that we've seen with regards to using data to inform business processes and missions has been that that cloud resource that allows us to do so much with so less and so much more flexibly and then the idea of cheap compute making it to the edge and the ability to apply sensors thio places where you know it would been a would have been, you know, operational cost prohibitive to do that and then, ironically, those air to things that aren't necessarily data analytics or machine learning focused but man, did they make it easier to collect that data and process that data and then get the answers back out. So that really has has has kind of, uh, shaped a lot of the way Nevada has grown as a company and how we serve our customers. >>So coming back over to you lets. One of the things that we've been talking about almost all year is just the acceleration in digital transformation and how much faster organizations, private sector, public sector need to innovate to stay relevant, to stay competitive. How do you are you working with government customers to help them innovate so quickly? >>You know, we're very fortunate that a set of customers that focuses actually innovation it's focuses. I rad on. Do you know we can't do the cool things we do without those customer relationships that really encourage us to, um, to try new things out and, quite frankly, fail quickly when we need Thio. And so, by establishing that relationship, what we've been able to do is to blend agile development. Actual acquisition with government requirements process, right? If if you know the typical stereotype of government work is it's this very stovepiped hard core acquisition process, right? And so we have been fortunate to instead try quick win kind of projects. And so one of the biggest things we do is partner with our government customers and try to find it difficult, um, challenged to solve over 6 to 12 month time, right? So instead of making this long four or five year acquisition cycles like show me, right. How can we solve this problem? And then we partner with the mission partner show success in six months show that we can do it with a smaller part of money, and then as we're able to actually make that happen, it expands in something bigger, broader, and then we kind of bringing together a coalition of the willing, if you will in the government and saying, Okay, are there other stakeholders to care about this problem, bring them on, bring their problems and bringing together? You know, we can't do that with some of the passionate people we have, like Stevens. A perfect example. When we talk about a car in the projects we're doing here, Stevens passion for this technology partner with our customers having these challenges and try to enhance what they're doing is a powerful combination. And then the last thing that we're able to is a company is we actually spend a decent amount of our own dollar dollars on I rad S O. R and D that we fund ourselves. And so, while finding those problems and spending government dollars in doing that. We also have spent our own dollars on machine learning Coyote sensor next Gen five g and things like that and how those compartment together partner together to go back to the government. >>Yeah, yeah, So I would even say, You know, there's this. There's a conventional wisdom that government is slow in plotting and a little bit behind commercial best practices. But there are There are pockets in growing pockets across the government, Um, where they're really they're really jumping ahead of, ah, lot of processes and getting in front of this curve and actually are quite innovative. And and because they kind of started off from behind, they could jump over a lot of kind of middle ground legacy technologies. And they're really innovating. As Kevin said with With With the card platform, we're partnering with um P E O Digital in the Air Force in South C, D. M and Air Force security forces as that kind of trifecta of stakeholders who all want toe kind of saw a mission problem and wanted to move forward quickly and leave the legacy behind and and really take a quantum leap forward. And if anything, they're they're driving us Thio, Innovate Mawr Thio Introduce more of those kind of modern back practices on bond. Nevada as a company loves to find those spots in the government sector where we've got those great partners who love what we're doing. And it's this great feedback loop where, um, where we can solve hard technical problems but then see them deployed to some really important and really cool and impactful missions. And we tend to recruit that that set that kind of nexus of people who want to both solve a really difficult problem but want to see it executed in a really impactful way as well. I mean, that really grates a great bond for us, and and I'm really excited to say that that a lot of the government it is really taking a move forward in this this this realm. And I think it's it's just good for our country and good for the missions that they support. >>Absolutely. And it's also surprising because, as you both said, you know, there is this expectation that government processes or lengthy, you know, laborious, um, not able to be turned around quickly. But as Kevin, you just said, you know helping customers. Government agencies get impact within 6 to 12 months versus 4 to 5 years. So you talked about Picard? Interesting name. Kevin. Tell me a little bit more about that technology and what it is that you guys deliver. That's unique. >>Well, honestly, it's probably best to start with Stephen. I can give you the high level. This is Stevens vision. I have to give him credit for that. And I will say way have lots of fun. Acronym. So it isn't Actually, it isn't backward. Um, right. Stephen doesn't actually stand for something. >>It stands for Platform for Integrated, a C three and Responsive for defense on >>Guy. You know >>that the Star Trek theme is the leg up from the last set of programs I had, >>which were >>my little ponies. So >>Oh, wow. That's a definite stuff in a different direction. Like >>it? Part of the great thing about working in the government is you get to name things, cool things, so but t get to your question eso So Picard really sprung out of this idea that I had a few years ago that the world but for our spaces, the Department of defense and the federal government was going to see a massive influx of the desire to consume sensors from from areas of responsibility, from installations and, frankly, from battlefields. Um, but they were gonna have to do it. In a way, um, uh, that presented some real challenges that you couldn't just kind of throw compute editor, throw traditional I t processes at it. You know, we have legacy sensors that are 40 years old sitting on installations. You know, old program, a logical controllers or facilities control systems that were written in cobalt in the seventies, right in the world are not even I, p based, most of them bond. Then on the other end of the spectrum, you have seven figure sensors that air, you know, throwing out megabits of second of data that are mounted to the back of jeeps. Right, That that air bouncing through the desert today. But we'll be bouncing through the jungle tomorrow, and you have to find all of those kind of in combined all of those together, um, and kind of create a cohesive data center for data set set for you know, the mission for, um, you know what we call a user to find common operating picture for a person. Thio kind of combine all of those different resource is and make it work for them. And so we found a great partner with security forces. Um, they realized that they wanted Thio to make a quantum leap forward. They had this idea that the next defender So there are there, like a military police outfit that the next defender was going to be a data driven defender and they were gonna have to win the information war war as much as they had to kind of dominate physical space. And they immediately got what we were trying to achieve, and it was just just great synergy. And then we've piled on some other elements, and we're really moving that platform forward to to kind of take every little bit of information we can get from the areas of responsibility and get it into a you know, your modern Data Lake, where they can extract information from all that data. >>Kevin, as the VP of information exploitation, that's a very interesting title. How are you helping government organizations to win the war on information? Leverage that information to make a big impact fast. >>Yeah. I mean, I think a lot of it is is that we try to break down the barriers between systems on data so that we can actually enable that data to fuse together to find and get insights into it. You know, as ML and I have become trendy topics, you know, they're very data hungry operations. And I think what Steven has done with the card and his team is really we want to be able to make those sensors seamless from a plug and play perspective that Aiken plug in a new sensor. It's a standards based, uh, interface that sends that data back so that we can and take it back to the user to find Operation Picture and make some decisions based off of that data. Um, you know, what's more is that data could even refused with more than the data that Stevens collecting off the sensors. It could be commercial data, other government data and I think is Davis. As Stephen said earlier, you have to get it back. And as long as you've gotten back in Labour's share with some of our mission partners, then you can do amazing things with it. And, you know, Stephen, I know you have some pretty cool ideas and what we're gonna do on the edge, right? How do we do some of this work of the edge where a sensor doesn't allow us to pull out that data back? >>Yeah, and and Thio follow on to what you were kind of referring to with regards to thio handling heterogeneous data from different sensors. Um, one of the main things that our government customers and we have seen is that there are a lot of historically there are a lot of vertical solutions where you know, the sensor, the platform, and then the data Laker kind of all part of this proprietary stack. And we quickly realized that that just doesn't work. And so one of the major thrust of that card platform was to make sure that we had ah, platform by which we could consume data through adapters from essentially any sensor speaking. Any protocol with any style data object, Whether that was an industry standard or a proprietary protocol, we could quickly interested and bring it into our Data lake. And then to pile on to what Kevin was talking about with compute. Right? So you have, uh, like, almost like a mass locks hierarchy of needs when it comes to cyber data or thio this coyote data or kind of unified data, Um, you know, you wanna turn it into basic information, alerts alarms, then you want to do reporting on it, or analytics or some some higher level workflow function. And then finally, you probably want to perform some analytics or some trending or sort of anomaly detection on it. And and that gets more computational e intensive each step of the way. And so you gotta You gotta build a platform that allows you to to both take some of that high level compute down to the edge, but also then bring some of that data up into the clouds where you could do that processing, and you have to have kind of fun jubilate e between that and so that hard platform allows you to kind of bring GP use and high processing units down to the edge and and make that work. Um, but then also and then as maybe even a first passive to rule out some of the most you know, some of the boring gated in the video Analytics platform. We call it Blue Sky and Blue Ocean. Right, so you're recording lots of video. That's not that interesting. How do you filter that out? So you're only sending the information The interesting video up eso You're not wasting bandwidth on stuff that just doesn't matter on DSO. It's It's a lot of kind of tuning these knobs and having a flexible enough platform that you could bring Compute down when you need it. And you could bring data up to compute on Big Cloud while you need it, and just kind of finding a way to tune that that that really does. I mean it. You know, that's a lot of words about how you do that. But what that comes to is flexible hardware and being able to apply those dev ops and C I. C D platform characteristics to that edge hardware and having a unified platform that allows you to kind of orchestrate your applications in your services all the way up and down your stack, from micro controllers to a big cloud instant creation. >>You make it sound so easy. Steven Kevin. Let's wrap it up with you in terms of like making impacts and going forward. We know the edge has exploded, even mawr, during this very interesting year. And that's going to be something that's probably going to stay, um, stay as a permanent impact or effect. What are some of the things that we can expect in 2021 in terms of how you're able to help government organizations capitalize on that, find things faster, make impact faster? >>Yeah. I mean, I think the cool thing we're seeing is that there's a lot more commoditization of sensors. There's a lot more censored information. And so let's use lighters. Example. We you know, things were getting cheaper, and so we can all of a sudden doom or or more things at the edge, and we ever would have expected. Right when you know Steven's team is integrating camera data and fence data from 40 years ago, you know, it's just saying on off it's not do anything fancy. But now we you know, you know, Stephen, I camera whether Metro you gave him before was, but the cost of light are has dropped so significantly that we can now then deploy that we can actually roll it out there and not being locked in their proprietary, uh, system. Um, so I see that being very powerful, you know? Also, I can see where you start having sensors interact with each other, right? So one sensor finds one thing and then a good example that we've started thio experiment with. And I think Steve, you could touch on it is using triggering a sensor, triggers a drone to actually investigate what's going on and then therefore, hybrid video back and then automatically can investigate instead of having to deploy a defender to actually see what happened at that. At that end, Points dio e don't know. There's it's amore detail you can provide there. >>Yeah, No. So exactly that Kevin. So So the power of the sensor is is something something old that that gives you very uninteresting Data like a one or a zero on on or off can detect something very specific and then do something kind of high speed, like task a drone to give you a visual assessment and then run object detection or facial recognition on, you know, do object detection to find a person and do facial recognition on that person to find out if that's a patrol walking through a field or a bad guy trying Thio invade your space. Um and so it's really the confluence and the gestalt of all of these sensors in the analytics working together, Um, that really creates the power from very simple, simple delivery. I think, um, there's this, You know, this idea that you know, ah 100 bytes of data is not that important. But when you put a million sensors giving you 100 bytes of data, you can truly find something extremely powerful. And then when you kind of and you make those interactions sing, um, it's amazing. Tow us the productivity that we can produce and the kind of fidelity of response that we can give thio actors in the space whether that's a defender trying to defend the base or a maintenance person trying thio proactively replace the fan or clean the fan on an H vac system. So So you know, you know, there isn't a fire at a base or for, uh, interesting enough. One of the things that we we've been able to achieve is we've taken maintenance data for helicopter engines and And we've been able to proactively say, Hey, you need to You need to take care of this part of the helicopter engine. Um and it saves money. It saves downtimes. It keeps the birds in the air. And it's a relatively simple algorithm that we were able to achieve. And we were able to do that with the maintenance people, bring them along in this endeavor and create analytics that they understood and could trust on DSO. I think that's really the power of this base. >>Tremendous power. I wish we had more time to to dig into it. Guys, thank you so much for sharing. Not just your insights, what nobody is doing but your passion for what you're doing and how you're making such an impact. Your passion is definitely palpable. Steven. Kevin, Thank you for joining me today. >>Thank you >>for my guests. I'm Lisa Martin. You're watching the Cube? Virtual. Yeah,

