(bright music) >> Hello cloud computing friends and welcome back to theCUBE, where we are live from Las Vegas, Nevada, here at AWS re:Invent all week. My name is Savannah Peterson, very excited to be joined by Paul Gillan today. How are you doing? >> I'm doing great, Savannah. It's my first re:Invent. >> I was just going to ask you >> So it's quite an experience. >> If you've ever been to re:Invent. >> It's dazzling much like the sequins on your top. It's dazzling. >> Yes. >> It's a jam packed affair. I came to the COMDEX Conference for many years in Las Vegas, which was huge event and this really rivals it in terms of these crowd sizes. But I think there's more intensity here. There's more excitement. People are just jazzed about being here to the extent that I never saw at other computer conferences. >> I thought I would agree with you. It's my first re:Invent as well. I'm glad we could share this experience together. And the vibe, the pulse, I think being back in person is really contagious as well. Ooh, maybe the wrong word to use, but in a great way. The energy is definitely radiating between people here. I'll watch my words a little bit better. >> And in person we have with us Samuel Nicholls, the director of public cloud at Global Product Marketing at Veeam Software. Sam, is it Sam or Samuel? >> Depends if I'm in trouble, Paul. >> Savannah: But it depends on who's saying it out loud. >> Yeah, yeah. It's typically, Samuel is usually reserved for my mother, so- >> Yeah. >> (laughs) Well, Sam, thanks for joining us. >> We'll stick with Sam on the show. >> Yeah. >> So Veeam been a red hot company for several years. Really made its, uh, its reputation in the VMware world. Now you've got this whole-sail shift to the cloud, not that VMware is not important still, but how is that affecting, you're shifting with it, how is that affecting your role as a product manager and the business overall? >> Yeah, it's a fantastic question. Obviously Veeam was pioneered in terms of being the purpose-built backup and recovery company for VMware. And as these workloads are being transitioned from the data center into the cloud or just net new workloads being created in the cloud, there is that equal need for backup and recovery there. So it's incredibly important that we were able to provide a purpose-built backup and recovery solution for workloads that live in AWS as well. >> Paul: And how different is it backing up an AWS workload compared to a VMware workload? >> I think it depends on what kind of service a user is, is, is utilizing, right? There's infrastructure as a service, platform as a service, software as a service. And given the differences in what is exposed to that customer that can make backup and recovery quite challenging. So I would say that the primary thing that we want to look at is utilizing native snapshots is our first line of defense when it comes to backup and recovery, irrespective of what workload that right might be whether it's a virtual machine, Amazon EC2, some sort of database on Amazon RDS, a file share, so on. >> Savannah: I bet you're seeing a lot across verticals and across the industry given the support that you're giving customers. What are you seeing in the market and in customer environments? What are some of those trends? >> So I think the major trends that we highlight in our data protection trend support, which is a new update is coming very shortly in the new year, is- >> Savannah: We have to check that out. >> Yeah, absolutely. The physical server is on a decline within the data center. Virtualized workloads, namely VMware is relatively static, kind of flat. The real hockey stick is with the cloud workloads. And as I mentioned before, that is partially because workloads are being transitioned from physical to virtual machines to being cloud hosted but also we're creating more applications and the cloud has become lead de facto standard for new workloads. So you hear about cloud first initiatives, digital transformation, the cloud is central to that. >> You mentioned snapshotting, which is a relatively new phenomenon, although it's taken a hold rapidly, how does snapshotting work in the cloud versus in on your on-prem environment? >> Samuel: It's not wildly different at all. I think the snapshots is again, a great first line of defense for helping users achieve very low recovery point objectives. So the frequency that they can protect their data as well as very low recovery time objectives, how quickly that I can recover the data. Because that's why we're backing up, right? We need the ability to recover. However, snapshots certainly have their limitations as well. They are not independent of the workload that is being protected. So if there were to be some sort of cybersecurity event like ransomware that is prolific throughout pretty much every business, every vertical. When that snapshot is not independent, if the production system becomes compromised that snapshot's likely to be compromised as well. And then going back to the recovery piece, not going to have something to recover from. >> And it's not a one and done with ransomware. >> No. >> It's, yeah. So how, so what is the role that backup plays? I mean a lot of people, I feel like security is such a hot topic here in the show and just in general, attacks are coming in unique form factors for everyone. I mean, I feel like backup is, no pun intended, the backbone of a system here. How does that affect what you're creating, I mean? >> Yeah, absolutely. I think, like you say the backup is core to any comprehensive security strategy, right? I think when we talk about security, everyone tends to focus on the preventative, the proactive piece, stopping the bad guys from getting in. However, there is that remediative aspect as well because like you say, ransomware is relentless, right? You, you as a good guy have to pretty much fend off each and every single attack that comes your way. And that can be an infinite number of attacks. We're all human beings, we're fallible, right? And sometimes we can't defend against everything. So having a secure backup strategy is part of that remediative recovery component for a cybersecurity strategy is critical. And that includes things like encryption, immutability, logical separation of data and so forth. >> Paul: We know that ransomware is a scourge on-premises, typically begins with the end users, end user workstation. How does ransomware work in the cloud? And do the cloud providers have adequate protections against ransomware? Or can they? >> Samuel: Yeah, it's a, it's a fantastic question as well. I think when we look at the cloud, one of the common misconceptions is as we transition workloads to the cloud, we are transitioning responsibility to that cloud provider. And again, it's a misconception, right? It is a shared responsibility between the cloud provider in this case, AWS and the user. So as we transition these workloads across varying different services, infrastructure, platform, software as a service, we're always, always transitioning varying degrees of responsibility. But we always own our data and it is our responsibility to protect and secure that data, for the actual infrastructure components, the hardware that is on the onus of the cloud provider, so I'd say that's the major difference. >> Is ransomware as big a threat in the cloud as it is on-prem? >> Absolutely. There's no difference between a ransomware attack on-premises or in the cloud. Irrespective of where you are choosing to run your workloads, you need to have that comprehensive cybersecurity strategy in order to defend against that and ultimately recover as well if there's a successful attempt. >> Yeah, it's, ooh, okay. Let's get us out at the dark shadows real quick (laughs) and bring us back to a little bit of the business use case here. A lot of people using AWS. What do you think are some of the considerations, they should have when they're thinking about this, thinking about growing their (indistinct)? >> Well, if we're going to stick down the dark shadows, the cybersecurity piece. >> We can be the darkness. >> You and me kind of dark shadows business. >> Yeah, yeah. >> We can go rainbows and unicorns, nice and happy if you like. I think there's a number of considerations they need to keep up. Security is, is, is number one. The next piece is around the recovery as well. I think folks, when they, when we talk about backup and recovery, the focus is always on the backup piece of it. But again, we need to focus on why we're doing the backup. It's the recovery, it's the recovery component. So making sure that we have a clean verifiable backup that we're able to restore data from. Can we do that in a, in efficient and timely manner? And I think the other major consideration is looking at the entirety of our environments as well. Very few companies are a hundred percent sole sourced on a single cloud provider. It is typically hybrid cloud. It's around 80% of organizations are hybrid, right? So they have their on-premises data and they also have workloads running in one or multiple clouds. And when it comes to backup and recovery of all of these different infrastructures and environments, the way that we approach it is very different. And that often leads to multiple different point products from multiple different vendors. The average company utilizes three different backup products, sometimes as many as seven and that can introduce a management nightmare that's very complex, very resource intensive, expensive. So looking at the entirety of the environment and looking to utilize a backup provider that can cover the entirety of that environment while centralizing everything under a single management console helps folks be a lot more efficient, a lot more cost effective and ultimately better when it comes to data protection. >> Amazon and all cloud providers really are increasingly making regions transparent. Just at this conference, Amazon introduced failover controls from multiple multi-region access points. So you can, you can failover from one access from one region to another. What kind of challenges does that present to you as a backup provider? >> I don't think it represents any challenges. When we look at the native durability of the cloud, we look at availability zones, we look at multi-region failover. That is, that durability is ultimately founded on, on replication. And I wouldn't say that replication and backup, you would use one or the other. I would say that they are complimentary. So for replication, that is going to help with the failover scenario, that durability component. But then backup again is that independent copy. Because if we look at replication, if let's say the source data were to be compromised by ransomware or there was accidental deletion or corruption, that's simply going to be copied over to the target destination as well. Having that backup as an independent copy, again compliments that strategy as well. >> Paul: You need it in either, in any scenario. >> Samuel: In any scenario. >> I think the average person would probably say that backup is not the most exciting technology aspect of this industry. But, but you guys certainly made, build a great business on it. What excites you about what's coming in backup? What are the new technologies, new advancements that perhaps we haven't seen and productized yet that you think are going to change the game? >> I think actually what we offer right now is the most exciting piece which is just choice flexibility. So Veeam again is synonymous with VMware backup but we cover a multitude of environments including AWS, containerized workloads, Kubernetes physical systems and the mobility pieces is critical because as organizations look to act on their digital transformation, cloud first initiatives, they need to be able to mobilize their workloads across different infrastructures, maybe from on-premises into the cloud, one cloud to another, maybe it's cloud back to on-premises, 'cause we do also see that. That flexibility of choice is what excites me about Veeam because it's ultimately giving the users best in class data protection tool sets without any prescriptive approach from us in terms of where you should be running your workloads. That is the choice that you use. >> Yeah, Veeam is definitely more than VMware. We actually had a chance to chat with you all like KubeCon and CloudNativeCon in Detroit. So we, we've seen the multitude of things that you touch. I want to bring it back to something and something kind of fun because you talked a lot about the community and being able to serve them. It's very clear, actually I shouldn't say this, I shouldn't say it's very clear, but to me it appears clear that community is a big priority for Veeam. I just want to call this out 'cause this was one of the cooler pieces of swag. You all gave out a hundred massage guns. Okay, very hot topic. Hot Christmas gift for 2022. I feel like Vanna White right now. And, but I thought that I was actually really compelled by this because we do a swag segment on theCUBE but it's not just about the objects or getting stuff. It's really about who's looking out for their community and how are they saying thanks. I mean, swag is a brand activation but it's also a thank you and I loved that you were giving out massage guns to the AWS Heroes and Community Builders. >> Yep. >> What role does community play in the culture and the product development at Veeam? >> So community has always been at the heart of Veeam. If you have a look at pretty much every single development across all of our versions, across all of our products it's always did by the community, right? We have a wonderful Veeam forum where we got 400,000 plus users actively providing feedback on the product what they would like to see. And that is ultimately what steers the direction of the product. Of course market trends and technology chain. >> A couple other factors, I'm sure. >> A couple of other factors, but community is huge for us. And the same goes for AWS. So, you know, talking with the AWS