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020. Special coverage sponsored by AWS Global Partner Network. Hello, everyone. This is Dave Balanta. And welcome to the cubes Virtual coverage of AWS reinvent 2020 with a special focus on the A p N partner experience. I'm excited to have two great guests on the program. Fernando Castillo is the head s a p on AWS Partner Network and s A P Alliance and AWS and Stephen Jones is the general manager s a p E c to enterprise that aws Gentlemen, welcome to the Cube. Great to see you. >>I'm here. >>So guys ASAP on AWS. It's a core workload for customers. I call it the poster child for mission Critical workloads and applications. Now a lot has happened since we last talked to you guys. So So tell us it. Maybe start with Stephen. What's going on with Sapna Ws? Give us the update. >>I appreciate the question Day. Look, a lot of customers continue to migrate. These mission critical workloads State of us on a good example is the U. S. Navy right? Who moved their entire recipe landscape European workload AWS. This is a very large system of support. Over 72,000 users across 66 different navy commands. They estimate that 70 billion worth of parts and goods actually transact through the system every year. Just just massive. Right? And this this type of adoptions continued to accelerate a very rapid clip. And today, over 5000 customers now are running SFP workloads. I need to be us on there really trusting us, uh, to to manage and run these workloads. And another interesting stat here is that more than half of these customers are actually running asap, Hana, which is a safe He's flagship in memory database. >>Right, Fernando, can you add to that? >>Sure. So definitely about, you know, the customs are also SCP themselves continue to lose a dollar less to run their own offerings. Right? So think about conquer SCP platform. SCP analytics were when new offers like Hannah Cloud. In addition to that, we continue to see the P and L despondent network to grow at an accelerated pace. Today we have over 60 SNP company partners all over the world helping SFP customers s O that customers are my green. There s appeal asking CW's. They only look for reduced costs, improved performance but also toe again access to new capabilities. So innovate around their core business systems and transform their businesses. >>So for now, I wonder if I could stay with you for a minute. I mean, the numbers that Steve was putting out there, it's just massive scale. So you obviously have a lot of data. So I'm wondering when you talk to these customers, Are you discerning any common patterns that are emerging? What are some of the things that you're hearing or seeing when you analyze the data? >>Sure. So just to give a couple example right. Our biggest customers are doing complete ASAP. Transformations on Toe s four Hana. Their chance they're going to these new S a p r p code nine All customers have immediate needs, and they're taking their existing assets to AWS, so looking to reduce costs and improve performance, but also to sell them apart for innovation. This innovation is something that operation or something that they can wait. They need it right now. It's they This time to innovate is now right on some of these customers saying that while s and P has nice apart. So that is a multi year process on most organizations and have a look from waiting for this just before they start innovating. So instead of that, they focus on bringing what they have on start innovating right away on Steve has some great stories around here, so maybe Steve can share with that. Goes with that? >>Yeah, that'd be great, Steve. >>Yeah. Look, I think a good example here on and Fernando touched it, touched on it. Well, right. So customers coming from all kind of different places in their journey aws as it relates to this this critical workload and some are looking to really reap the benefits of the investments they made over the last couple decades sometimes. And Vista is a really good example Here, um there a subsidiary of Cook Industries, they migrated and moved their existing S a P r P solution called E c C. To AWS. They estimate that this migration alone from an infrastructure cost savings perspective, has netted them about two million per year. Additionally, you know, they started to bring some of the other issues they were trying to solve from a business perspective, together now that they were on the on the on the business on the AWS platform. And one thing that recognizes they had different data silos, that they had been operating in an on premises world. Right? So massive factories solution and bringing all of that data together on a single platform on AWS and enriching that with the SCP data has allowed them to actually improve their forecasting supply chain processes across multiple data sources and the estimate that that is saving them additional millions per year. So again, customers are not necessarily waiting to innovate. Um, but actually moving forward now. >>All right, so I gotta ask, you don't hate me for asking this question, but but everybody talks about how great they are. Supporting s a P is It's one of the top, of course, because s a p, you know, huge player in the in the application space. So I want you guys to address how aws specifically compares Thio some of your competitors that are, you know, the hyper scaler specifically as it relates to supporting S a P workloads. What's the rial differential value that you guys bring? Maybe Steve, you could start >>Sure, you're probably getting to know us a little bit. Way don't focus a lot on competition, Aziz mentioned week We continue to see customers adopt AWS for S a p a really rapid clip. And that alone actually brings a lot of feedback back into how we consider our own service offerings as it relates to this particular workload on that, that's it. That's important signal right for what we're building. But customers do tell us the security performance availability matters, especially for this workload, which, you know, to be honest, is the backbone of many, many organizations. Right? And we understand why. And there was a study that was done recently about a. D. C. Where they found that even a single hour of unplanned downtime as a released this particular workload could cost millions. And so it's it's super important. And if you look at, um, you know, publicly available data from an average perspective, um, it has considerably less downtime than the other hyper scale is out there way. Take the performance and availability of oh, our entire global footprint and in this workload in particular, super important. >>Well, you know, that's a great point, Steve. I mean, if you got critical mission critical applications like ASAP supporting the business, that's driving revenue. It's driving productivity. The higher the value of the application, the greater the impact when it's down, I wonder, Fernando, you know, Steve said, You guys don't focus on the competition. Well, is an analyst. You know, I always focused on the competition, So I wonder if you're gonna add anything to that. >>Sure. So again, as you can imagine, multiple analyst called Space right. And, uh, everybody shares information. And analysts have agreed that Italy's clean infrastructure services, including the three quite a for CP across the globe. So we feel very humble and honor about this recognition on this encourages to continue to improve ourselves to give you a couple examples for a 10 year in a row. Italy's US evaluated as a leader in the century Gardner Magic Quadrant, right for cloud infrastructure from services. And, as you know, the measure to access right they measure very execute on complete, insufficient were the highest, both of them. Another third party, just not keep with one is icy, right? You know, technology research dreamers, you already you might know advice for famous Well, the reason they publisher s a p on infrastructure service provider lands reports long name which, basically, the analyzers providers were best suited to host s a. P s four hana workloads on more broadly s a p Hannah landscapes, you know, very large scape ASAP 100 landscapes. So they recognize it, at least for the third year in a row. And conservative right, the best class enterprise. Great infrastructure towards security performances, Steve mentioned, but also making the panic community secure. Differentiation. Andi, they posted. They mentioned it all us as a little position in quadrant for the U. S. U K France, Germany, the Nordics in Brazil. So again, really honor and humble on discontinued in court just to continue to improve. >>You know, Steve, I just wrote a piece on Cloud 2030 trying to project what the next 10 years is gonna look like in one of the I listed a lot of things, but one of the things I talked about was some of the technical factors like alternative processors, specialized networks, and you guys have have have really, always done a good job of sort of looking at purpose built, you know, stuff that that can run workloads faster. How relevant is that in the the S A P community? >>Oh, that's a great question, David. It's It's absolutely relevant. You take a look at what? What we've done over the years with nitro and how we've actually brought the ability for customers to run on environmental infrastructure but still have that integrated, uh, native cloud experience. Uh, that is absolutely applicable to Unless if you workload and we're actually able toe with that technology, bring the capability to customers to run thes mission critical workloads on instances with up to 24 terabytes of brand, albeit bare metal, but fully integrated into the AWS network fabric, >>right? I mean, a lot of people, you know, need that bare metal raw performance on, and that makes sense that you've been, you know, prioritize such an important class of workload. I'm not surprised that that I mean, the numbers that you threw out a pretty impressive eso. It's clear you're leading the charge here. Maybe you could share a little glimpse of what's coming in the future. Show us a little leg, Steve. >>Yeah, well, look, uh, we know that infrastructure is super important. Thio. Our customers and in particular the customers are running these mission critical workloads. But there's a lot of heavy lifting, uh, that that we also want to simplify. And so you've seen some indications of what we've done here over the years, uh, ice G that Fernando mentioned actually called out. AWS is differentiating here, right? So for for many years, we've actually been leading in releasing tools for customers to actually orchestrate and automate the deployment of these types of worthless so ASAP in particular. I mean, if you think about it a customer who is coming to a to a hyper scale platforms like AWS and having to learn what that means, Plus understand all the best practices from S, A, P and AWS to make that thing really shine from a performance and availability perspective, that's a heavy asked. Right? So we put a lot of work from a tooling perspective into into automating this and making this super simple not just for customers, but also partners. >>Anything you wanna chime in on that particular the partner side, Fernando. >>Sure. So this is super important for public community, right? As you can imagine, the tooling that we're bringing together toe. The market is helping the Spanish to move quicker, right? So they don't have to reinvent. They will all the time. They will just take this and move and take it and move forward. Give an example. One of our parents in New York, three hosts. Thanks for lunch. We start with Steve just reference right. They want to create work clothes in an automated way. Speeding up the delivery time. 75% corporation is every environments. So it just imagine the the impact of these eso a thing here that is important is our goal is to help customers and partners move quicker, removing any undifferentiated heavy lifting, right, Andi, that's kind of the mantra of this group. >>You know, when you think about what Doug Young was saying is in the keynote, um, the importance of partners and I've been on this kick about we've moved in this industry from products to platforms, and the next 10 years is gonna be about leveraging ecosystems. The power of many versus the resource is of a few or even one is large is a W s so so partners air critical on I wonder if you could talk toe the role that that the network partners air playing in affecting S a p customer outcomes and strategies. Maybe Steve, you could take that first. >>Yeah, but look, we recognize that the migration on the management of these systems it's complex, right? And for years, we've invested in a global community of partners many partners who have been fundamental to s a p customer success over over a couple decades, Right? And so, um, that there are some nuances that that need to be realized when it comes to running ASAP on on a hyper scale platforms like AWS. And so we put a lot of work into making sure these partners are equipped to ensure customers have have a really good experience. And I mean, in a recent conversation I had with a CEO of a large, uh, CPG company, he told me he reflected that the partners really are the glue. That kind of brings it all together for them. And, uh, you know, just to share something with you today, our partners, our partner community network for S. If he is actually helping over 90% of net new customers who are coming toe migrate as if you were close to AWS, so they're just absolutely critical. >>So, Fernando, there's the m word, the migration, you know, it's you don't want to unless you have to, but people have to move to the cloud. So So what can you add to this conversation? >>Sure, they So again, just to echo what Steve mentioned, right? Uh, migration. Super important. We have ah group of partners that are right now specializing in migration projects. And they have built migration factories. You may have seen some of them. They have been doing press releases through the whole year saying that they're part of these and their special cells they're bringing to the helping customers adopt AWS. So they go through the next, you know, very detailed process. We call them map for ASAP partners. So they have these incremental value on top of being SCP competent funds, which I referred earlier on. This group has, as mentioned, you know, show additional capability to safeguard these migrations on. Of course, we appreciate and respect and we have put investment programs for them to help them support their own customers right in those in these migrations. But because the SNP ecosystem on it. But it's not about only migrations, right? One important topic that we need technologies as you as Steve mentioned, we have these great set of partner of customers have trusted us or 5000 through a year on these, uh, these customers asking for innovation right there, asking us how come the ecosystem help us innovate faster? So these partners are using a dollars a plan off innovation, creating new solutions that are relevant for SCP. So basically helping customers modernize their business processes so you can take an example like Accenture Data Accelerator writers taking SCP information and data legs Really harm is the power of data there or the Lloyd you know, kinetic finances helping, you know, deploy Central finance, which is a key component of SCP, or customer like partners like syntax that has created our industrial i o. T. Offering that connects with the SNP core. So more and more you will see thes ecosystem partners innovating on AWS to support SNP customers. >>You know, I think that's such an important point because for for decades have been around for a while. It's the migrations air like this. Oftentimes there's forced March because maybe a vendor is not going to support it anymore. Or you're just trying to, you know, squeeze Mawr costs out of the lemon. What you guys are talking about is leveraging an ecosystem for innovation and again that ties into the themes that we're talking about about Cloud 2030 in the next decade of innovation. Let's close, guys. What can customers ASAP customers AWS customers expect from reinvent this year? Um, you know, maybe more broadly, what can they expect from A W S in the coming 12 months? Maybe, Steve, you could give us a sense, and then Fernando could bring us home. >>You bet. Look, um, this year we've really tried to focus on customer stories, right? So we've we've optimized. There's a number of sessions here agreement this year. We want customers and partners to learn from other from other customer experiences, so customers will be able to listen to Bristol Myers Squibb talk about their performance, their their experiences, Alando Newmont's and Volkswagen. And I'll be talking about kind of different places where they are on this, this journey to cloud and this innovation life cycle, right, because it really is about choice and what's right for their business. So we're pretty excited about that. >>Yeah. Nice mix of representative Industries there. I Fernando bring us home, please. >>Sure. So, again, we think about 21 in the future. Rest assured, we'll continue to invest heavily to make sure it values remains the platform innovation. Right on choice for recipe customers where a customer wants to move their existing investments on continue to add value. So what they have already done for years or goto export transformation. We're here to support their choice. Right? And we're committed to that as part of our customers Asian culture. So we're super excited about the future. And we're thankful for you to spend time with us today. >>Great, guys, Look, these are the most demanding workloads we're seeing that that rapid movement to the cloud is just gonna accelerate over the coming years. Thanks so much for coming on The Cube. Really appreciate it. >>Our pleasure. Thank >>you. All >>right. Thank you for watching everyone keep it right there from or great content. You're watching the cube aws reinvent 2020

(upbeat music) >> From around the globe, it's the CUBE. With digital coverage of AWS reinvent 2020 sponsored by Intel, AWS and our community partners. Hello, and welcome back to the CUBE's coverage. Virtual coverage of AWS reinvent 2020. It's three weeks we're going on, we're on the ground here in Palo Alto. Doing the remote CUBE, CUBE virtual. We are virtual CUBE here. Wall-to-wall coverage over the three weeks. Got a great segment here, Steven Long regional CTO for AppDynamics and Ron Teeter chief architect with Jobvite. Gentlemen, thank you for joining me on the segment. Appreciate it. Thanks for coming on the CUBE Virtual. >> Thanks for having us. >> Great to be here. >> I wish we were in person. Normally we are, but with the pandemic, it's hard. Steven at AppDynamics. I want to ask you, you've got a customer here. We're going to dig into the use cases of the transformation journey, multiple wave transformation which I'm excited to talk about. But talk about AppDynamics. What's the big story for you guys at re:Invent, quickly get 10 seconds to explain. >> Yeah, sure. AppDynamics is the number one world's leading APM vendor and we're there to be the full stack observability platform. And in that we're talking today about our deep code insights, really to gain that visibility into production, securely capture data and really get that context through a dynamic application. So that you can find the problem and fix it right the first time. >> Great. Thanks. Thanks for that insight. Ron, I want to get into you're the chief architect which means you get the keys to the kingdom at Jobvite. You got to look over the landscape. You got to have the 20 mile stare out to the future but you also got to deal with the reality present here. And it's a tough one. When you go back, this is a terrible year a lot of weirdness, a lot of craziness but everyone's hurting, but they're retooling or they have a tailwind behind their back. So they're either accelerating faster or they're retooling. What does transformation mean to you these days? >> Yeah, so for Jobvite we had a distributed workforce prior to the pandemic shutdown. And what it did for us is it actually forced us to go all in on why can't we work remote all the time? Why do we care where we have facilities? And so we've gotten really good at scaling our organization and being productive remote. Now we actually can hire anywhere we want to, right? And that gives us more leverage and opportunities to scale our DMS going into '21. >> Awesome. Now from a technology standpoint, I'm hearing a couple of different stories, there's two extremes is the... I'll say airlines or those kinds of markets where there's not a lot of business happening, but they're retooling. And then you got obviously video or anything virtual modern applications. It's a surge in business so you have to move faster. Speed is critical. How do you retool in an environment where you've got remote which is totally productive, get that. But now I got more teams. I got to coordinate, I got to communicate. I got to make decisions, architectural decisions. They're big ones. And cloud certainly is here. You've got hybrid and you got the edge big themes this week. How do you look at that? >> The way we look at it, Jobvite has the longevity to remember what it was like from 2008 to 2013. we took that economic recession to build two additional products and launch them into the market in 2013 so that we could ride that wave of growth to drive our business objectives. And we're doing the same thing now. Hiring is a fluid market and this year hiring was way down, right? We saw a 60% drop in open requisitions on our platform alone. And you could see it just dive in March but it started coming back in August and September. And so at this point, we're now post pandemic. So the hiring rates right now are higher than they've been all year. They're very close to where they were last year for the same time. And we expect that to continue to climb as businesses continue to evaluate whether or not it's safe to scale. For us, this lull means we've got an opportunity to make changes to our infrastructure, that aren't going to be disruptive to our customers. But also allow us to get out in front of that so that we can go into '21 with a very strong product focus by taking care of some of the technical debt now. That's exactly what we've been doing is investing in ourselves so that we can operate faster with more agility next year. >> That's worth calling out and mentioning. That's great insight. It really is. You got to come out of the pandemic with a growth strategy. I hear all the CXOs and CSOs in particular dealing with all the security uses but they've got to have the growth strategy. Steven, this is where I think the cloud speed scale the operating model of software networks compute. You're seeing that now get back into the swing of things. That's always been the holy trinity, if you will, of technology, network compute, and storage. Now it's got the cloud and you've got an operating model. We're back to kind of a groove swing here. How does AppDynamics and the Amazon all fit together into this kind of journey? >> Yeah, and if we really look at AppDynamics, we focus on that digital experience. And I think when the pandemic hit we saw 95% of customers that we surveyed in our agents of transformation COVID survey that their priorities shifted. And 95% of those said, within that shift the digital experience became front and center. And so when you're operating in the cloud and you want to have that full stack observability not just from the end user and not just from your application perspective but also from your business perspective. And in any given business the application is the north star of the business. So placing the emphasis on that AppDynamics and surfacing what's actually happening at eliminating blind spots during this pandemic where it's more important than ever to have the best digital experience because that's the brand loyalty really, is that digital experience. >> Ron, I want to ask you, you mentioned something earlier you were talking about how you seized on the opportunity to virtual at home and you retool. But also you mentioned some of the macro conditions in the market, jobs are down, the other on the backup on the upswing. But the geographic hiring is a huge deal. I can almost imagine that's kind of an unforeseen use case where, I mean, it's kind of like working at home. Yeah, X percentage of people be working at home. We plan for that in our disaster recovery or disruption management whatever they do, now 100% people work at home. Now, 100% people looking for jobs. You probably need to rethink the use case because when you have a platform, you've got to think, okay how do I serve my customers who might have a need to recruit from more geographical places? That's a coding thing. So how do you do that stuff? Take us through the mindset of what goes on in your world. >> Yeah, so one of the nice things about building Jobvite as a hiring platform and a sourcing engine is, we use it, right? We now need to solve the same kinds of problems internally that our customers are facing, right? And so we control the software, we understand the problem. And so it's just a matter of deciding these are the things that we're going to prioritize next. We saw a very active summer of social justice outrage, and a lot of that stems from the lack of diversity and inclusive in hiring. And we're already responding to that by delivering features into the product line to help our customers address that in their places. And the key to this is speed. I think you mentioned it and Steve's talked about it as well, right? The ability to move quickly, safely it's the grail that everybody's looking for. And you have to have the right partners to make that successful. >> So I got to ask you then, what are the main benefits you see as you've got working at AppDynamics obviously you're a good customer there. As we're talking through it, you've got a great environment. You're a leader, and how to take advantage of these opportunities and code and shift. What's the benefits of AppDynamics in that equation? >> The key there that we see with AppDynamic's Cisco is the scale and the amount of innovation that they can drive through their product line. One of the things that Steve and I were talking about earlier this year during their transform event, was this deep code insights component which is really production runtime debugging. So imagine I can knock out my meantime to repair, my zero trust and my accelerated solutions of early detection in one solution, right? I can take something that would normally take hours, if not days, into minutes to resolve. The impact on an organization of just one simple feature like that is tremendous when people understand what it can do for them. And it's been invaluable for us. >> Well, you got the speed and the scale with the cloud. So take me through that impact. Because one of the things that's being discussed heavily here at re:Invent and in the industry is the new normal and the new realities we're living in, post pandemic as well. What's going to come out of it. And that is the expectations of the users and is going to drive the new experiences. That's kind of the theme. So the question is whether that's developers or end users or consumers or business users. That's huge, for applications to know what the user experience may be because we don't know what they expect and you don't have the right security (laughs). It kind of all crashes. So what's your... I mean you're nodding your head, weigh in, please. >> Yeah, so I heard a comment earlier from one of my peers in the industry, that is basically saying that nine months ago he had 400 facilities and now he has 18,000. Trying to imagine securing that environment For us, the way we think about it is, work is where you're at. And so we solve the access problems and the tooling problems a long time ago for Jobvite. But we'd been doing for our customers is delivering mobile recruiting solutions. So imagine I don't even need my desktop to complete the hiring process. I can work through the negotiations with a recruiter. I can talk to the candidate, I can text them. One of the big things that we released in early access last month was our new intelligent messaging platform. So that recruiters and hiring managers can have a much more rich conversation with candidates on the mobile device where the candidate is, right? And that's how we're trying to bring this new reality to the marketplace and say, "I can't assume that somebody has a browser and an email client anymore," right? Texting-- >> I mean that's a huge point. I mean, the joke Steven is, it's just distributed computing again 'cause there's really no cloud, there's no... If you think about the edge it's everything is the operating environment. Edge is the data center, edge is the cloud, edge is someone else's place. So if you're thinking about what Ron just said, 18,000 facilities, their homes or wherever, everything has to be looked, that's distributed computing we've been there before, right? I mean. >> Yeah and I think the way Ron, I think describes it, is highly accurate in his company, obviously, but in many companies where, if you've got those 18,000 end points in distributed computing you need to be able to gain visibility into production. And production could be a piece of code living anywhere. And if you can gain that and do that in a secure way which what we do AppDynamics, with our deep code insights, then you can look at data on demand and you can begin to understand the context of what's happening for that end user experience. And you can line up a watch point to watch the code that's executing. And then if it's not working you can actually see how it's not working, recreate that and actually fix it right the first time because you can actually see the code and production in the cloud in this distributed environment. And really be it's more powerful way to operate to reduce time when something happens that you need to fix. >> I was talking to a friend yesterday about this. We weren't on camera. I wish the cameras were rolling. But I'd love to get you guys' reaction to this, because we were saying... I remember when I broke into the business as a young CS major in the late '80s. We had to install everything by hand the software, you install stuff on a server. Or had stuff on a machine and then you put it on a server, you put it in a data center, all those things, right? The young kids then come in and saying, "Okay, I just use the cloud." The next generation and they never installed anything. They don't even know what an installation pack is. Now the next generation's not going to have versions. So you start to get into this notion of evolution with software, because if it's software operated you don't know what version you're running. It shouldn't be disruptive. And the point is this is where I think you guys are getting to here is, the holy grail is there's no disruption. You're running your software at home. Your reaction to that kind of evolution. >> Yeah, you want to take that Steve first? >> Yeah, sure. I mean, it's like you said, the way that code has gone to be delivered and executed is it explodes and disappears. And if there is no way to track that and trace that and understand it, the generation we're in of code, that's a femoral. What's it going to be next? And where's it going to go? And will it ever live anywhere that's alive? The technologies are really being pushed and that's the exciting part. And that's why from an AppDynamics perspective, we're investing in open telemetry is distributed tracing. So as you got distributed computing, we do distributed tracing. And we really look at the problem and provide solutions for our customers. >> Awesome. Ron, your time. I mean, observability, if you can't observe it, you can't measure it. You don't understand, it opens up a lot of things. You got to have the observation space and that's everything. It's hard. >> Yeah. Yeah. And especially as we transition from visual physical servers to virtual hosts, to virtual processes, to virtual functions, right? At some point it's the, I don't even know how to measure capacity for a function in the cloud, right? Let alone try to understand well, what's the cost going to be before I actually deploy it and measure what it's going to cost, right? So these are some of the areas where I think a lot of companies are struggling in understanding how do I move something I'm traditionally very comfortable with. This, I know how much a host costs and I can put my software on there and I can run the CPU at 100% and then I know what I'm getting. But as you start moving into virtual processes and virtual functions, it makes it so much more difficult to think about how you do that capacity planning and budgeting exercise in advance. One of the things that we do with observability is we can test it and we can measure it. And then based on that measure we can make predictions about, okay, this is what it looks like in Def, let's extrapolate what that looks like in production, just by scaling the load. And in areas where you've taken IO and network out of the equation that kind of extrapolation works very well. >> That's awesome. Congratulations on a great a use case, Ron. Thanks for sharing your story, Steven. >> Of course. >> Thanks for coming along and highlighting this great use case and congratulations on having a killer product with observability of AppDynamics. We've been following your work as a company and now at Cisco. So yeah, it's killer software. >> Thanks. Modern software is upon us again the next levels here, gentlemen, thank you for your time. Appreciate the insight >> It been a pleasure. >> Welcome. Thank you for the opportunity. >> Okay, This is the CUBE's virtual covers. We are the CUBE virtual. This is what we do now. We're not in person, but we're remote. When we get back to real life, we'll be back on the scene. We're still doing the interviews. Thanks for watching re:Invent coverage 2020 virtual. (soft music)