Heroes, the Community Builders helps Veeam reach further into that, into that community and the AWS user base and empower those folks with data protection tools and massage guns, when your feet are tired from, you know, being standing on them all day in Vegas. >> (laughs) Yeah, well, I mean, everybody, everybody's working hard and it's nice to say, it's nice to say, thank you. So I love, I love to hear that and it's, it's clear from the breadth of products that you're creating, the ways that you're supporting your customers that you already, they care a lot about community. We have a new challenge on theCUBE this year at AWS re:Invent. Think of it as an Instagram reel of your thought leadership, your hot take on the show, key themes as we look into 2023. What do you think is the most important story or trend or thing going on here at the show? >> I think it's just the continuation of cybersecurity and the importance of backup as a comprehensive cybersecurity strategy. You know, some folks might say that secure backup is your last line of defense. Again, ransomware is relentless. These folks are going to keep coming and even if they're successful, it's not a one and done thing. It's going to happen again and again and again. So, you know, we have a look around the show floor, the presentations there is a huge cybersecurity focus and really just what folks should be doing as their best practice to secure their AWS environments. >> That's awesome. Well, Paul, any final, any final thoughts or questions? >> I just quickly, you've mentioned data security, you mentioned data protection and backup sort of interchangeably but they're not really the same thing, are they? I mean, what businesses do you see Veeam as being here? >> I would say that we are a data protection company because of, yes, there is backup, but there's also the replication component. There's the continuous data protection component where we've got, you know, near-zero RTOs and then we again look at the cybersecurity components of that. What can we do to really protect that data? So I would say that the two are different. Backup is a subset of data protection. >> Sam, thank you so much for being here with us on theCUBE. It's been a super insightful conversation. Hopefully we'll get you back soon and more of the teams, there seem to be celebrities here with us on theCUBE. Paul Gillan, thank you so much for being here with me. >> Pleasure Savannah. >> And I'm glad we get to celebrate our first re:Invent and most importantly, thank you to the audience for tuning in. Without you, we don't get to hang out here in fabulous Las Vegas, Nevada, where we're live from the show floor at AWS re:Invent. My name is Savannah Peterson with Paul Gillan. We're theCUBE and we are the leading source for high-tech coverage. (bright music)

[Music] we're here with steve melanie the president and ceo of aviatrix steve john and i started this whole super cloud narrative as a way to describe that something different is happening specifically within the aws ecosystem but more broadly across the cloud landscape at re invent last year you and i spoke on the cube and you said one of your investors guy named nick sterile said to you at the show it's happening steve welcome to the cube what's happening what did nick mean by that yeah we were we were just getting ready to go on and i leaned over and he looked at me and he whispered in my ear and said it's happening he said it just like that and and you're right it was it was kind of funny and we talked about that and what he means is enterprises you know this is why i went to aviatrix three and a half years ago is the the the flip switch for enterprises and they said now we mean it we've been talking about cloud for 12 years or 15 years now we mean it we are digitally transforming we are the movement to cloud is going to make that happen and oh by the way of course it's multi-cloud because enterprises put workloads where they run best where they have the best security the best performance the best cost and the business is driving this transformation and they decide that i'm going to use that azure and another business unit decides i'm using google and another one says i'm using aws and so of course it's going to be multi-cloud and i think we're going to start seeing actual multi-cloud applications once that infrastructure and you know you call it the super cloud once that starts getting built developers are going to go wait a minute so i can pick this feature from google and and that service from azure and that service from aws easily without any hesitation once that happens they're going to start really developing today there aren't multi-cloud applications but but but the what's happening is the enterprise embracing public cloud they're using multiple clouds many of them call it four plus one right they're four different public clouds plus what they have on prem that to me is what's happening i am now re-architecting my enterprise infrastructure from applications all the way down to the network and i am embracing uh uh public clouds in that in that process so i mean you nailed us so many things in there i mean digitally transforming to me this is the digital transformation it's leveraging embracing the capex from the hyperscalers now you know people in the industry we're not trying to do what gartner does and create a new category per se but we do use super cloud as a metaphor so i don't expect necessarily vendors to use it or not but but i and i get that but when you talk about multi-cloud what specifically is new in other words what you touched on some of this stuff what constitutes a modern multi-cloud or what we would call a super cloud you know network architecture what are the salient attributes yeah i would say today so two years ago there was no such thing even as multiple clouds it was aws let's be clear everything was aws and for people to even back then two three years ago to even envision that there would be anything else other than aws people couldn't even envision now people kind of go yeah that was done we now see that we're going to use multiple clouds we're going to use azure we're going to use gcp and we're going to use this and we'll guess we're going to use oracle and even ollie cloud we're going to use five or four or five different public clouds what's but that would be i think of as multiple clouds but from an i.t perspective they need to be able to support all those clouds in these shared services and what they're going to do i actually think we're starting and you may have hit on something in the super cloud or i know you've talked about metacloud that that's got bad connotations for facebook i know everybody's like no please not another meta thing but there is that concept of this abstracted layer above you know writing we call it you know altitude you know aviatrix everything's you know riding above the clouds right that that that common abstracted layer this application infrastructure that runs the application that rides above all the different public clouds and i think once we do that you know dave what's going to happen is i think really what's going to happen is you're going to start seeing these these multi-cloud applications which to my knowledge really doesn't exist today i i think that might be the next phase and in order for that to happen you have to have all of the infrastructure be multi-cloud meaning not just networking and network security from from from aviation but you need snowflake you need hashtag you need datadog you need all the new horsemen of the new multi-cloud which isn't the old guys right this is all new people aviatrix dashie snowflake datadonk you name it that are going to be able to deliver all this multi-cloud cross-cloud wherever you want to talk about it such that application development and deployment can happen seamlessly and frictionlessly across multi-cloud once that happens the entire stack then you're going to start seeing and that to me starts enabling this what you guys call you know the super cloud the meta cloud the whatever cloud but that then rides above all the individual clouds that that's going to start getting a whole new realm of application development in my mind so we've got some work to do to basic do some basic blocking and tackling then the application developers can really build on top of that so so some of the skeptics on on this topic would ask how do you envision this changing networking versus it just being a bolt-on to existing fossilized network infrastructure in other words yeah how do we get from point a where we are today to point b you know so-called networking so we can actually build those uh super cloud applications yeah so you know what it is it's interesting because it goes back to my background at nasira and what we used to talk about then it isn't about managing complexity it's about creating simplicity it's very different and when you put the intelligence into the software right this is what computer science is all about we're turning networking into computer science when you create an abstraction layer we are not just an overlay day we dave we actually integrate in with the native services of the cloud we are not managing the complexity of these multi-clouds we are using it you know controlling the native constructs adding our own intelligence to this and then creating what is basically simplification for the people above it so we're simplifying things not just managing the complexity that's how you get the agility for cloud that's how you get to be able to do this because if all you are is a veneer on top of complexity you're just hiding complexity you're not creating simplicity and what happens is it actually probably gets more complex because if all you're doing is hiding the bad stuff you're not getting rid of it i love that i love that we're doing that at the networking and network security layer you're going to see snowflake and datadog and other people do it at their layers you know i reminds of a conversation i had with cause the one of the founders of pure storage who they're all about simplicity this idea of of creating simplicity versus like you said just creating you know a way to handle the complexity compare you know pure storage with the sort of old legacy emc storage devices and that's what you had you had you you had emc managing the complexity at pure storage disrupting by creating simplicity so what are the challenges of creating that simplicity and delivering that seamless experience that continuous experience across cloud is it engineering is it mindset is it culture is it technology what is it well i mean look at look you see the recession that we're we're hitting you see there is a significant problem that we have in the general it industry right now and it's called skills gap skills shortage it's two problems we don't have enough people and we don't have enough people that know cloud and the reason is everybody on the same tuesday three and a half years ago all said now i mean i'm moving the cloud we're a technology company we don't make sneakers anymore we don't make beer we're a technology company and we're going to digitally transform and we're going to move the cloud guess what three years ago there were probably seven people that understood cloud now everyone on the same tuesday morning all decides to try to hire those same seven people there's just not enough people around so you're going to need software and you're going to have to put the intelligence into the software because you're not going to be able to a hire those people and b even if you hire them you can't keep them as soon as they learn cloud guess what happens dave they're off they're on to the next job at the next highest bidder so how are you going to handle that you have to have software that intelligent software that is going to simplify things for you we have people managing massive multi-cloud network and network security people with two people on-prem they got hundreds right you it's not about taking that complex model that it had on-prem and jam it into the cloud you don't have the people to do it and you're not going to get the people to do it you know i want to ask you yeah so i want to ask you about the go to market challenges because we our industry gets a bad rap for for selling we're really good at selling and then but but actually delivering what we sell sometimes we fall down there so so i love tom sweet as cfo of of dell he talks about the the say do ratio uh how that's actually got to be low but you know but you know what i mean uh the math the fraction guy right so but do do what you say you're going to do are there specific go to market challenges related to this type of cross cloud selling where you can set you have to set the customer's expectations because what you're describing is not going to happen overnight it's a journey but how do you handle that go to market challenge in terms of setting those customer expectations and actually delivering what you say you can sell and selling enough to actually have a successful business um so i think everything's outside in so so i think the the what really is exciting to me about this cloud computing model that with the transformation that we're going through is it is business-led and it is led by the ceo and it is led by the business units they run the business it is all about agility is about enabling my developers and it's all about driving the business market share revenue all these kind of things you know the last transformation of mainframe to on to pc client server was led by technologists it wasn't led by the business and it was it was really hard to tie that to the business so then so this is great because we can look at the initiatives you can look at the the the initiatives of the ceo in your company and now as an i.t person you can tie to that and they're going to have two or three or four initiatives and you can actually map it to that so that's where we start is let's look at what the c your ceo cares about he cares about this he cares about that he cares about driving revenue he cares about agility of getting new applications out to the market sooner to get more revenue there's this and oh by the way transfer made transforming your infrastructure to the cloud is the number one thing so it's all about agility so guess what you need to be able to respond to that immediately because tomorrow the business is going to go to you and say great news dave we're moving to gcp wait what no one told me about that well we're telling you now and uh you need to be ready tomorrow and if you're sitting there and you're tied to the low-level constructs and all you know is aws well i don't have those people and even if i have even if i could hire them i'm not allowed to because i can't hire anybody how am i going to respond to the business and the needs of the business now all of a sudden i'm in the way as the infrastructure team of the ceo's goals because we decided we need to we need to get the ai capabilities of gcp and we're moving to gcp or i just did a big deal with gcp and uh miraculously they said i need to run on gcp right i did a big deal with google right guess what comes along with that oh you're moving to gcp great the business says we're moving to gcp and the i.t guys are sitting there going well no one told me well sorry so it's all about agility it's all about that and the and and complexity is the killer to agility this is all about business they're going to come to you and say we just acquired a company we need to integrate them oh but they got they use the same ip address range as we do there's overlapping ips and oh by the way they're in a different cloud how do i do that no one cares the business doesn't care they're like me they're very impatient get it done or we'll find someone who will yeah so you've got to get ahead of that and so when we in terms of when we talk to customers that's what we do this isn't just about defenses this is about making you get promoted making you do good for your company such that you can respond to that and maybe even enable the company to go do that like we're going to enable people to do true multi-cloud applications because the infrastructure has to come first right you you put the foundation in your big skyscraper like the crew behind me and the plumbing before you start building the floors right so infrastructure comes first then comes then comes the applications yeah so you know again some people call it super cloud like us multi-cloud 2.0 but the the real mega trend that i see steve and i'd love you to bottom line this and bring us home is you know andreessen's all companies are software companies it's like version 2.0 of that and the applications that are going to be built on that top this tie into the digital transformations it was goldman it's jpmc it's walmart it's capital one b of a oracle's acquisition of cerner is going to be really interesting to see these super clouds form within industries bringing their data their tooling and their specific software expertise built on top of that hyperscale infrastructure and infrastructure for companies like yours so bottom line is stephen steve what's the future of cloud how do you see it the future is n plus one so two years ago people had one plus one i had what i had on prem and then what i had in aws they today if you talk to an enterprise they'll have what they call four plus one right which is four public clouds plus what i have on prem it's going to n plus one right and what's going to happen is exactly what you said you're going to have industry clouds you're going to the the multi-cloud aspect of it is going to end it's not going to go from four to one some people think oh it's not going to be four it's going down to one or two bs it's going to end it's going to a lot as they start extending to the edge and they start integrating out to the to the branch offices it's not going to be about that branch offer so that edge iot or edge computing or data centers or campus connecting into the cloud it's going to be the other way around the cloud is going to extend to those areas and you're going to have ai clouds you know whether it's you know ultra beauty who's a customer of ours who's starting to roll out ar and vr out to their retail stores to show you know makeup and this and the other thing these are new applications transformations are always driven by new applications that don't exist this isn't about lift and shift of the existing applications the 10x tam in this market is going to becomes all the new things that's where the explosion is going to happen and you're going to see end level those those branch offices are going to look like clouds and they're going to need to be stitched together and treated like one infrastructure so it's going to go from four plus one to n plus one and that's what you're gonna want as an enterprise i'm gonna want n clouds so we're gonna see an explosion it's not going to be four it's going to be end now at the end underneath all of that will be leveraging and effectively commoditizing the existing csps yeah and but you're going to have an explosion of people commoditizing them and just like the goldmans and the industry clubs are going to do they're going to build their own eye as well right no way no way it's that's what's going to happen it's going to be a 10x on what we saw last decade with sas it's all going to happen around clouds and supercloud steve malini thanks so much for coming back in the cube and helping us sort of formulate this thinking i mean it really started with with with you and myself and john and nick and really trying to think this through and watching this unfold before our eyes so great to have you back thank you yeah it's fun thanks for having me are you welcome but keep it right there for more action from super cloud 22 be right back [Music] you

welcome back everyone to thecube's live coverage in san francisco for vmware explorer 2022 formerly vmworld i'm john furrier david live dave 12 years we've been covering this event formerly vmware first time in west now it's explore we've been in north we've been in south we've been in vegas multi-cloud is now the exploration vmware community is coming in john siegel svp at dell cube alumni dave mccraw vp at vmware guys thanks for coming back both cube alumni it's great to see you very senior organizations senior roles in the organizations of vmware and dell one year since the split great partnership continuing i mean some of the conversations we've been having over the past few years is that control plane the management layer making everything work together it's essentially been the multi-cloud hybrid cloud story what's the update what's how's the partnership look yeah i you know i just to start off i mean i would say i don't think our partnership's been any has ever been any better um if you look at you mention our vision very much a shared vision in terms of the multi-cloud world and i don't think we've ever had more joint innovation projects at one time i think we have over 40 now dave that are going on across multi-cloud ai cyber security uh modern applications and and uh you know here just at you just vmworld vmware explorer we have over 30 uh vmware sessions that are featuring dell um and this is i think more than we've ever had so look i think um there's a lot of momentum there and we're really looking forward to what's to come so you guys obviously spent a lot of time together when vmware was part of dell and then you've been it's been a year since the spin and then you codified i think it was a five-year agreement you know so you had some time to figure that out and then put it into paper so you just kind of quantified some of the stuff that's going on but now we're entering a yet another phase so that that that that agreement's probably more important than ever now i mean list in terms of getting it documented and an understanding right yeah that agreement really defines a framework for solution development and for go to market so we've been doing it and refining it for the last five years so now you know putting and codifying it into a written signed agreement it basically is instantiating what we've been doing that we know works uh where we can drive uh solution development we can drive deep architectural co-innovation together as well and as john said across multiple you know project and solution areas so we we've been talking to years to you know a lot of these strat guys guys like matt baker about things like you know you see aws do nitro and then of course project monterey and and i know that you guys have had a you know a big sort of input into that and so now to see it come to fruition is is huge because you know from our view it's the future of computing architectures how do you handle you know data rich applications ai applications that's what are your thoughts on here i couldn't agree more uh project monterey is a great example of how we're innovating together we just talked about i mean first of all it's all so we have vxrail which let's let's start there right we have over 19 000 joint customers right now we continue to innovate more and more on the vxrail architecture great example of that as our partnership with project monterey and taking essentially vsphere 8 and running it for the first time on an hci system directly on the dp used itself right on the dpus ability now to offload nsxt from from the cpus to the dpus uh hope you know in the short term first of all great benefits for customers in terms of better performance but as you just mentioned it's game changing in terms of laying the foundation for the future architectures that we plan on together helping out customers there's one other dynamic for you on is um and it's not unique to dell but dell's the biggest you know supply supplier partner etc but you're able to take vmware software and drive it through your business and and that enables you to get more subscription revenue and makes it stickier and that's a really important change from you know 10 years ago yeah and it's it's a combination as you know of dell software and vmware software together absolutely and i think what's with this is a game-changing innovation that you can run on top of our joint system vxrail if you will um and now what our customers can expect is life cycle automation of now you know the dpus as well as tanzu as well as everything else we layer on top of that core foundation that we have over 19 000 customers running today so i mean like that 19 000 number i want to get back up to the vx rail and you mentioned vsphere that's big news here this year vsphere 8 big release a lot of going on what's the hci angle you mentioned that what's in it for the customer what does that mean for the folks here because let's face it the vsphere aids got everyone in that they've all the v-sections are going going crazy right another vsphere release getting training they have the labs here what's it mean for the customers what's the value there with that hci solution with the gpus well first of all vsphere 8 as we know it has a lot of goodies in it but you know what what i think to me what's been most powerful about this is the ability to run vsphere 8 uh and and specifically on the dpus now you can run it it is open up all new possibilities now and so that nsxt that i mentioned you know running that on gpus opens up a whole new uh architecture now for our customers going forward and now really sets us up for modern distributed architecture for the future so like edge okay yeah and vsphere 8 brings in you know cloud connectivity as well so you know customers can run in a cloud disconnected mode they can run in a cloud connected mode so you know that's going to bring in the ability to do specialized things on security cycle management there's a whole series of services that can now be added as well as you know leveraging you know vcenter management capabilities so what's happening at the edge we had i think it was lows on hotel tech world right okay good not the other one um but so so that's got to be exploding now with that with that because it just changes the game for for these stores there's i mean retail uh manufacturing maybe you can give us an update on there's so much happening on the edge side as you know i mean that's where most of the a lot of the innovations happening right now is at the edge and a lot of the companies we talked to 8x right 8x expectation of increase in uh edge workloads over the next and the data challenge too and the data challenge is huge so you heard about the innovations with vsphere 8. in addition to that we just introduced today as well the smallest vx rail for the edge ever this thing is it's like think picture a couple eight and a half by 11 notebooks not much not much you know maybe a little wider than that but not much more um you know these these are stacked on top of each other these are you can rack and stack and mount these things anywhere and it also is the first aci system that has you know a built-in hardware witness so this helps set it up for environments that are you know network bandwidth constrained or have high high latency no longer an issue next gen app is going to want to have a local data server at the edge right and with compute there right high performance right right so now you're getting it across the wire yes you get racket stack a couple of these small things i mean they can they can fit into like a you know clark kent's briefcase right these things are so small um you want to do the analytics on site and return responses back you don't want to be moving massive data payloads off the egg so you got to have the right level of compute to run machine learning algorithms and and do the analytics type work that you want to do to make local decisions yeah i mean we just had david lithimon who was one of the keynote speakers here at the event and we've been talking about super cloud and multi-cloud meta cloud all the different versions of what we see as this next-gen and this brings up a point of like his advice to young people learn how multi-cloud