from the cube studios in Palo Alto in Boston connecting with thought leaders all around the world this is a cube conversation hi Bri welcome back this is Dave Volante and welcome to this special presentation made possible by IBM we're talking about data op data ops in Acton Steve Lucas here he's the senior vice president and director of data management at Associated Bank be great to see how are things going and in Wisconsin all safe we're doing well we're staying safe staying healthy thanks for having me Dave yeah you're very welcome so Associated Bank and regional bank Midwest to cover a lot of the territories not just Wisconsin but another number of other states around there retail commercial lending real estate offices stuff I think the largest bank in in Wisconsin but tell us a little bit about your business in your specific role sure yeah no it's a good intro we're definitely largest bank at Corvis concen and then we have branches in the in the Upper Midwest area so Minnesota Illinois Wisconsin our primary locations my role at associated I'm director data management so been with the bank a couple of years now and really just focused on defining our data strategy as an overall everything from data ingestion through consumption of data and analytics all the way through and then I'm also the data governance components and keeping the controls and the rails in place around all of our data in its usage so financial services obviously one of the more cutting-edge industries in terms of their use of technology not only are you good negotiators but you you often are early adopters you guys were on the Big Data bandwagon early a lot of financial services firms we're kind of early on in Hadoop but I wonder if you could tell us a little bit about sort of the business drivers and and where's the poor the pressure point that are informing your digital strategy your your data and data op strategy sure yeah I think that one of the key areas for us is that we're trying to shift from more of a reactive mode into more of a predictive prescriptive mode from a data and analytics perspective and using our data to infuse and drive more business decisions but also to infuse it in actual applications and customer experience etc so we have a wealth of data at our fingertips we're really focused on starting to build out that data link style strategy make sure that we're kind of ahead of the curve as far as trying to predict what our end users are going to need and some of the advanced use cases we're going to have before we even know that they actually exist right so it's really trying to prepare us for the future and what's next and and then abling and empowering the business to be able to pivot when we need to without having everything perfect that they prescribed and and ready for what if we could talk about a little bit about the data journey I know it's kind of a buzzword but in my career as a independent observer and analyst I've kind of watched the promise of whether it was decision support systems or enterprise data warehouse you know give that 360 degree view of the business the the real-time nature the the customer intimacy all that in and up until sort of the recent digital you know meme I feel as though the industry hasn't lived up to that promise so I wonder if you could take us through the journey and tell us sort of where you came from and where you are today and I really want to sort of understand some of the successes they've had sure no that's a that's a great point nice I feel like as an industry I think we're at a point now where the the people process technology have sort of all caught up to each other right I feel that that real-time streaming analytics the data service mentality just leveraging web services and API is more throughout our organization in our industry as a whole I feel like that's really starting to take shape right now and and all the pieces of that puzzle have come together so kind of where we started from a journey perspective it was it was very much if your your legacy reporting data warehouse mindset of tell me tell me the data elements that you think you're going to need we'll figure out how do we map those in and form them we'll figure out how to get those prepared for you and that whole lifecycle that waterfall mentality of how do we get this through the funnel and get it to users quality was usually there the the enablement was still there but it was missing that that rapid turnaround it was also missing the the what's next right than what you haven't thought of and almost to a point of just discouraging people from asking for too many things because it got too expensive it got too hard to maintain there was some difficulty in that space so some of the things that we're trying to do now is build that that enablement mentality of encouraging people to ask for everything so when we bring out new systems - the bank is no longer an option as far as how much data they're going to send to us right we're getting all of the data we're going to we're going to bring that all together for people and then really starting to figure out how can this data now be used and and we almost have to push that out and infuse it within our organization as opposed to waiting for it to be asked for so I think that all of the the concepts so that bringing that people process and then now the tools and capabilities together has really started to make a move for us and in the industry I mean it's really not an uncommon story right you had a traditional data warehouse system you had you know some experts that you had to go through to get the data the business kind of felt like it didn't own the data you know it felt like it was imposing every time it made a request or maybe it was frustrated because it took so long and then by the time they got the data perhaps you know the market had shifted so it create a lot of frustration and then to your point but but it became very useful as a reporting tool and that was kind of this the sweet spot so so how did you overcome that and you know get to where you are today and you know kind of where are you today I was gonna say I think we're still overcoming that we'll see it'll see how this all goes right I think there's there's a couple of things that you know we've started to enable first off is just having that a concept of scale and enablement mentality and everything that we do so when we bring systems on we bring on everything we're starting to have those those components and pieces in place and we're starting to build more framework base reusable processes and procedures so that every ask is not brand new it's not this reinvent the wheel and resolve for for all that work so I think that's helped if expedite our time to market and really get some of the buy-in and support from around the organization and it's really just finding the right use cases and finding the different business partners to work with and partner with so that you help them through their journey as well is there I'm there on a similar roadmap and journey for for their own life cycles as well in their product element or whatever business line there so from a process standpoint that you kind of have to jettison the you mentioned waterfall before and move to a more being an agile approach did it require different different skill sets talk about the process and the people side of yeah it's been a it's been a shift we've tried to shift more towards I wouldn't call us more formal agile I would say we're a little bit more lean from a an iterative backlog type of approach right so what are you putting that work together in queues and having the queue of B reprioritized working with the business owners to help through those things has been a key success criteria for us and how we start to manage that work as opposed to opening formal project requests and and having all that work have to funnel through some of the old channels that like you mentioned earlier kind of distracted a little bit from from the way things had been done in the past and added some layers that people felt potentially wouldn't be necessary if they thought it was a small ask in their eyes you know I think it also led to a lot of some of the data silos and and components that we have in place today in the industry and I don't think our company is alone and having data silos and components of data in different locations but those are there for a reason though those were there because they're they're filling a need that has been missing or a gap in the solution so what we're trying to do is really take that to heart and evaluate what can we do to enable those mindsets and those mentalities and find out what was the gap and why did they have to go get a siloed solution or work around operations and technology and the channels that had been in place what would you say well your biggest challenges in getting from point A to point B point B being where you are today there were challenges on each of the components of the pillar right so people process technology people are hard to change right men behavioral type changes has been difficult that there's components of that that definitely has been in place same with the process side right so so changing it into that backlog style mentality and working with the users and having more that be sort of that maintenance type support work is is a different call culture for our organization and traditional project management and then the tool sets right the the tools and capabilities we had to look in and evaluate what tools do we need to Mabel this behavior in this mentality how do we enable more self-service the exploration how do we get people the data that they need when they need it and empower them to use so maybe you could share with us some of the outcomes and I know it's yeah we're never done in this business but but thinking about you know the investments that you've made in intact people in reprocessing you know the time it takes to get leadership involved what has been so far anyway the business outcome and you share any any metrics or it is sort of subjective a guidance I yeah I think from a subjective perspective the some of the biggest things for us has just been our ability to to truly start to have that very 60 degree view of the customer which we're probably never going to get they're officially right there's there everyone's striving for that but the ability to have you know all of that data available kind of at our fingertips and have that all consolidated now into one one location one platform and start to be that hub that starts to redistribute that data to our applications and infusing that out has been a key component for us I think some of the other big kind of components are differentiators for us and value that we can show from an organizational perspective we're in an M&A mode right so we're always looking from a merger and acquisition perspective our the model that we've built out from a data strategy perspective has proven itself useful over and over now in that M&A mentality of how do you rapidly ingest new data sets it had understood get it distributed to the right consumers it's fit our model exactly and and it hasn't been an exception it's been just part of our overall framework for how we get that data and it wasn't anything new that we had to do different because it was M&A just timelines were probably a little bit more expedited the other thing that's been interesting in some of the world that were in now right from a a Kovach perspective and having a pivot and start to change some of the way we do business and some of the PPP loans and and our business models sort of had to change overnight and our ability to work with our different lines of business and get them the data they need to help drive those decisions was another scenario where had we not had the foundational components there in the platform there to do some of this if we would have spun a little bit longer so your data ops approach I'm gonna use that term helped you in this in this kovat situation I mean you had the PPE you had you know of slew of businesses looking to get access to that money you had uncertainty with regard to kind of what the rules of the game were what you was the bank you had a Judah cape but you it was really kind of opaque in terms of what you had to do the volume of loans had to go through the roof in the time frame it was like within days or weeks that you had to provide these so I wonder if we could talk about that a little bit and how you're sort of approach the data helped you be prepared for that yeah no it was a race I mean the bottom line was it felt like a race right from from industry perspective as far as how how could we get this out there soon enough fast enough provide the most value to our customers our applications teams did a phenomenal job on enabling the applications to help streamline some of the application process for the loans themselves but from a data and reporting perspective behind the scenes we were there and we had some tools and capabilities and readiness to say we have the data now in our in our lake we can start to do some business driven decisions around all all of the different components of what's being processed on a daily basis from an application perspective versus what's been funded and how do those start to funnel all the way through doing some data quality checks and operational reporting checks to make sure that that data move properly and got booked in in the proper ways because of the rapid nature of how that was was all being done other covent type use cases as well we had some some different scenarios around different feed reporting and and other capabilities that the business wasn't necessarily prepared for we wouldn't have planned to have some of these types of things and reporting in place that we were able to give it because we had access to all the data because of these frameworks that we had put into place that we could pretty rapidly start to turn around some of those data some of those data points and analytics for us to make some some better decisions so given the propensity in the pace of M&A there has to be a challenge fundamentally in just in terms of data quality consistency governance give us the before and after you know before kind of before being the before the data ops mindset and after being kind of where you are today I think that's still a journey we're always trying to get better on that as well but the data ops mindset for us really has has shifted us to start to think about automation right pipelines that enablement a constant improvement and and how do we deploy faster deploy more consistently and and have the right capabilities in place when we need it so you know where some of that has come into place from an M&A perspective is it's really been around the building scale into everything that we do dezq real-time nature this scalability the rapid deployment models that we have in place is really where that starts to join forces and really become become powerful having having the ability to rapidly ingesting new data sources whether we know about it or not and then exposing that and having the tools and platforms be able to expose that to our users and enable our business lines whether it's covent whether it's M&A the use cases keep coming up right they we keep running into the same same concept which is how rapidly get people the data they need when they need it but still provide the rails and controls and make sure that it's governed and controllable on the way as well [Music] about the tech though wonder if we could spend some time on that I mean can you paint a picture of us so I thought what what what we're looking at here you've got you know some traditional IDI w's involved I'm sure you've got lots of data sources you you may be one of the zookeepers from the the Hadoop days