learn about system architecture because if you can figure out how to put it together you're going to have to make more money anyway that this whole edge piece opens up huge challenges and opportunities around how do you configure these next-gen apps what does the ai look like what's the data architecture this is not like get some training curriculum online and you get you know 101 and you're getting a job no this is more complicated but with the hardware you guys make it easier so where's the complexity shift between having a powerful edge device like the vxrail with the vsphere what's the ec button on that like how do you guys what's the vision because this is going to be a major battleground this whole edge piece yeah it's going to be huge well i think when you look at the innovation that dell is bringing to market with technologies like outlander and then designing that into vxrail and then you combine that with our tonzu capabilities to manage development and deployment of applications this is about heterogeneous deployment and management at scale of applications with technologies like tons of mission control then deploying service mesh right for security being able to use sassy to be able to secure you know with cloud security over the wire so it's bringing together multiple technologies to deliver simplicity to the customer the ability to go one to many you know in terms of being able to deploy and manage and update whether that's a security patch or an application update and do that very rapidly at a low cost so the benefit with this solution now just putting this together is i can ship a box small and or stack them and essentially it's done remotely it's that's provision the provisioning issues not a truck roll as they say or professional services enabled you can just drop that out there and this is where the customers need to be yeah that absolutely is that the vision don't get that right exactly you don't you don't need the you don't need the skills yeah you don't need the specialized skills you don't need a lot of space you don't need you know high network bandwidth all these things right all these innovations that we're talking about here um really combined into really enabling a whole new whole new future here for edge is are you doing apex now is that i think thickest part sure part of yours okay so um is apex fitting into the to the edge how does it fit yeah i mean well first of all you know a lot of what we talked with apex is really about a consumption a way to ensure there's a common cloud experience wherever the data is and where the applications are and so absolutely edge fits into this as well and so we have we have common ways to consume our infrastructure today our joint infrastructure whether it's in the data center at the edge um or you know uh in the cloud usain ragu when he was on i said it was great keynote loved it one of the things that i didn't think there was enough of was security and he's like yeah we only had so much time but vmware is a very strong security story we heard a really strong security story at dell tech world i mean half the innovations and the new you know storage products were security and the new os's and it was impressive what what's how are you guys working together on security is that one of those let me give you a few key things you know our teams are working together at the engineer to engineer level you know reference architectures for zero trust as an example being able to look you know hardware root of trust up into the application layer right so we're looking at really defense in depth here you know i mentioned what we're doing with sassy right with cloud security capabilities so you really have to look at this from the edge to the core with the you know from a networking perspective getting the network the insights on things that maybe anomalies that may be happening on the network so using our network insight technology you know uh nsx and then being able to ultimately uh have a secure development pipeline as well i mean you we all know about the supply chain attacks that happen right and so being able to have a you know secure pipeline for development is critical for both of our companies working together i think the tan zoo and you mentioned the developer self-service that experience combined with kind of the power of the dell you know let's face it the boxes are awesome hardware matters and software matters so bringing that expertise together michael daley always used to say on thecube better together in respect to vmware and dell a lot of fruit has been born from that labor right specifically around and now when you add the tan zoo and you get vsphere you got the operational excellence you got the you got the performance and scale with the dell boxes and hardware and software and now you've got the tan zoo what's missing or is it all there now i mean where how would you how would you guys peg the progress bar is it like it's all rocking right now or or i'd say you're never done first of all but i you know i look at some of the innovations that we've brought to market recently where we've are combining and stacking these technologies into a more defense in-depth like solution you know bringing nsx onto vxrail so that you can flip a switch easily and light up the firewall the new plug-in yeah that's a great example simple simple um carbon black workload another example where we're taking carbon black technology that was typically on endpoints you know on pcs bringing that into the data center right and leveraging all the analytics and insights around you know being able to identify anomalies and then remediate those anomalies so we're seeing very good traction with those and the cloud native developers containers they're all native container working with compute and container storage object store in the cloud kubernetes we've embraced it yeah i mean yeah containers running containers and vms on the same infrastructure common way to manage it all i mean that that's been a big part of it as well obviously a lot of the focus that dell's bringing here as well is is the inability to run that stack easily right you heard the announcement on uh tanzu for kubernetes operators right earlier today tko we call it uh you know that running on vxrail now is really targeted at the i.t operator in allowing them to easily stand up a self-service developer devops environment on vxrail going forward and then a piece that might be invisible to them is back to monterey isolation right encryption and data moving you know absolutely storage the security the compute right the management right that's that's a complete and it's about reducing attack services as well right the security perspective as well when you when you're moving nsxt onto a dpu you're doing that as well so there's it takes the little things right at the end of the day security is a mindset up across both companies in terms of how we approach our architectures um and it's the you know a lot of times it's the little things as well that we make sure right so shared vision working at the engineering levels together for many many years know that you guys are validating more of that coming what's next take us through okay we're here 2022 we got super cloud multi-cloud hybrid full throttle right now it's hybrid's a steady state that's cloud operations infrastructure as code has happened it's happening what's next for you guys in the relationship can you share a little bit that you can if you can what we can expect what you see uh with monterrey is the start of a re-architecting of i.t infrastructure not just in the data center but also at the edge right these technologies will move out and be pervasive you know across i think edge to colo to core data center to cloud right and so that's a starting point now we're looking at memory tiering right i think we talked last time about capitola and memory tiering and you know being able to bring that forward uh being able to do more with confidential computing as an example right secure enclaves and confidential computing so you know a lot of this is focused around simplicity and security going forward and ease of management around take the heavy lifting away from the customer abstract that in offer the power and performance that's right and it's going to come down to delivering time to value for our customers you know can we cut that time to value by 25 50 percent so they can be in production faster yeah i think project monterey is something we'll be building on for a long time right i mean this is the start of a major new future architecture of these companies so if you had to pick one we have 40 initiatives that are joined together real literally project monterey is one of my favorites for sure in terms of what it's going to do not just for that common cloud experience but for the edge and and we talked a lot about the edge today and where that's headed you think it's going to explode up new apps i really do think so well it's going to put you in a new it's going to put in curve yeah absolutely right and operationally uh security wise um from a modern apps perspective i mean all it checks all the boxes and it's going to allow us to to help and take our existing customers on that journey as well what's great about this conversation we've been following both you guys for a long time and your companies and and technology upgrades and and the business impact and open source and all doing all this for customers but the wave that's coming we're seeing the expo hall here i mean it's people are really excited they're enthused they're committed highly confident that this this wave is coming they kind of see it people kind of seeing the fog lift they're seeing money making value creation people kind of feeling more comfortable but still a little nervous around you know what's coming next because it's still uncertainty but pretty good ecosystem i'd have to say that's pretty pretty interesting yeah a lot of them are excited about you know what they can do at the edge and how they can differentiate their businesses i mean that's right well congratulations guys thanks for coming on thecube and sharing the update thank you it more innovation it's not stopping here at vmware explorer dell and vm we're continuing to have that kind of relationship joint engineering it's all coming together and you can mix and match this and the stack but it's ultimately going to be cloud operations edge is the action of course hybrid cloud as well it's thecube thanks for watching [Music] you

[Music] welcome to thecube's coverage of vmworld 2021 i'm lisa martin pleased to welcome back to the program the cto of vmware kit kohlberg welcome back to the program and congrats on your new role thank you yeah i'm really excited to be here so you've been at vmware for a long time you started as an intern i read yeah yeah it's been uh 18 years as a full-timer but i guess 19 if you count my internship so quite a while it's many lifetimes in silicon valley right many lifetimes in silicon valley well we've seen a lot of innovation from vmware in its 23 years you've been there the vast majority of that we've seen a lot of successful big tech waves ridden by vmware in april vmware pulled tanzu and vmware cloud foundation together vmware cloud you've got some exciting news with respect to that what are you announcing today well we got a lot of exciting announcements happening at vmworld this week but one of the ones i'm really excited about is vmware cloud with tons of services so let me talk about what these things are so we have vmware cloud which is really us taking our vmware cloud foundation technology and delivering that as a service in partnership with our public cloud providers but in particular this one with aws vmware cloud on aws we're combining that with our tanzu portfolio of technologies and these are really technologies focused at developers at folks driving devops building and operating modern applications and what we're doing is really bringing them together to simplify customers moving from their data centers into the cloud and then modernizing their applications it's a pattern that we see very very often this notion of migrate and then modernize right once you're on a modern cloud infrastructure makes it much easier to modernize your applications talk to me about some of the catalysts for this change and this offering of services was it you know catalyzed by some of the events we've seen in the world in the last 18 months and this acceleration of digital adoption yeah absolutely and we saw this across our customer base across many many different industries although as you can imagine those industries that that were really considered essential uh were the ones where we saw the biggest sorts of accelerations we saw a tremendous amount of people needing to support remote workers overnight right and cloud is a perfect use case for that but the challenge a lot of customers had was that they couldn't take the time to retool that they had to use what they already had and so something like vmware cloud was perfect for that because it allowed them to take what they were doing on-prem and seamlessly extend it into the cloud without any changes able to do that you know almost overnight right but at the same time what we also saw was the acceleration of their digital transformation people are now online they're needing to interact with an app over their phone to get something you know remotely delivered or to schedule maybe um an appointment for their pet because you know a lot of people got pets during the pandemic and so you just saw this rush toward digitization and these new applications need to be created and so as customers move their application estate into the cloud with vmware cloud and aws they then had this need to modernize those applications to be able to deliver them faster to respond fast to the very dynamic nature of what was happening during the pandemic so let's talk about uh some of the opportunities and the advantages that vmware cloud with tanzania service is going to deliver to those it admins who have to deliver things even faster yep so let me talk a bit about the tech and then talk about how that fits into uh what the users will experience so vmware cloud with tons of services is really two key components uh the first of which is the tanzu kubernetes grid service the tkg service as we call it so what this is is actually a deep integration of tonsil kubernetes grid with vmware cloud and and the kubernetes we've actually integrated into vmware cloud foundation folks who are familiar with vmware may remember that a couple of years ago we announced project pacific which was a deep integration of kubernetes into vsphere essentially enabling vsphere to have a kubernetes interface to be natively kubernetes and what that did was it enabled the i.t admins to have direct insight inside of kubernetes clusters to understand what was happening in terms of the containers and pods that that their developers were running it also allowed them to leverage uh their existing vsphere and vmware cloud foundation tooling on those workloads so fast forward today we we have this built in now and what we're doing is actually offering that as a service so that the customer doesn't need to deal with managing it installing it updating any of that stuff instead they can just leverage it they can start creating kubernetes clusters and upstream conformant kubernetes clusters to allow their developers to take advantage of those capabilities but also be able to use their native tooling on it so i think that's really really important is that the it admin really can enable their developers to seamlessly start to build and operate modern applications on top of vmware cloud got it and talk to me about how this is going to empower those it admins to become kubernetes operators yeah well i think that's exactly it you know we talk to a lot of these admins and and they're seeing the desire for kubernetes uh from their lines of business from you know from the app teams and the idea is that when you look start looking at the kubernetes ecosystem there's a whole bunch of new tooling and technology out there we find that people have to spend a lot of time figuring out what the right thing to use is and for a lot of these folks they say hey i've already figured out how to operate applications in production i've got the tooling i've got the standardization i got things like security figured out right super important and so the real benefit of this approach and this deep integration is it allows them to take those those tools those operational best practices that they already have and now apply them to these new workloads fairly seamlessly and so this is really about the power of leveraging all the investments they've made to take those forward with modern applications and the total adjustable market here is pretty big i heard your cto referring to that in an interview in september and i was looking at some recent vmware survey numbers where 80 of customers say they're deploying applications in highly distributed environments that include their own data center multiple clouds uh edge and also customers said hey 90 of our application initiatives are focused on modernization so vmware clearly sees the big tam here yeah it's absolutely massive um you know we see uh many customers the vast majority something like 75 percent are using multiple clouds or on-prem in the cloud we have some customers using even more than that and you see this very large application estate that's spread out across this and so you know i think what we're really looking at is how do we enable uh the right sorts of consistency both from an infrastructure perspective enabling things like security but also management across all these environments and by the way it's another exciting thing neglected to mention about this announcement vmware cloud with tonsil services not only includes the tonsil kubernetes grid service giving you that sort of kubernetes uh cluster as a service if you will but it also includes tons of mission control essentials and this is really the next generation of management when you start looking at modern applications and what tons of mission control focuses on is enabling managing kubernetes consistently across clouds and so this is the other really important point is that yes we want to make vmware cloud vmware cloud infrastructure the best place to build and operate applications especially modern ones but we also realize that you know customers are doing all sorts of things right they're in the native cloud whether that's aws or azure or google and they want ways of managing more consistently across all these environments in addition to their vmware environments both in the cloud and on-prem and so tons of mission control really enables that as well and that's another really powerful aspect of this is that it's built in to enable that next level of administration and management that consistency is critical right i mean that's probably one of the biggest benefits that customers are getting is that familiarity with the console the consistency of being able to manage so that they can deploy apps faster um that as businesses are still pivoting and changing direction in light of the pandemics i imagine that that is a huge uh from a business outcomes perspective the workforce productivity there is probably pretty pretty big yeah and i think it's also about managing risk as well you know one of the the biggest worries that we hear from many of the cios uh ctos executives that we talk to at our customers is this uh software supply chain risk like what is it exactly like what are the exact bits that they're running out there right in their applications because the reality is that um those apps are composed of many open source technologies and you know as we saw with solarwinds it's very possible for someone to get in and you know plant malicious code into their source repository such that as it gets built and flows out it'll you know just go out and customers will start using it and it's a huge huge security vulnerability and one thing on that note that customers are particularly worried about is the lack of consistency across their cloud environments that because things are done different ways and the different teams have different processes across different clouds it's easy for small mistakes to creep in there for little openings right that a hacker might be able to go and exploit and so i think this gets back to that notion of consistency and that you're right it's great for productivity but the one i think that's almost in some ways you might say uh for many of these folks more important for is from a security standpoint that they can validate and ensure they're in compliance with their security standards and by the way you know this is uh for most companies a board level discussion right the board is saying hey like do we have the right controls in place because it is um such an important thing and such a critical risk factor it is a critical risk factor we saw you mentioned solar winds but just in the last 18 months the the massive changes to the threat landscape the huge rise in ransomware and ddos attacks you know we had this scatterer everybody went home and you've got you know the edge is booming and you've got folks using uh you know not using their vpns and things when they should be so that the fact that that's a board level discussion and that this is going to help from a risk mitigation perspective that consistency that you talked about is huge i think for a customer in any industry yep yeah and it's pretty interesting as well like you mentioned ransomware so we're doing some work on that one as well actually not specifically with this announcement but it's another vmware cloud service that plugs into this uh seamlessly vmware cloud disaster recovery and one of the really cool features that we're announcing at vmworld this week is the ability to actually support and and maybe uh handle ransomware attacks and so the idea there is that if you do get compromised and what typically happens is that the hackers come in and they encrypt you know some of your data and they say hey if you want to get access to it you got to pay us and we'll decrypt it for you but if you have the right dr solution um that's backing up on a fairly continuous basis it means that whatever data might be encrypted you know would only be a small delta like the last let's say hour or two of data right and so what we're looking at is leveraging that dr solution to be able to very rapidly restore specific individual files uh that may have been compromised and so this is like one way that we're helping customers deal with that like obviously we want to put a whole bunch of other security protections in place and we do when we enable them to do that but one thing when you think about security is that it's very much defense in depth that you have multiple layers of the fail-safes there and so this one being kind of like the end result that hackers do get in they do manage to compromise it they do manage to get a hold of it and encrypt it well you still got unencrypted backups that you control and that you have um a very clean delineation and separation from just like kind of an architectural standpoint that the hackers won't be able to get at right so that you can control that and restore it so again you know this is something very top of mind for us and it's funny because we don't always lead with the security angle maybe we should as i'm saying it here but uh but it's something that's very very top of mind for a lot of our customers it's something that's also top of mind for us and that we're focused on it is because it's no longer if we get attacked it's one and they've got to be able to have the right recovery strategy so that they don't have to pay those ransoms and of course we only hear about the big ones like the solar winds and the colonial pipelines and there's many more going on when i get back to vmware cloud with tanzania services talk to me about how this fits into vmware's bigger picture yeah yeah yeah great question thanks for bringing me back i'd love to geek out on some of these things so um but when you take a step back so what we're really doing uh with vmware cloud is trying to provide this really powerful infrastructure layer uh that is available anywhere customers want to run applications and that could be in the public cloud it could be in the data center it could be at the edge it could be at all those locations and you know you mentioned edge earlier and i think we're seeing explosive growth there as well and so what we're really doing is driving uh broad optionality in terms of how customers want to adopt these technologies and then as i said we're sort of you know we're kind of going broad many locations we're also building up in each of those locations this notion of ponzu services being seamlessly integrated in doing that uh you know starting now with vmware cloud aws but expanding that to every every location that we have in addition you know we're also really excited another thing we're announcing this week called project arctic now the idea with arctic is really to start driving more choice and flexibility into how customers consume vmware cloud do they consume it as software or as a service and where do they do that so traditionally the only way to get it delivered as a service would be in the public cloud right vmware cloud aws you can click a few buttons and you get a software defined data center set up for you automatically now traditionally on-prem we haven't had that we we did do something pretty powerful uh a year or two back with the release of vmware cloud on dell emc we can deliver a service there but that often required new hardware you know new setup for customers and customers are coming back to us and saying hey like we've got these really large vsphere deployments how do we enable them to take advantage of all this great vmware cloud functionality from where they are today right they say hey we can't rebuild all these overnight but we want to take advantage of vmware cloud today so that's what really what project arctic is focused on it's focused on connecting into these brownfield existing vsphere environments and delivering some of the vmware cloud benefits there things like being able to easily well first of all be able to manage those environments through the vmware cloud console so now you have one place where you can see your on-prem deployments your cloud deployments everything being able to really easily move uh applications between on-prem and the cloud leveraging some of the vmware cloud disaster recovery capabilities i just mentioned like the ransomware example you can now do that even on prem as well because keep in mind it's people aren't attacking you know the hackers aren't attacking just the public cloud they're attacking data centers or anywhere else where these applications might be running and so arctic's a great example of where we're saying hey there's a bunch of cool stuff happening here but let's really meet customers where they're at and many of our customers still have a very large data center footprint still want to maintain that that's really strategic for them or as i said may even want to be extending to the edge so it's really about giving them more of that flexibility so in terms of meeting customers where they are i know vmware has been focused on that for probably its entire history we talk about that on the cube in every vmworld where can customers go like what's the right starting point is this targeted for vmware cloud on aws current customers what's kind of the next steps for customers to learn more about this yeah absolutely so there's a bunch of different ways so first of all there's a tremendous amount of activity happening here at vmworld um just all sorts of breakout sessions like you know detailed demos like all sorts of really cool stuff just a ton of content i'm actually kind of i'm in this new role i'm super excited about it but one thing i'm kind of bummed out about is i don't have as much time to go look at all these cool sessions so i highly recommend going and checking those out um you know we have hands-on labs as well which is another great way to test out and try vmware products so hold.vmware.com uh you can go and spin those things up and just kind of take them for a test drive see what they're all about and then if you go to vmc.vmware.com that is vmware cloud right we want to make it very easy to get started whether you're in just a vsphere on-prem customer or whether you already have vmware cloud and aws what you can see is that it's really easy to get started in that there's a ton of value-add services on top of our core infrastructure so it's all about making it accessible making it easy and simple to consume and get started with so there's a ton of options out there and i highly recommend folks go and check out all the things i just mentioned excellent kit thank you for joining me today talking about vmware cloud with tons of services what's new what's exciting the opportunities in it for customers from the i.t admin folks to be empowered to be kubernetes operators to those businesses being able to do essential services in a changing environment and again congratulations on your promotion that's very exciting awesome thank you lisa thank you for having me our pleasure for kit colbert i'm lisa martin you're watching thecube's coverage of vmworld 2021 [Music] you