with a lot of you know experimentation there may be some machine intelligence and they are painting a pic before us but sure no so we're kind of evolving some of the tool sets and capabilities as well we have some some generic kind of custom in-house build ingestion frameworks that we've started to build out for how to rapidly ingest and kind of script out the nature of of how we bring those data sources into play what we're what we've now started as well as is a journey down IBM compact product which is really gonna it's providing us that ability to govern and control all of our data sources and then start to enable some of that real-time ad hoc analytics and data preparation data shaping so some of the components that we're doing in there is just around that data discovery pointing that data sources rapidly running data profiles exposing that data to our users obviously very handy in the emanating space and and anytime you get new data sources in but then the concept of publishing that and leveraging some of the AI capabilities of assigning business terms in the data glossary and those components is another key component for us on the on the consumption side of the house for for data we have a couple of tools in place where Cognos shop we do a tableau from a data visualization perspective as well that what that were we're leveraging but that's where cloud pack is now starting to come into play as well from a data refinement perspective and giving the ability for users to actually go start to shape and prep their data sets all within that governed concept and then we've actually now started down the enablement path from an AI perspective with Python and R and we're using compact to be our orchestration tool to keep all that governed and controlled as well enable some some new AI models and some new technologies in that space we're actually starting to convert all of our custom-built frameworks into python now as well so we start to have some of that embedded within cloud pack and we can start to use some of the rails of those frameworks with it within them okay so you've got the ingest and ingestion side you've done a lot of automation it sounds like called the data profiling that's maybe what classification and automating that piece and then you've got the data quality piece the governance you got visualization with with tableau and and this kind of all fits together in a in an open quote unquote open framework is that right yeah I exactly I mean the the framework itself from our perspective where we're trying to keep the tools as as consistent as we can we really want to enable our users to have the tools that they need in the toolbox and and keep all that open what we're trying to focus on is making sure that they get the same data the same experience through whatever tool and mechanism that they're consuming from so that's where that platform mentality comes into place having compact in the middle to help govern all that and and reprovision some of those data sources out for us has it has been a key component for us well see if it sounds like you're you know making a lot of progress or you know so the days of the data temple or the high priest of data or the sort of keepers of that data really to more of a data culture where the businesses kind of feel ownership for their own data you believe self-service I think you've got confidence much more confident than the in the compliance and governance piece but bring us home just in terms of that notion of data culture and where you are and where you're headed no definitely I think that's that's been a key for us too as as part of our strategy is really helping we put in a strategy that helps define and dictate some of those structures and ownership and make that more clear some of the of the failures of the past if you will from an overall my monster data warehouse was around nobody ever owned it there was there wasn't you always ran that that risk of either the loudest consumer actually owned it or no one actually owned it what we've started to do with this is that Lake mentality and and having all that data ingested into our our frameworks the data owners are clear-cut it's who sends that data in what is the book record system for that source data we don't want a ability we don't touch it we don't transform it as we load it it sits there and available you own it we're doing the same mentality on the consumer side so we have we have a series of structures from a consumption perspective that all of our users are consuming our data if it's represented exactly how they want to consume it so again that ownership we're trying to take out a lot of that gray area and I'm enabling them to say yeah I own this I understand what I'm what I'm going after and and I can put the the ownership and the rule and rules and the stewardship around that as opposed to having that gray model in the middle that that that we never we never get but I guess to kind of close it out really the the concept for us is enabling people and end-users right giving them the data that they need when they need it and it's it's really about providing the framework and then the rails around around doing that and it's not about building out a formal bill warehouse model or a formal lessor like you mentioned before some of the you know the ivory tower type concepts right it's really about purpose-built data sets getting the giving our users empowered with the data they need when they need it all the way through and fusing that into our applications so that the applications and provide the best user experiences and and use the data to our advantage all about enabling the business I got a shove all I have you how's that IBM doing you know as a as a partner what do you like what could they be doing better to make your life easier sure I think I think they've been a great partner for us as far as that that enablement mentality the cloud pack platform has been a key for us we wouldn't be where we are without that tool said I our journey originally when we started looking at tools and modernization of our staff was around data quality data governance type components and tools we now because of the platform have released our first Python I models into the environment we have our studio capabilities natively because of the way that that's all container is now within cloud back so we've been able to enable new use cases and really advance us where we would have a time or a lot a lot more technologies and capabilities and then integrate those ourselves so the ability to have that all done has or and be able to leverage that platform has been a key to helping us get some of these roles out of this as quickly as we have as far as a partnership perspective they've been great as far as listening to what what the next steps are for us where we're headed what can we what do we need more of what can they do to help us get there so it's it's really been an encouraging encouraging environment I think they as far as what can they do better I think it's just keep keep delivering write it delivery is ping so keep keep releasing the new functionality and features and keeping the quality of the product intact well see it was great having you on the cube we always love to get the practitioner angle sounds like you've made a lot of progress and as I said when we're never finished in this industry so best of luck to you stay safe then and thanks so much for for sharing appreciate it thank you all right and thank you for watching everybody this is Dave Volante for the cube data ops in action we got the crowd chat a little bit later get right there but right back right of this short break [Music] [Music]

I'm Steve looook I am director of data management at associated bank regional bank headquartered out of Green Bay Wisconsin so some IBM cloud pack has really helped enable our organization from an orchestration tool all the way from ingestion of data all the way through consumption and doing data prep and shaping and really helped enable some of our advanced analytics through Python and AI models in our studio for a must' tist achill perspective and really opened our worlds from a use case perspective as well as providing the the governance and controls and rails in place that we need to maintain our data governance self service is a key focus area for us it's it's really around enablement and getting the right people access to the right data when they need it so you know cloud pack is able to help us solution for that and provide them what they need to start to infuse that data into their business processes and applications data obstinate tality for us has helped us change the way we really do business and think it's it's our focus is on scalability and in time to market and in efficiency is is really the focus that that's allowed us to shift do so it's it's how do we enable folks and enable our people process and technologies across the whole stack to to be as scalable and as efficient as as they can be along with that constant improvement mentality that continuous improvement components of DevOps that also brings to the table

>> Live from San Francisco, California, it's theCUBE, covering the IBM Chief Data Officer Summit. Brought to you by IBM. >> We're back at San Francisco. We're here at Fisherman's Wharf covering the IBM Chief Data Officer event #IBMCDO. This is the tenth year of this event. They tend to bookend them both in San Francisco and in Boston, and you're watching theCUBE, the leader in live tech coverage. My name is Dave Valante. John Thomas is here, Cube alum and distinguished engineer, Director of Analytics at IBM, and somebody who provides technical direction to the data science elite team. John, good to see you again. Steve Aliouk is back. He is the Vice President of Deep Learning in the Global Chief Data Office, thanks for comin' on again. >> No problem. >> Let's get into it. So John, you and I have talked over the years at this event. What's new these days, what are you working on? >> So Dave, still working with clients on implementing data science and AI data use cases, mostly enterprise clients, and seeing a variety of different things developing in that space. Things have moved into broader discussions around AI and how to actually get value out of that. >> Okay, so I know one of the things that you've talked about is operationalizing machine intelligence and AI and cognitive and that's always a challenge, right. Sounds good, we see this potential but unless you change the operating model, you're not going to get the type of business value, so how do you operationalize AI? >> Yeah, this is a good question Dave. So, enterprises, many of them, are beginning to realize that it is not enough to focus on just the coding and development of the models, right. So they can hire super-talented Python TensorFlow programmers and get the model building done, but there's no value in it until these models actually are operationalized in the context of the business. So one aspect of this is, actually we know, we are thinking of this in a very systematic way and talking about this in a prescriptive way. So, you've got to scope your use cases out. You got to understand what is involved in implementing the use case. Then the steps are build, run, manage, and each of these have technical aspects and business aspects around, right. So most people jump right into the build aspect, which is writing the code. Yeah, that's great, but once you build the code, build the models by writing code, how do you actually deploy these models? Whether that is for online invocation or back storing or whatever, how do you manage the performance of these models over time, how do you retrain these models, and most importantly, when these models are in production, how do I actually understand the business metrics around them? 'Cause this goes back to that first step of scoping. What are the business KPI's that the line of business cares about? The data scientist talks about data science metrics, position and recall and Area Under the ROC Curve and accuracy and so on. But how do these relate to business KPI's. >> All right, so we're going to get into each of those steps in a moment, but Steve I want to ask you, so part of your charter, Inderpal, Global Chief Data Officer, you guys have to do this for IBM, right, drink your own champagne, dog footing, whatever you call it. But there's real business reasons for you to do that. So how is IBM operationalizing AI? What kind of learnings can you share? >> Well, the beauty is I got a wide portfolio of products that I can pull from, so that's nice. Like things like AI open to Watson, some of the hardware components, all that stuffs kind of being baked in. But part of the reason that John and I want to do this interview together, is because what he's producing, what his thoughts are kind of resonates very well for our own practices internally. We've got so many enterprise use cases, how are we deciding, you know, which ones to work on, which ones have the data, potentially which ones have the biggest business impact, all those KPI's etcetera, also, in addition to, for the practitioners, once we decide on a specific enterprise use case to work on, when have they reached the level where the enterprise is having a return on investment? They don't need to keep refining and refining and refining, or maybe they do, but they don't know these practitioners. So we have to clearly justify it, and scope it accordingly, or these practitioners are left in this kind of limbo, where they're producing things, but not able to iterate effectively for the business, right? So that process is a big problem I'm facing internally. We got hundreds of internal use cases, and we're trying to iterate through them. There's an immense amount of scoping, understanding, etcetera, but at the same time, we're building more and more technical debt, as the process evolves, being able to move from project to project, my team is ballooning, we can't do this, we can't keep growing, they're not going to give me another hundred head count, another hundred head count, so we're definitely need to manage it more appropriately. And that's where this mentality comes in there's-- >> All right, so I got a lot of questions. I want to start unpacking this stuff. So the scope piece, that's we're setting goals, identifying the metrics, success metrics, KPI's, and the like, okay, reasonable starting point. But then you go into this, I think you call it, the explore or understanding phase. What's that all about, is that where governance comes in? >> That's exactly where governance comes in. Right, so because it is, you know, we all know the expression, garbage in, garbage out, if you don't know what data you're working with for your machine learning and deep learning enterprise projects, you will not have the resource that you want. And you might think this is obvious, but in an enterprise setting, understanding where the data comes from, who owns the data, who work on the data, the lineage of that data, who is allowed access to the data, policies and rules around that, it's all important. Because without all of these things in place, the models will be questioned later on, and the value of the models will not realized, right? So that part of exploration or understanding, whatever you want to call it, is about understanding data that has to be used by the ML process, but then at a point in time, the models themselves need to be cataloged, need to be published, because the business as a whole needs to understand what models have been produced out of this data. So who built these models? Just as you have lineage of data, you need lineage of models. You need to understand what API's are associated with the models that are being produced. What are the business KPI's that are linked to model metrics? So all of that is part of this understand and explore path. >> Okay, and then you go to build. I think people understand that, everybody wants to start there, just start the dessert, and then you get into the sort of run and manage piece. Run, you want a time to value, and then when you get to the management phase, you really want to be efficient, cost-effective, and then iterative. Okay, so here's the hard question here is. What you just described, some of the folks, particularly the builders are going to say, "Aw, such a waterfall approach. Just start coding." Remember 15 years ago, it was like, "Okay, how do we "write better software, just start building! "Forget about the requirements, "Just start writing code." Okay, but then what happens, is you have to bolt on governance and security and everything else so, talk about how you are able to maintain