>>Hello. And we're here at the cube startup showcase made possible by a Ws. Thanks so much for joining us today. You know when Jim McDaid Ghani was formulating her ideas around data mesh, She wasn't the only one thinking about decentralized data architecture. Hello, Fresh was going into hyper growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of last decade, Hello Fresh relied on a monolithic data architecture and the internal team. It had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture which possessed many principles of so called data mesh even though they didn't use that term. Specifically, the company is a strong example of an early but practical pioneer of data mission. Now there are many practitioners and stakeholders involved in evolving the company's data architecture, many of whom are listed here on this on the slide to are highlighted in red are joining us today, we're really excited to welcome into the cube Clements cheese, the Global Senior Director for Data at Hello Fresh and christoph Nevada who's the Global Senior Director of data also, of course. Hello Fresh folks. Welcome. Thanks so much for making some time today and sharing your story. >>Thank you very much. Hey >>steve. All right, let's start with Hello Fresh. You guys are number one in the world in your field, you deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling christoph. Tell us a little bit more about your company and its vision. >>Yeah. Should I start or Clements maybe maybe take over the first piece because Clements has actually been a longer trajectory yet have a fresh. >>Yeah go ahead. Climate change. I mean yes about approximately six years ago I joined handle fresh and I didn't think about the startup I was joining would eventually I. P. O. And just two years later and the freshman public and approximately three years and 10 months after. Hello fresh was listed on the German stock exchange which was just last week. Hello Fresh was included in the Ducks Germany's leading stock market index and debt to mind a great great milestone and I'm really looking forward and I'm very excited for the future for the future for head of fashion. All our data. Um the vision that we have is to become the world's leading food solution group and there's a lot of attractive opportunities. So recently we did lounge and expand Norway. This was in july and earlier this year we launched the U. S. Brand green >>chef in the U. K. As >>well. We're committed to launch continuously different geographies in the next coming years and have a strong pipe ahead of us with the acquisition of ready to eat companies like factor in the U. S. And the planned acquisition of you foods in Australia. We're diversifying our offer now reaching even more and more untapped customer segments and increase our total addressable market. So by offering customers and growing range of different alternatives to shop food and consumer meals. We are charging towards this vision and the school to become the world's leading integrated food solutions group. >>Love it. You guys are on a rocket ship, you're really transforming the industry and as you expand your tam it brings us to sort of the data as a as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company specifically as it relates to your data journey. You began as a start up. You had a basic architecture like everyone. You made extensive use of spreadsheets. You built a Hadoop based system that started to grow and when the company I. P. O. You really started to explode. So maybe describe that journey from a data perspective. >>Yes they saw Hello fresh by 2015 approximately had evolved what amount of classical centralized management set up. So we grew very organically over the years and there were a lot of very smart people around the globe. Really building the company and building our infrastructure. Um This also means that there were a small number of internal and external sources. Data sources and a centralized the I team with a number of people producing different reports, different dashboards and products for our executives for example of our different operations teams, christian company's performance and knowledge was transferred um just via talking to each other face to face conversations and the people in the data where's team were considered as the data wizard or as the E. T. L. Wizard. Very classical challenges. And those et al. Reserves indicated the kind of like a silent knowledge of data management. Right? Um so a central data whereas team then was responsible for different type of verticals and different domains, different geographies and all this setup gave us to the beginning the flexibility to grow fast as a company in 2015 >>christoph anything that might add to that. >>Yes. Um Not expected to that one but as as clement says it right, this was kind of set up that actually work for us quite a while. And then in 2017 when L. A. Freshman public, the company also grew rapidly and just to give you an idea how that looked like. As was that the tech department self actually increased from about 40 people to almost 300 engineers And the same way as a business units as Clemens has described, also grew sustainable, sustainably. So we continue to launch hello fresh and new countries launching brands like every plate and also acquired other brands like much of a factor and with that grows also from a data perspective the number of data requests that centrally we're getting become more and more and more and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very or basically get a very deep understanding about the business. And also suffered a lot from this context switching back and forth, essentially there to prioritize across our product request from our physical product, digital product from the physical from sorry, from the marketing perspective and also from the central reporting uh teams. And in a nutshell this was very hard for these people. And this that also to a situation that, let's say the solution that we have became not really optimal. So in a nutshell, the central function became a bottleneck and slowdown of all the innovation of the company. >>It's a classic case, isn't it? I mean Clements, you see you see the central team becomes a bottleneck and so the lines of business, the marketing team salesman's okay, we're going to take things into our own hands. And then of course I I. T. And the technical team is called in later to clean up the mess. Uh maybe, I mean was that maybe I'm overstating it, but that's a common situation, isn't it? >>Yeah. Uh This is what exactly happened. Right. So um we had a bottleneck, we have the central teams, there was always a little of tension um analytics teams then started in this business domains like marketing, trade chain, finance, HR and so on. Started really to build their own data solutions at some point you have to get the ball rolling right and then continue the trajectory um which means then that the data pipelines didn't meet the engineering standards. And um there was an increased need for maintenance and support from central teams. Hence over time the knowledge about those pipelines and how to maintain a particular uh infrastructure for example left the company such that most of those data assets and data sets are turned into a huge step with decreasing data quality um also decrease the lack of trust, decreasing transparency. And this was increasing challenge where majority of time was spent in meeting rooms to align on on data quality for example. >>Yeah. And and the point you were making christoph about context switching and this is this is a point that Jemaah makes quite often is we've we've we've contextualized are operational systems like our sales systems, our marketing system but not our our data system. So you're asking the data team, Okay. Be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it start stop, start, stop, it's a paper cut environment and it's just not as productive. But but on the flip side of that is when you think about a centralized organization you think, hey this is going to be a very efficient way, a cross functional team to support the organization but it's not necessarily the highest velocity, most effective organizational structure. >>Yeah, so so I agree with that. Is that up to a certain scale, a centralized function has a lot of advantages, right? That's clear for everyone which would go to some kind of expert team. However, if you see that you actually would like to accelerate that and specific and this hyper growth, right, you wanna actually have autonomy and certain teams and move the teams or let's say the data to the experts in these teams and this, as you have mentioned, right, that increases mental load and you can either internally start splitting your team into a different kind of sub teams focusing on different areas. However, that is then again, just adding another peace where actually collaboration needs to happen busy external sees, so why not bridging that gap immediately and actually move these teams and to end into into the function themselves. So maybe just to continue what, what was Clements was saying and this is actually where over. So Clements, my journey started to become one joint journey. So Clements was coming actually from one of these teams to build their own solutions. I was basically having the platform team called database housed in these days and in 2019 where basically the situation become more and more serious, I would say so more and more people have recognized that this model doesn't really scale In 2019, basically the leadership of the company came together and I identified data as a key strategic asset and what we mean by that, that if we leverage data in a proper way, it gives us a unique competitive advantage which could help us to, to support and actually fully automated our decision making process across the entire value chain. So what we're, what we're trying to do now or what we should be aiming for is that Hello, Fresh is able to build data products that have a purpose. We're moving away from the idea. Data is just a by problem products, we have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to for the company as a business, we also want to provide them as a trust versi asset to the rest of the organization. We say there's the best customer experience, but at least in a way that users can easily discover, understand and security access high quality data. >>Yeah, so and and and Clements, when you c J Maxx writing, you see, you know, she has the four pillars and and the principles as practitioners you look at that say, okay, hey, that's pretty good thinking and then now we have to apply it and that's and that's where the devil meets the details. So it's the four, you know, the decentralized data ownership data as a product, which we'll talk about a little bit self serve, which you guys have spent a lot of time on inclement your wheelhouse which is which is governance and a Federated governance model. And it's almost like if you if you achieve the first two then you have to solve for the second to it almost creates a new challenges but maybe you could talk about that a little bit as to how it relates to Hello fresh. >>Yes. So christophe mentioned that we identified economic challenge beforehand and for how can we actually decentralized and actually empower the different colleagues of ours. This was more a we realized that it was more an organizational or a cultural change and this is something that somebody also mentioned I think thought words mentioned one of the white papers, it's more of a organizational or cultural impact and we kicked off a um faced reorganization or different phases we're currently and um in the middle of still but we kicked off different phases of organizational reconstruct oring reorganization, try unlock this data at scale. And the idea was really moving away from um ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do, what shall we do? This is value creation and how, which is capability building and both are equal in authority. This actually then creates a high urge and collaboration and this collaboration breaks up the different silos that were built and of course this also includes different needs of stuffing forward teams stuffing with more, let's say data scientists or data engineers, data professionals into those business domains and hence also more capability building. Um Okay, >>go ahead. Sorry. >>So back to Tzemach did johnny. So we the idea also Then crossed over when she published her papers in May 2019 and we thought well The four colors that she described um we're around decentralized data ownership, product data as a product mindset, we have a self service infrastructure and as you mentioned, Federated confidential governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then leads to a not only organisational restructure but also in completely new approach of how we need to manage data, show data. >>Got it. Okay, so your business is is exploding. Your data team will have to become domain experts in too many areas, constantly contact switching as we said, people started to take things into their own hands. So again we said classic story but but you didn't let it get out of control and that's important. So we actually have a picture of kind of where you're going today and it's evolved into this Pat, if you could bring up the picture with the the elephant here we go. So I would talk a little bit about the architecture, doesn't show it here, the spreadsheet era but christoph maybe you can talk about that. It does show the Hadoop monolith which exists today. I think that's in a managed managed hosting service, but but you you preserve that piece of it, but if I understand it correctly, everything is evolving to the cloud, I think you're running a lot of this or all of it in A W. S. Uh you've got everybody's got their own data sources, uh you've got a data hub which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure. That is really not the focus of this conversation today. But the key here, if I understand it correctly is these domains are autonomous and not only that this required technical thinking, but really supportive organizational mindset, which we're gonna talk about today. But christoph maybe you could address, you know, at a high level some of the architectural evolution that you guys went through. >>Yeah, sure. Yeah, maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning with the model is on the operation of playing right? Actually, it wasn't just one model is both to one for the back end and one for the for the front and and or analytical plane was essentially a couple of spreadsheets and I think there's nothing wrong with spreadsheets, right, allows you to store information, it allows you to transform data allows you to share this information. It allows you to visualize this data, but all the kind of that's not actually separating concern right? Everything in one tool. And this means that obviously not scalable, right? You reach the point where this kind of management set up in or data management of isn't one tool reached elements. So what we have started is we've created our data lake as we have seen here on Youtube. And this at the very beginning actually reflected very much our operational populace on top of that. We used impala is a data warehouse, but there was not really a distinction between borders, our data warehouse and borders our data like the impala was used as a kind of those as the kind of engine to create a warehouse and data like construct itself and this organic growth actually led to a situation as I think it's it's clear now that we had to centralized model is for all the domains that will really lose kimball modeling standards. There was no uniformity used actually build in house uh ways of building materialized use abuse that we have used for the presentation layer, there was a lot of duplication of effort and in the end essentially they were missing feedbacks, food, which helped us to to improve of what we are filled. So in the end, in the natural, as we have said, the lack of trust and that's basically what the starting point for us to understand. Okay, how can we move away and there are a lot of different things that you can discuss of apart from this organizational structure that we have said, okay, we have these three or four pillars from from Denmark. However, there's also the next extra question around how do we implement our talking about actual right, what are the implications on that level? And I think that is there's something that we are that we are currently still in progress. >>Got it. Okay, so I wonder if we could talk about switch gears a little bit and talk about the organizational and cultural challenges that you faced. What were those conversations like? Uh let's dig into that a little bit. I want to get into governance as well. >>The conversations on the cultural change. I mean yes, we went through a hyper growth for the last year since obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company which then results that collaboration uh >>got a bit more difficult. Of course >>there are times and changes, you have different different artifacts that you were created um and documentation that were flying around. Um so we were we had to build the company from scratch right? Um Of course this then resulted always this tension which I described before, but the most important part here is that data has always been a very important factor at l a fresh and we collected >>more of this >>data and continued to improve use data to improve the different key areas of our business. >>Um even >>when organizational struggles, the central organizational struggles data somehow always helped us to go through this this kind of change. Right? Um in the end those decentralized teams in our local geography ease started with solutions that serve the business which was very very important otherwise wouldn't be at the place where we are today but they did by all late best practices and standards and I always used sport analogy Dave So like any sport, there are different rules and regulations that need to be followed. These rules are defined by calling the sports association and this is what you can think about data governance and compliance team. Now we add the players to it who need to follow those rules and bite by them. This is what we then called data management. Now we have the different players and professionals, they need to be trained and understand the strategy and it rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in a different domains. And one of our mission of our data literacy program for example is to really empower >>every employee at hello >>fresh everyone to make the right data informs decisions by providing data education that scaled by royal Entry team. Then this can be different things, different things like including data capabilities, um, with the learning paths for example. Right? So help them to create and deploy data products connecting data producers and data consumers and create a common sense and more understanding of each other's dependencies, which is important, for example, S. S. L. O. State of contracts and etcetera. Um, people getting more of a sense of ownership and responsibility. Of course, we have to define what it means, what does ownership means? But the responsibility means. But we're teaching this to our colleagues via individual learning patterns and help them up skill to use. Also, there's shared infrastructure and those self self service applications and overall to summarize, we're still in this progress of of, of learning, we are still learning as well. So learning never stops the tele fish, but we are really trying this um, to make it as much fun as possible. And in the end we all know user behavior has changed through positive experience. Uh, so instead of having massive training programs over endless courses of workshops, um, leaving our new journalists and colleagues confused and overwhelmed. >>We're applying um, >>game ification, right? So split different levels of certification where our colleagues can access, have had access points, they can earn badges along the way, which then simplifies the process of learning and engagement of the users and this is what we see in surveys, for example, where our employees that your justification approach a lot and are even competing to collect Those learning path batteries to become the # one on the leader board. >>I love the game ification, we've seen it work so well and so many different industries, not the least of which is crypto so you've identified some of the process gaps uh that you, you saw it is gloss over them. Sometimes I say paved the cow path. You didn't try to force, in other words, a new architecture into the legacy processes. You really have to rethink your approach to data management. So what what did that entail? >>Um, to rethink the way of data management. 100%. So if I take the example of Revolution, Industrial Revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. Um, so we needed to establish a new set of cross functional business processes to run faster, dry faster, um, more robustly and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector with internal, I'm always referring to the data operations around new things like data catalog, how to identify >>ownership, >>how to change ownership, how to certify data assets, everything around classical software development, which we know apply to data. This this is similar to a new thinking, right? Um deployment, versioning, QA all the different things, ingestion policies, policing procedures, all the things that suffer. Development has been doing. We do it now with data as well. And in simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes and as a creation as management and as a consumption. >>So data has become kind of the new development kit. If you will um I want to shift gears and talk about the notion of data product and, and we have a slide uh that we pulled from your deck and I'd like to unpack it a little bit. Uh I'll just, if you can bring that up, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems where customers, both internal and external. So pretty straightforward. I know you've gone much deeper and you're thinking and into your organization, but how do you think about that And how do you determine for instance who owns what? How did you get everybody to agree? >>I can take that one. Um, maybe let me start with the data product. So I think um that's an ongoing debate. Right? And I think the debate itself is an important piece here, right? That visit the debate, you clarify what we actually mean by that product and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say okay that our product is something which is important for the company has come to its value what you mean by that. Okay, it's it's a solution to a customer problem that delivers ideally maximum value to the business. And yes, it leverages the power of data and we have a couple of examples but it had a fresh year, the historical and classical ones around dashboards for example, to monitor or error rates but also more sophisticated ways for example to incorporate machine learning algorithms in our recipe recommendations. However, I think the important aspects of the data product is a there is an owner, right? There's someone accountable for making sure that the product that we are providing is actually served and is maintained and there are, there is someone who is making sure that this actually keeps the value of that problem thing combined with the idea of the proper documentation, like a product description, right that people understand how to use their bodies is about and related to that peace is the idea of it is a purpose. Right? You need to understand or ask ourselves, Okay, why does this thing exist does it provide the value that you think it does. That leads into a good understanding about the life cycle of the data product and life cycle what we mean? Okay from the beginning from the creation you need to have a good understanding, we need to collect feedback, we need to learn about that. We need to rework and actually finally also to think about okay benefits time to decommission piece. So overall, I think the core of the data product is product thinking 11 right that we start the point is the starting point needs to be the problem and not the solution and this is essentially what we have seen what was missing but brought us to this kind of data spaghetti that we have built there in in Russia, essentially we built at certain data assets, develop in isolation and continuously patch the solution just to fulfill these articles that we got and actually these aren't really understanding of the stakeholder needs and the interesting piece as a result in duplication of work and this is not just frustrating and probably not the most efficient way how the company should work. But also if I build the same that assets but slightly different assumption across the company and multiple teams that leads to data inconsistency and imagine the following too narrow you as a management for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kind of grass, different kind of data and numbers and in the end you do not know which ones to trust. So there's actually much more ambiguity and you do not know actually is a noise for times of observing or is it just actually is there actually a signal that I'm looking for? And the same is if I'm running in a B test right, I have a new future, I would like to understand what has it been the business impact of this feature. I run that specific source in an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you've seen in a B test is actually not what you see then in production typical thing then is you're asking some analytics tend to actually do a deep dive to understand where the discrepancies are coming from. The worst case scenario. Again, there's a different kind of source. So in the end it's a pretty frustrating scenario and that's actually based of time of people that have to identify the root cause of this divergence. So in a nutshell, the highest degree of consistency is actually achieved that people are just reusing Dallas assets and also in the media talk that we have given right, we we start trying to establish this approach for a B testing. So we have a team but just providing or is kind of owning their target metric associated business teams and they're providing that as a product also to other services including the A B testing team, they'll be testing team can use this information defines an interface is okay I'm joining this information that the