agility in this model. >> Yeah, I was going to use the word agile, right? So even in each of these phases, it is an agile approach. So the mindset is about agile sprints and our two week long sprints, with very specific metrics at the end of each sprint that is validated against the line of business requirements. So although it might sound waterfall, you're actually taking an agile approach to each of these steps. And if you are going through this, you have also the option to course correct as it goes along, because think of this, the first step was scoping. The line of business gave you a bunch of business metrics or business KPI's they care about, but somewhere in the build phase, past sprint one or sprint 2, you realize, oh well, you know what, that business KPI is not directly achievable or it needs to be refined or tweaked. And there is that circle back with the line of business and a course correction as it was. So it's a very agile approach that you have to take. >> Are they, are they, That's I think right on, because again, if you go and bolt on compliance and governance and security after the fact, we know from years of experience, that it really doesn't work well. You build up technical debt faster. But are these quasi-parallel? I mean there's somethings that you can do in build as the scoping is going on. Is there collaboration so you can describe, can you describe that a little bit? >> Absolutely, so for example, if I know the domain of the problem, I can actually get started with templates that help me accelerate the build process. So I think in your group, for example, IBM internally, there are many, many templates these guys are using. Want to talk a little bit about that? >> Well, we can't just start building up every single time. You know, that's again, I'm going to use this word and really resonate it, you know it's not extensible. Each project, we have to get to the point of using templates, so we had to look at those initiatives and invest in those initiatives, 'cause initially it's harder. But at least once we have some of those cookie-cutter templates and some of them, they might have to have abstractions around certain parts of them, but that's the only way we're ever able to kind of tackle so many problems. So no, without a doubt, it's an important consideration, but at the same time, you have to appreciate there's a lot of projects that are fundamentally different. And that's when you have to have very senior people kind of looking at how to abstract those templates to make them reusable and consumable by others. >> But the team structure, it's not a single amoeba going through all these steps right? These are smaller teams that are, and then there's some threading between each step? >> This is important. >> Yeah, that's tough. We were just talking about that concept. >> Just talking about skills and >> The bind between those groups is something that we're trying to figure out how to break down. 'Cause that's something he recognizes, I recognize internally, but understanding that those peoples tasks, they're never going to be able to iterate through different enterprise problems, unless they break down those borders and really invest in the communication and building those tools. >> Exactly, you talk about full stack teams. So you, it is not enough to have coding skills obviously. >> Right. What is the skill needed to get this into a run environment, right? What is the skill needed to take metrics like not metrics, but explainability, fairness in the moderates, and map that to business metrics. That's a very different skill from Python coding skills. So full stack teams are important, and at the beginning of this process where someone, line of business throws 100 different ideas at you, and you have to go through the scoping exercise, that is a very specific skill that is needed, working together with your coders and runtime administrators. Because how do you define the business KPI's and how do you refine them later on in the life cycle? And how do you translate between line of business lingo and what the coders are going to call it? So it's a full stack team concept. It may not necessarily all be in one group, it may be, but they have to work together across these different side loads to make it successful. >> All right guys, we got to leave it there, the trains are backing up here at IBM CDO conference. Thanks so much for sharing the perspectives on this. All right, keep it right there everybody. You're watchin' "theCUBE" from San Francisco, we're here at Fisherman's Wharf. The IBM Chief Data Officer event. Right back. (bubbly electronic music)

>> Live from San Francisco, California, it's the Cube, covering the IBM Chief Data Officer Summit, brought to you by IBM. >> Hello, everyone. Welcome to historic Fisherman's Wharf in San Francisco. We're covering the IBM Chief Data Officer event, #IBMCDO. This is the Cube's, I think, eighth time covering this event. This is the tenth year anniversary of the IBM CDO event, and it's a little different format today. We're here at day one. It's like a half day. They start at noon, and then the keynotes. We're starting a little bit early. We're going to go all day today. My name is Dave Volante. Steve Eliuk is here. He's a Cube alum and Vice President of Deep Learning and the Global Chief Data Officer at IBM. And Tim Humphrey, the VP at the Chief Data Office at IBM. Gents, welcome to the Cube. >> Welcome, glad to be here. >> So, couple years ago, Ginni Rometty, at a big conference, talked about incumbent disruptors, and the whole notion was that you've got established businesses that need to transform into data businesses. Well, that struck me, that well, if IBM's going to sell that to its customers, it has to go through its own transformation, Steve. So let's start there. What is IBM doing to transform into a data company? >> Well, I've been at IBM for, you know, two years now, and luckily I'm benefiting from a lot of that transformation that's taken place over the past three or four years. So, internally, getting (mumbling) in order, understanding it, going through various different foundation stones, building those building blocks so that we can gather new insights and traverse through the cognitive journey. One of the nice things though, is that we have such a wide, diverse set of data within the company. So for different types of enterprise use cases that have benefits from AI, we have a lot of data assets that we can pull from. Now, keeping those data assets in good order is a challenging task in itself. And I'm able to pull from a lot of different tools that IBM's building for our customers. I get to use them internally, look at them, evaluate them, give them real practitioner's point of view to ultimately get insight for our internal business practices, but also for our customers in turn. >> Okay, so, when you think about a data business, they've got data at the core. I'm going to draw a, like, simple conceptual picture, and you've got people around it, maybe you've got processes around it. IBM, hundred-plus-year-old company, you've got different things at the core. It's products. It's people. It's business process. So maybe you could talk, Tim, about how you guys have gone about putting data at the center of the universe. Is that the right way to think about it? >> It is the right way to think about it, and I like how you were describing it. Because when you think about IBM, we've been around over a hundred years, and we do business in roughly over 170 countries. And we have businesses that span hardware, software, services, financing. And along the way, we've also acquired and divested a lot of companies and a lot of businesses. So what that leaves you with is a very fragmented data landscape, right? You know, to support regulations in this country, taxes, tax rules in another country, and having all these different types of businesses. Some you inherit. Some are born from within your company. It just leaves a lot of data silos. And as we see transformations being so important, and data is at the heart of that transformation, it was important for us to really be able to organize ourselves such that access to data is not a problem. Such that being able to combine data across disciplines from finance to HR to sales to marketing to procurement. That was the big challenge, right? And to do this in a way that really unlocks the value of the data, right? It's very easy to use somebody like one of my good, smart friends here, Steven Eliuk to develop models within a domain. But when you talk about cross-functional, complex data coming together to enable models, that's like the Holy Grail of transformation. Then we can deliver real business value. Then you're not waiting to make decisions. Then you can actually be ahead of trends. And so that's what we've been trying to do And the thought and the journey that we have been on is build a enterprise data platform. So, take the concept of a data lake. Bring in all your data sources into one place, but on top of that, make it more than just a data lake. Bring the services and capabilities that allow you to deliver insights from data together with the data so we have a data platform. And our Cognitive Enterprise data platform sort of enables that transformation, and it makes people like my good friend here much more productive and much more valuable to the business. >> This sounds like just a massive challenge. It's not just a technology challenge, obviously. You've got cultural. I mean, people, "This is my data." >> Yes. >> (laughs) And I'm referring, Tim, you're talking like you're largely through this process, right? So it first of all is... Can you talk about-- >> Basically, I will say this. This is a journey. You're never done, right? And one of the reasons why it is a journey is, if you're going to have a successful business, your business is going to keep transforming. Things are going to keep changing. And even in our landscape today, regulations are going to come. So there's always going to be some type of challenge. So I like to say, we're in a journey. We're not finished. (laughing) We're well down the path, and we've learned a lot. And one of the things we have learned, you hit on it, is culture, right? And it's a little hard to say, okay, I'm opening things up. I don't own the data. The company owns the data. There is that sort of cultural change that has to go along with this transformation. >> And there are technology challenges. I mean, when I first started in this business, AI was a hot concept, but you needed, like, massive supercomputers to actually make them work. Today, you now see their sort of rebirth. You know, (mumbling) talks about the AI winter, and now it's like the AI spring. >> Yeah. >> So how are you guys applying machine intelligence to make IBM a better business? >> Well, ultimately, the technology is really, basically transitioned us from the Dark Ages forward. Previously in the supercomputer mentality, didn't fit well for a lot of AI tasks. Now with GPUs and accelerators and FBGAs and things like that, we're definitely able, along with the data and the curated data that we need, to just fast-track. You know, the practitioners would spend an amazing amount of time gathering, crowdsourcing data, getting it in good order, and then the computational challenges were tough. Now, IBM came to the market with a very interesting computer. The POWER8 and POWER9 architecture has NVLink, which is a proprietary Nvidia, interconnect directly to the CPU. So we can feed GPUs a lot quicker for certain types of tasks. And for certain types of tasks that could mean, you know, you get to market quicker, or we get insights for enterprise problems quicker. So technology's a big deal, but it doesn't just center around GPUs. If you're slow to get access to the data, then that's a big problem. So the governance (mumbling) aspects are just as important, in addition to that, security, privacy, et cetera, also important. The quality of the data, where the data is. So it's and end-to-end system, and if there's any sort of impedance on any of it, it slows down the entire process. But then you have very expensive practitioners who are trying to do their job that are waiting on data or waiting on results. So it's really an end-to-end process. >> Okay, so let's assume for a second the technology box is checked. And again, as you say, Tim, it's a journey, and technology's going to continue to evolve. But we're at a point in technology now where this stuff actually can work. But what about data quality? What about compliance and governance? How are you dealing with the natural data quality problem? Because I'm a PNL manager. I'm saying, well, we're making data decisions, but if I don't like the decision, I'm going to attack the quality of the data. (laughing) So who adjudicates all that, and how have you resolved those challenges? >> Well, I like to think of... I'm an engineer by study, and I just like to think of simple formulas. Garbage in, garbage out. It applies to everything, and it definitely applies to data. >> (laughs) >> Your insights, the models, anything that you build is only going to be as good as the data foundation you have. So one of the key things that we've embarked on a journey on is, how do we standardize all aspects of data across the company? Now, you might say, hey, that's not a hard challenge, but it's really easy to do standards in a silo. For this organization, this is how we're going to call terms like geography, and this is how we'll represent these other terms. But when you do that across functions, it becomes conflict, right? Because people want to do it their own way. So we're on the path of standardizing data across the enterprise. That's going to allow us to have good definitions. And then, as you mentioned earlier, we are trying to use AI to be able to improve our data quality. One of the most important things about data is the metadata, the data that describes the data. >> Mm-hm. >> And we're trying to use AI to enhance our metadata. I'd love for Steven to talk a little bit about this, 'cause this is sort of his brainchild. But it's fascinating to me that we can be on a AI transformation, data can be at the heart of it, and we can use AI (laughs) to help improve the quality of our data. >> Right. >> It's fascinating. >> So the metadata problem is (mumbling) because you've talked about data length before. Then in this day and age, you're talking schema lists. Throw it into a data lake and figure out because you have to be agile for your business. So you can't do that with just human categorization, and you know, it's got to-- >> It could take hours, maybe years. >> For a company the size of IBM, the market would shift so fast, right? So how do you deal with that problem? >> That's exactly it. We're not patient enough to do the normative kind of mentality where you just throw a whole bunch of bodies at it. We're definitely moving from that non-extensible man count, full-time-employee type situation, to looking for ways that we can utilize automation. So around the metadata, quality and understanding of that data was incredibly problematic, and we were just hiring people left, right, and center. And then it's a really tough job that they have dealing with so many different business islands, et cetera. So looking for ways that we could automate that process, we finally found away to do it. So there's a lot of curated data. Now we're looking at data quality in addition to looking at regulatory and governance issues, in addition to automating the labeling of business metadata. And the business metadata is the taxonomy that everything is linked together. We understand it under the same normative umbrella. So then when one of the enterprise use cases says, "Hey, we're looking for additional data assets," oh, it's (snaps) in the cloud here, or it's in a private instance here. But we know it's there, and you can grab it, right? So we're definitely at probably the tail end of that curve now, and it started off really hard, but it's getting easier. So that's-- >> Guys, we got to leave it there. Awesome discussion. I hope we can pick it up in the future when maybe we have more metadata than data. >> (laughs) >> And metadata's going to become more and more valuable. But thank you so much for sharing a little bit about IBM's transformation. It was great having you guys on. >> Thank you. >> Alright, keep it right there, everybody. We'll be back with our next guest right after this short break. You're watching the Cube at IBM CDO in San Francisco. Right back. (electronic music) >> Alright, long clear. Alright, thank you guys. Appreciate it, I wish we had more time.

>> from San Matteo. It's the Cube covering Scaler. Innovation Day. Brought to You by Scaler >> The Run Welcome to this special on the Ground Innovation Day. I'm John for a host of The Cube. We're here at scale. His headquarters in San Mateo, California Hardest Silicon Valley. But here the cofounder and CEO Steve, It's Irwin Ski and Jeff Low product marketing director. Thanks for having us. Thanks for having us. Thank you. But a great day so far talked Teo, the other co founders and team here. Great product opportunity. You guys been around for a couple of years, Got a lot of customers, Uh, just newly minted funded syriza and standard startup terms. That seems early, but you guys are far along, you guys, A unique architecture. What's so unique about the architecture? >> Well, thinks there's really three elements of the architecture's designed that I would highlight that differentiates us from our competitors. Three things that really set us apart. I think the biggest the 1st 1 is our use of a common our database. This is what allows us to provide a really superior search experience even though we're not using keyword indexing. Its purpose built for this problem domain and just provides us with great performance in scale. The second thing I would highlight would be the use of well, essentially were a cloud native solution. We have been architected in such a way that we can leverage the great advantage of cloud the scale, ability that cloud gives you the theological city. That cloud gives you andare. Architecture was built from the ground up to leverage that, uh and finally I would point out the way that we do our data. Um, the way that we don't silo data by data type, essentially any type of observe ability, data, whether it's logs or tracing or metrics. All that data comes into this great platform that we were in that provides a really great superior query performance over, >> and we talked earlier about Discover ability. I want to just quickly ask you about the keyword indexing and the cloud native. To me, that seems to be a two big pieces because a lot of the older all current standards people who are state of the art few years ago, 10 years ago, keyword index thing was a big part of it, and cloud native was still emerging except for those folks that were born the clouds. So >> this is a dynamic. How important is that? Oh, it's It's just critical. I mean, here, when we go to the white board, I love to talk about this in a little more detail in particular. So let's let's talk about keyword indexing, right? Because you're right. This is a lot of the technology that people leverage right now. It's what all of our competitors do in keyword indexing. Let's let's look at this from the point of view of a log ingestion pipeline. So in your first stage, you have your input, right? You've got your raw logs coming in. The first thing you do after that typically is parse. You're goingto parse out whatever fields you want from your logs. Now, all of our competitors, after they do that, they do in indexing step. Okay, this has a lot of expense to it. In fact, I'm going to dig into that after the log content is index. It's finally available for search. Where will be returned as a search result. Okay, this one little box, this little index box actually has a lot of costs associated with it. It contributes to the bloat of storage. It contributes to the cost of the overall product. In fact, that's why I love our competitors. Charge you based on how much you're indexing now, even how much you're ingesting. When you look at the cost for indexing, I think you can break it down into a few different categories. First of all, building the index. There's certain costs with just taking this data, building the index and storing it. Computational storage, memory, everything okay, But you build the index in order to get superior query performance, Right? So that kind of tells you that you're going to have another cost. You're going tohave an optimization cost. Where the index is that you're building are dependent on the queries that your users want to conduct, right, because you're trying to make sure you get as good of query performance as possible. So you have to take a look at the career. Is that your user performing the types of logs that you're coming in and you have to decide what indexing that you want to do? Okay. And that cost is shouldered by the burden of the customers. Um, okay, but nothing static in this world. So at some point your logs are going to change. The type of logs here in Justin is going to change. Maybe your query is goingto change. And so you have another category of costs, which is maintenance, right? You're going to have to react to changes in your infrastructure. It's used the type of logs you're ingesting, and basically, this is just creates a whole big loop where you have to keep an eye on your performance. You have to be constantly optimizing, maintaining and just going around in the circle. Right? And for us, we just thought that was ridiculous because all this costs is being born by the customer. And so when we designed the system, we just wanted to get rid of that. >> That's the classic shark fin. You see a fin on anything great whites going to eat you up or iceberg. You see that tip you don't see what's underneath? This seems to be the key problem, because the trend is more data. New data micro services gonna throw off new data type so that types is going up a CZ. Well, that's what that does that consistent with what you got just >> that's consistent. I mean, what we hear from our customers is they want flexibility, right? These are customers that are building service oriented, highly scalable applications on top of new infrastructure. They're reacting to changes everywhere, so they want to be able to not have to, you know, optimize their careers. They're not goingto want to maintain things. They just want to search product that works. That works over everything that they're ingesting. >> So, good plan. You eliminate that fly wheel of cost right for the index. But you guys, you were proprietary columnist, Or that's the key on >> your That's a Chiana and flexibility on data types. Yes, it does. And here, let me draw a little something to kind of highlight that because, you know, of course, it's a it begs the question. Okay, we're not doing keyword indexing. What do you do? What we do actually is leverage decades of research and distribute systems on commoner databases, and I'll use an example on or two >> People know that the data is, well, that's super fast, like a It's like a Ferrari. >> Yes, it's a fryer because you're able to do much more targeted essentially analysis on the data that you want to be searching over, right? And one way to look at this is, uh, no, Let's take a look at ah, Web access lock. Okay. And when we think about this and tables, we think that each line in the table represents, ah, particular entry from the access log. Right. And your columns represent what fields you've extracted. So for example, one the fields you might extract is thie HP status code. You know, Was it, um, a success or not? Right. Or you might have the your eye, or you might have the user agent of the incoming web request. Okay. Now, if you're not using a commoner database approach to execute a quarry where you're trying to count the number of non two hundreds that you've your Web server has responded with, you'd have to load in all the data for this >> table, right? >> And that's just its overkill in a commoner database. Essentially, what you do is you organize your data such that each column essentially has saved as a separate file. So if I'm doing a search where I just want to count the number of non two hundreds. I just have to read in these bites. And when your main bottleneck, it's sloshing bites in and out of Main Ram. This just gives you orders of magnitude better performance. And we've just built this optimize engine that does essentially this at its core and doesn't really well, really fast leveraging commoner database technology. >> So it lowers the overhead. You have to love the whole table in. That's going to take time. Clearing the table is going to take time. That seems to be the update. That's exactly right. Awesome, right? Okay. All right, Jeff. So you're the director of product marketing. So you got a genius pool of co founders here? Scaler. Been there, done that ball have successful track records as tech entrepreneurs, Not their first rodeo, making it all work. Getting it packaged for customers is the challenge that you guys have you been successful at it? What does it all mean? >> Yeah, it essentially means helping them explore and discover their data a lot more effectively than they happen before, you know, With applications and infrastructure becoming much more complex, much more distributed, our engineering customers are finding it increasingly difficult to find answers And so all of this technology that we've built is specifically designed to help him do that at much greater speed, Much greater ease, much more affordably and at scale. We always like to say we're fast, easy, affordable, at scale. >> You know, I noticed in getting to know you guys and interviewing people around around company. The tagline built by engineers for engineers is interesting. One. You guys are all super nerdy and geeky, so you get attacked and you take pride in the tech in the code. But also, your buyers are also engineers because they're dealing with cloud Native Wholenother Dev ops, level of scale where they love scale people in that market love infrastructures code. This is kind of the ethos of that market, but speed scale is what they live for, and that's their competitive advantage in most cases. How do you hit that point there? What's the alignment with the customers on scale and speed? >> Yeah, you know, with the couple of things that Stephen had mentioned, you know, the columnar database on DH, he mentioned cloud native. We like to refer to that as massively parallel or true multi tendency in the cloud those 11 two things give us really to key advantages when it comes to speed. So speed on in just that goes back to what Steven was talking about with the column. In our database, we're not having a weight to build the index so weakening unjust orders of magnitude faster than traditional solutions. So whereas a conventional solution might taking minutes even up to hours to ingest large sets of data, we can literally do it in seconds. It's the data's available immediately for used in research. One of our customers, in fact, that I'm thinking of down Australia actually uses our live tail because it actually works and as they push code out to production that can actually monitor what happens and see if the changes are impacting anything positively or negatively >> and speed two truths, a tagline the marking people came up with, which is cool. I love that kind of our fallouts. We have to get the content out there and get that let the people decide. But in your business, ingestion is critical. Getting the ingestion to value time frame nailed down is table stakes. People engineers want to test stuff. It doesn't work out of the box we ingest and they don't see value. They're not gonna kind of be within next levels. Kind of a psychology of the customer. >> Yeah, You know, when you're pushing code, you know, on an hourly basis, sometimes even minutes now, the last thing you want to do is wait for your data to analyse it, especially when a problem occurs. When a problem occurs and it's impacting a customer or impacting your overall business. You immediately go into firefighting mode, and you just can't wait to have that data become available so that speed to ingest becomes critical. You don't want to wait. The other aspect on the speed topic is B to search. So we talked about the types of searches that are calling. Our database affords us a couple that, within massively parallel and true multi tendency approach, basically means that you could do very, very ad hoc searches extremely quickly. You don't have to bill the keyword index. You don't have to have two, even build a query or learn how to build queries on DH, then run and then wait for it. And maybe in the meantime, wait to get a coffee or something like that. >> I mean, we grew up in Google search. Everyone who's exactly the Web knows what searches and discoveries kind the industry word in discovering navigation. But one of the things about searches about that made Google say Greg was relevance. You guys seem to have that same ethos around data discover, ability, speed and relevance. Talk about the relevance piece, because I think that, to me is what is everyone's trying to figure out as more data comes in? You mentioned some of the advantages Steven around, you know, complexity around data types. You know, Maur data types are coming on, so Relevance sees, is what everyone's chasing. >> So one of >> the things that I think we are very good at is helping people discover what is relevant. There are solutions out there. In fact, there's a lot of solutions out there that will focus on summarizing data, letting you easily monitor with a set of metrics, or even trace a single transaction from point A to point B through a set of services. Those are great for telling you that there is a problem or that problem exist. Maybe in this one service, this one server. But where we really shine is understanding why something has happened. Why a problem has occurred. And the ability to explore and discover through your data is what helps us get to that relevancy. >> Ameren meeting Larry and Sergey back into 1998. And you know, from day one it's fine. What you looking for him? And they did their thing. So I want to just quickly have you guys explain it. I think one thing that also has come up love to get your take on it, guys, is multi tendency urine in the clouds to get a lot of scale. We're out of resource talk about the debt. Why multi tendency is an important piece and what does that specifically mean? But the customer visa be potentially competitive solutions. And what do you guys bring for the tables? That seems to be an important discussion Point >> sure know. And it is one of the key piece of our architecture. I mean, when we talk about being designed for the cloud, this is a central part of that right? When you look at our competitors, for the most part, a lot of them have taken existing open the source off the shelf technologies and kind of taking that and shoved it into this, you know, square hole of, you know, let's run in the cloud, right? And so they're building. These SAS services were essentially they pretend like everyone's got access to a lot. Resource is but under the covers there, sitting there, spinning up thes open source solutions. Instances for each of the customers each of these instances are on ly provisioned with enough ramsi pew for that customer's needs, right? And so heaven forbid you try to issue more crews than you normally do or try to use Mohr you know, storage than you normally do, because your instance will just be capped out, right? Um, and also it's kind of inefficient in that when your users aren't issue inquiries, those CPU and RAM researchers are just sitting there idle instead, what we've done is we've built a system where we essentially have a big pool of resource is we have a big pool of CPU, a big pool of ram, a big pool of disc. Everyone comes in, get access to that, so it doesn't matter what customer you are. Your queries get full access to all these si pues that we have run around right? And that's that's the core of multi tendency is that we're able to not provision for just one look for each individual customer. But we have a big pool of resource is that everyone gets the >> land that's gonna hit the availability question on. And it's also have a side effect for all those app developers who want to build a I and stuff used data and build these micro services systems. >> They're going to get >> the benefit because you have that closed loop. Are you fly? Will, if you will. >> Yeah, yeah, the fight could just add the multi tendency really gives us a lot of economies of scale, both from, you know, the over provisioning and the ability to really effectively use resources. We also have the ability to pass those savings on to our customers. So there's that affordability piece that I think is extremely important. Find answers, this architectural force that >> Stephen I want to ask you because, you know, I know the devil's work pretty well. People are they're hard core, you know. They build their own stuff. They don't want us, have a vendor. Kuo. I can do this myself. There's always comes up there. But this use cases here. You guys seem to be doing well in that environment again. Engineering led solution, which I think gives you guys a great advantage. But what's the How do you handle the objection when you hear someone say, Well, I could do it. Just go do it myself. >> What I always like to point at is, yes, you can up to a decree, right? We often hear people that use open source technologies like elk. They can get that running and they can run it up to a certain scale like a you know, tens of gigabytes per day of logs. They're fine, right? But with those technologies, once it goes above a certain scale, it just becomes a lot more difficult to run. It's one those classic things you know, getting 50% of the way. There is easy getting 80% of the way. There is a lot harder. Getting 100% is almost impossible, right? And you, as whatever company that that that you're doing whatever product you're building, do you really want to spend your engineer? Resource is pushing through that curve, getting 80%. 100% of kind of good, a great solution. No, what we always pitches like Look, we've always solve these problems. These hard problems for this problem, too may come and leverage our technology. You don't have to spend your engineering capital on that. >> And then the people who are doing that scale that you guys provide, they want, they need those engineering resource is somewhere else. So I have to ask, you just basically followed question. Which is how does the customer know whether they have a non scaleable for scaleable solution? Because some of these SAS services air masquerading as scaleable solutions. >> No, they are. I mean, we we actually encourage our customers when they're in the pre sale stage to benchmark against us. We have ah customer right now that sending us terabytes of data per day as a trial just to show that we can meet the scale that they need. We encourage those same customers to go off and ask the other competitors to do that. And, you know, the proof is in the pudding. >> And how's the results look good? Yeah. So bring on the ingest Yes, that's that's That's the sales pitch. Yes, guys, thanks so much for sharing the inside. Even. Appreciate it, Jeff. Thanks for sharing. Appreciate it. I'm John for the Cube. Here for a special innovation Days scales >> headquarters in the heart of >> Silicon Valley's sent Matteo California. Thanks for watching.

(upbeat music) >> Live from Las Vegas, it's theCUBE covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We're joined by Steven Guggenheimer, he is the corporate vice president of AI and ISV engagement at Microsoft. Thank you so much for coming on theCUBE. >> Sure, thanks for having me. >> So one of the things that we're hearing so much at this conference is, "data needs AI but AI needs data." I'm wondering from your perspective, AI engagement, where do you come down on this? What are you hearing? what are your thoughts on that big theme? >> Um, well, data is the -- some people say the oil for AI, pick your terminology, but there is no AI without data. The reason that AI is such a hot topic right now is the combination of sort of compute storage and networking at scale, which means the access for developers and data scientists to work with large sets of data and then the actual data. If you don't have data you can't build models, if you can't build models, that's what is the definition of AI. So you need data. I always-- all the coaching I do is about sort of, BI before AI. If you can't actually get insight out of your data, let's not try to add intelligence. If you can't get insight out of your data, it means your data is not in a good-- your data state is not in order. So data first. >> A lot of architectural work is being done on data. I see a horizontally scalable cloud, gives a nice access to a lot of different you know, observational data sets. >> Yeah >> It used to be give the guy the silo, got the data, go get more data, slower. Now, data feeds the developer process because SaaS business models have been proven that data and SaaS work well together. So how do we get more-- what's the sequence of architecture to usability of data so that not only can you just have analytical systems, but where developers can start building their SaaS apps with data? >> Yeah, I mean we have this notion where we often talk about sort of, blades or feedback loops. There's sort of four or five things most companies do. You work with customers, you have employees, you have a supply chain or some type of partner chain, You run your finance and operations. So the question becomes, in each of those processes, there's data. Human-generated forms over data or pick your loop and now you getting tons and tons of data. The trick now is to make it reusable. Mostly what we've done for years, form over data, take the data, form over data. And what we do is we get all these different databases. We try and create some layer that brings it all together. We build cubes out of it to view and then we get this hopeless spaghetti. So the trick right now, we're working on something called Common Data Model, which others are well, or Common Data Service. Let's get the entities lined up from the very beginning. We've worked with Adobe and SAP on the Open Data Initiative. Let's start at the core, let's make the data layer reusable, We're you know, databases have become data warehouses have become data lakes. We're heading towards a data tidal wave, and if we don't get the data estate in order to run the line of business applications, to feed all of the things we do to use the ML and AI on top of it, we're going to drown in data and not get what we want out of it. So, architecturally I think about the Common Data Model and the Common Data Service both generically by industry, we build accelerators for that, getting the big organizations like the three I mentioned aligned around that, making it such that any, you know, organization can build from that and then building on top of that. For big companies you have to decide, what do I keep and what do I throw out? You know, what do I just give up on and start from fresh? What do I actually clean? Where do I use tools from Informatica or others to help me clean it, secure it? But you've got to put all that thought in. >> You know we were chatting before we came on camera about the internet days and the storied history that you had at Microsoft. And during the internet, search was the big application. And search on the internet actually worked really well because they didn't have a legacy. And the people that tried to crack the code on search inside an enterprise, much harder problem (Giggles). Because of the database things you mentioned. How does today's enterprise get the benefit of SaaS as if they were cloud-native SaaS with the data? So you know, the challenge we're hearing here is having a Common Data Model is all great, but I just want to be a SaaS player, I want to use my data to feed into my business value. How does a company move out of those legacy constraints? What do you see as-- >> Well there's different paths that different companies will take. I mean, the good news is that if you get your data in order to do what you said, then whether you build, buy or partner for the SaaS services, you can use that data underneath and you should be feeding it back in and making it such that it's sort of reusable and the pipeline is consistent. The truth is on all this, it's just going to end up infused anyway. When you used the internet, which is a funny analogy 'cause I remind people, you know, when the internet came out we had internet products, we had internet events, we had internet shows. We don't have any of that anymore. It's just woven into everything we do. AI is going to be the same. You have all this hype right now, you have AI shows, you have, you know, AI groups. The truth is, in 10, 15 years, AI it's just going to be woven into everything. The data is going to be set up for that. >> So what's the misconception on AI? 'Cause, first of all, I love the fact that AI is hyped up because my kids love it. Machine learning they learn because they hear about AI and they hear all this coolness. So machine learning goes hand-in-hand with AI, you feed machine learning, machine learning feeds the AI application. But a lot of people have aspirations around AI. Some of them are ungettable and so that's probably a misalignment around the hype. What's your feeling of where the reality is and what's the misconceptions, how should people approach AI? Any thoughts there. >> I think a lot about the AI journey, the first year we were having these AI conversations, we talked about AI for everybody, just go play. Now the conversation is, I call it pragmatic AI. Look, lets talk about, you know, how you want to think about AI, it's going to end up everywhere, so the question becomes, what's your differentiation as a company, and how is AI going to support it? Like any other new technology, in the beginning, people just want to play. Just because you can -- let's just say just you can build a virtual agent, doesn't mean every company should. So the question becomes, first off, BI before AI, get your data state in order. Second, in a build buy partner model, what's your differentiation as a company? Whether you want to use either your unique data or your unique skill sets to use AI against that differentiation to help you grow. Otherwise, like, expect somebody else to have infused AI into the products you buy, the SaaS services, you know, use that, then build whatever you want and then there's, you know, if you think you're going to build a new business based on your unique data or your unique AI capabilities, great, let's have that conversation, we need that too but rarely does that become the state. so, most of the conversations move from, you know, the hype to okay, let's get pragmatic which is why I always come back to data first 'cause if you not doing that, you're not setting up for the long run. Let's build for the long run, then let's just have a business conversation like, how do you differentiate yourself as a business? Okay, how is this tool going to help you? >> I want to ask about, uh about innovation, and particularly because Microsoft is a company that's now entering its middle age (giggling) and-- >> What does that say about me, oh no >> As one of famously innovative company, but how do you stay on the cutting edge? I mean, I'm wondering internally how you think about AI for Microsoft's business purposes. What are the conversations around AI? >> One of such is, core conversations around this notion of tech intensity you know, from where we focus on how we think about things we think about tech intensity against different areas, AI being one of those. Look, AI is really this interesting thing. I would say we're plumbers by trade, we build software plumbing for others. So, we do three things right, with AI. Basically, there's a layer growing on top of the core development stack, compute, storage, networking for AI. So we're building a layer, cognitive services, bot services, machine learning, set of tools for developers to infuse AI into things that they've built, so that's thing number one. Thing number two, is we infuse AI into our own products, into Windows, into Office, into Azure, into dynamics. You don't see it, we don't talk about it, we don't say Microsoft Windows Inking brought to you by Azure AI. It just works, but our inking works, our face login works, oh, you know, I can -- it's helping me write a better resume in LinkedIn, that's all AI behind the scenes. Now, the third thing you think about then is, "how do you actually use AI to run the business better"? So, how do you think about, AI assisting professionals, how do we think about the, how we do mocking better, How we forecasting sales, so AI is about plumbing, let's build a platform for others, let's use it ourselves on our own products, and then let's think about how you actually use it to run the company better. And that's how we think about it-- >> That's pragmatic >> Very pragmatic AI is kind of -- >> Yeah, that's how I think about it and we, you know, it's interesting 'cause back to the tech intensity point, we get together on an AI conversation, we searching with the senior leadership team about once every other week, and we're round robin between a research topic, the platform and one of the solutions. So it's, you're always getting constant feedback about is the platform doing what we need to build solutions? Is the research feeding the platform? So, you're getting this really nice feedback loop right now and that tech intensity. >> Quality data always has been a big part of the data modeling in the past, Cloud now allows for data marketplaces I've seen sharing of data as a dynamic, almost like sharing libraries of your developer back in the day, so data is now being merchandised in a new way. This is a trend, what's your thought on it? Because if this continues, you're going to have more data inputs, does that-- >> Err, there are places where data is aggregated and potentially can be re-used. We can -- Bing is an example, Google would be an example um, I know people who aggregate data for different industries, etcetera. It's not an easy business, the rules and rights around data, the GPR compliance, the rest of it. I think there's a deer there but you really have to be in the business for-- the trick you run into is, if you're going to be an aggregator, and then a reseller of data, where's that data coming from? What are the rights, what's the security? And then, are the people who are providing that data comfortable with their competitors getting the data? 'cause if you're really going to be a data provider marketplace, first person who's going to want on is the competitor, so, I think it's an interesting conversation, I think it's kind of growing and there's some real good work there, I don't think it's as-- >> not viable yet >> Easily to do it at scale, for as many people who think they have the data asset as believed they do. But that's Steve's view, that's not a Microsoft's statement. (laughing) >> good disclaimer >> Steve's view, so I want to hear Steve's view on the skills gap, this is a huge problem in the technology industry, as so few people to fill roles. How's Microsoft dealing-- what's your view-- >> my view is I'm glad I work at Microsoft, 'cause we spend a lot of energy on that, um, I wish there were a single solution, but we have Minecraft for education, starting with kids, how do you help, you know, Minecraft is this great tool that teachers use help kids get started, so that's a tool set we work on something called tills, which is uh, basically, our developers teach school kids remotely, junior, high school level, you know, coding. Um, we have made investments against this, we have online training, you know, we work with universities. I don't know the perfect answer, um, but I do know we invest and we work with Hadi Partovi and his group on code.org, I mean any place that there is work going on, we work with the military for people coming out of the military service. So we're heavily invested. I'm hopeful that the ease of use of some of the tools and just from a job area, it drives people but I don't know the perfect answer. Steve's view is I don't know the answer, I do know we try every trick in the book-- >> Multipronged attack >> I'm a parent of two kids, like I have my daughter, you know, working on more on the tech side and you know, it's hard to keep kids on a track for that-- >> There's no degree yet, but we had a first degree this year, graduated from the school but there's kind of like a skills portfolio of different things depending on the make-up I mean, domain expertise is critical, if you don't know what you're tryna do, that's -- >> I think we got a mix, because what you're starting to see is, the tools for subject matter experts, are getting better, we have something called the power platfrom, which allows people who aren't necessarily coders by trade, but want to be able to build, you know, sort of apps or services to be able to do that more easily and mix their subject matter expertise. And you see many more people come out of any program, take biology, with sort of computer knowledge to a decent level. AI and ML research, different area, hard skills gap right there >> Steve, great insights, thanks for spending some time with us, great insights on the skills gap and just overall >> thanks for coming on theCUBE >> We didn't talk about rugby, but okay, fine. Thanks, next time >> next time >> You're one of those ballsmen >> we'd track you down >> The ballsmen can throw >> Exactly, shout out to them >> There we go, >> thank you >> Ah, you are watching theCUBE we'd come right back with more from Informatica World I'm Rebecca Knight for John Furrier, stay tuned (upbeat music)