metadata of an experiment and in the end after the assignment after this data collection face, they can easily add a graph to the dashboard. Just group by the >>Beatles Hungarian. >>And we have seen that also in other companies. So it's not just a nice dream that we have right. I have actually worked in other companies where we worked on search and we established a complete KPI pipeline that was computing all this information. And this information was hosted by the team and it was used for everything A B test and deep dives and and regular reporting. So uh just one of the second the important piece now, why I'm coming back to that is that requires that we are treating this data as a product right? If you want to have multiple people using the things that I am owning and building, we have to provide this as a trust mercy asset and in a way that it's easy for people to discover and actually work with. >>Yeah. And coming back to that. So this is to me this is why I get so excited about data mesh because I really do think it's the right direction for organizations. When people hear data product they say well, what does that mean? Uh but then when you start to sort of define it as you did, it's it's using data to add value, that could be cutting costs, that could be generating revenue, it could be actually directly you're creating a product that you monetize, So it's sort of in the eyes of the beholder. But I think the other point that we've made is you made it earlier on to and again, context. So when you have a centralized data team and you have all these P NL managers a lot of times they'll question the data because they don't own it. They're like wait a minute. If they don't, if it doesn't agree with their agenda, they'll attack the data. But if they own the data then they're responsible for defending that and that is a mindset change, that's really important. Um And I'm curious uh is how you got to, you know, that ownership? Was it a was it a top down with somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what in other words, you know, did you get, how did you get the business to take ownership of the data and what is owning? You know, the data actually mean? >>That's a very good question. Dave I think this is one of the pieces where I think we have a lot of learnings and basically if you ask me how we could start the feeling. I think that would be the first piece. Maybe we need to start to really think about how that should be approached if it stopped his ownership. Right? It means somehow that the team has a responsibility to host and self the data efforts to minimum acceptable standards. This minimum dependencies up and down string. The interesting piece has been looking backwards. What what's happening is that under that definition has actually process that we have to go through is not actually transferring ownership from the central team to the distributor teams. But actually most cases to establish ownership, I make this difference because saying we have to transfer ownership actually would erroneously suggests that the data set was owned before. But this platform team, yes, they had the capability to make the changes on data pipelines, but actually the analytics team, they're always the ones who had the business understands, you use cases and but no one actually, but it's actually expensive expected. So we had to go through this very lengthy process and establishing ownership. We have done that, as in the beginning, very naively. They have started, here's a document here, all the data assets, what is probably the nearest neighbor who can actually take care of that and then we we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent over years and these people who have built this thing have already left the company. So there's actually not a nice thing that is that you want to see and people build up a certain resistance, e even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, but what needs to happen as first, the company needs to really understand what our core business concept that they have, they need to have this mapping from. These are the core business concept that we have. These are the domain teams who are owning this concept and then actually link that to the to the assets and integrated better with both understanding how we can evolve actually, the data assets and new data build things new in the in this piece in the domain. But also how can we address reduction of technical death and stabilizing what we have already. >>Thank you for that christoph. So I want to turn a direction here and talk about governance and I know that's an area that's passionate, you're passionate about. Uh I pulled this slide from your deck, which I kind of messed up a little bit sorry for that, but but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks. But it's one of the most challenging aspects of data mesh, if you're going to decentralize you, you quickly realize this could be the Wild West as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy, compliance etcetera. So, so how did you approach this? >>It's yeah, it's about connecting those dots. Right. So the aim of the data governance program is about the autonomy of every team was still ensuring that everybody has the right interoperability. So when we want to move from the Wild West riding horses to a civilised way of transport, um you can take the example of modern street traffic, like when all participants can manoeuvre independently and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights in the different signals. Um, so likewise as a business and Hello Fresh, we do operate autonomously and consequently need to follow those external and internal rules and standards to set forth by the redistribution in which we operate so in order to prevent a car crash, we need to at least ensure compliance with regulations to account for society's and our customers increasing concern with data protection and privacy. So teaching and advocating this advantage, realizing this to everyone in the company um was a key community communication strategy and of course, I mean I mentioned data privacy external factors, the same goes for internal regulations and processes to help our colleagues to adapt to this very new environment. So when I mentioned before the new way of thinking the new way of um dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. Um in a nutshell then this means the data governance provides a framework for managing our people the processes and technology and culture around our data traffic. And those components must come together in order to have this effective program providing at least a common denominator, especially critical for shared dataset, which we have across our different geographies managed and shared applications on shared infrastructure and applications and is then consumed by centralized processes um for example, master data, everything and all the metrics and KPI s which are also used for a central steering. Um it's a big change day. Right. And our ultimate goal is to have this noninvasive, Federated um ultimatum and computational governance and for that we can't just talk about it. We actually have to go deep and use case by use case and Qc buy PVC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status by identifying together with the business teams with the different domains have a risk assessment for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of illiteracy comes into place where we go in and trade based on the findings based on the most valuable use case um and based on that help our teams to do this change to increase um their capability just a little bit more and once they hand holding. But a lot of guidance >>can I kind of kind of trying to quickly David will allow me I mean there's there's a lot of governance piece but I think um that is important. And if you're talking about documentation for example, yes, we can go from team to team and tell these people how you have to document your data and data catalog or you have to establish data contracts and so on the force. But if you would like to build data products at scale following actual governance, we need to think about automation right. We need to think about a lot of things that we can learn from engineering before. And that starts with simple things like if we would like to build up trust in our data products, right, and actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do and we should probably think about what we can copy and one example might be. So the level of service level agreements, service level objectives. So that level indicators right, that represent on on an engineering level, right? If we're providing services there representing the promises we made to our customers or consumers, these are the internal objectives that help us to keep those promises. And actually these are the way of how we are tracking ourselves, how we are doing. And this is just one example of that thing. The Federated Governor governance comes into play right. In an ideal world, we should not just talk about data as a product but also data product. That's code that we say, okay, as most as much as possible. Right? Give the engineers the tool that they are familiar basis and actually not ask the product managers for example to document their data assets in the data catalog but make it part of the configuration. Have this as a, as a C D C I, a continuous delivery pipeline as we typically see another engineering task through and services we say, okay, there is configuration, we can think about pr I can think about data quality monitoring, we can think about um the ingestion data catalog and so on and forest, I think ideally in the data product will become of a certain templates that can be deployed and are actually rejected or verified at build time before we actually make them deploy them to production. >>Yeah, So it's like devoPS for data product um so I'm envisioning almost a three phase approach to governance and you kind of, it sounds like you're in early phases called phase zero where there's there's learning, there's literacy, there's training, education, there's kind of self governance and then there's some kind of oversight, some a lot of manual stuff going on and then you you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >>Yeah, I would rather think think about automation as early as possible in the way and yes, there needs to be certain rules but then actually start actually use case by use case. Is there anything that small piece that we can already automate? It's as possible. Roll that out and then actually extended step by step, >>is there a role though that adjudicates that? Is there a central Chief state officer who is responsible for making sure people are complying or is it how do you handle that? >>I mean from a from a from a platform perspective, yes, we have a centralized team to uh implement certain pieces they'll be saying are important and actually would like to implement. However, that is actually working very closely with the governance department. So it's Clements piece to understand and defy the policies that needs to be implemented. >>So Clements essentially it's it's your responsibility to make sure that the policy is being followed. And then as you were saying, christoph trying to compress the time to automation as fast as possible percent. >>So >>it's really it's uh >>what needs to be really clear that it's always a split effort, Right? So you can't just do one thing or the other thing, but everything really goes hand in hand because for the right automation for the right engineering tooling, we need to have the transparency first. Uh I mean code needs to be coded so we kind of need to operate on the same level with the right understanding. So there's actually two things that are important which is one its policies and guidelines, but not only that because more importantly or even well equally important to align with the end user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >>Got it. So just a couple more questions because we gotta wrap I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment but but major learnings, we've got some of the challenges that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks. But my question, I mean this is the advice for your peers question if you had to do it differently if you had a do over or a Mulligan as we like to say for you golfers, what would you do differently? Yeah, >>I mean can we start with from a from the transformational challenge that understanding that it's also high load of cultural change. I think this is this is important that a particular communication strategy needs to be put into place and people really need to be um supported. Right? So it's not that we go in and say well we have to change towards data mesh but naturally it's in human nature, you know, we're kind of resistance to to change right? Her speech uncomfortable. So we need to take that away by training and by communicating um chris we're gonna add something to that >>and definitely I think the point that I have also made before right we need to acknowledge that data mesh is an architecture of scale, right? You're looking for something which is necessary by huge companies who are vulnerable, data productive scale. I mean Dave you mentioned it right, there are a lot of advantages to have a centralized team but at some point it may make sense to actually decentralized here and at this point right? If you think about data Mash, you have to recognize that you're not building something on a green field. And I think there's a big learning which is also reflected here on the slide is don't underestimate your baggage. It's typically you come to a point where the old model doesn't doesn't broke anymore and has had a fresh right? We lost our trust in our data and actually we have seen certain risks that we're slowing down our innovation so we triggered that this was triggering the need to actually change something. So this transition implies that you typically have a lot of technical debt accumulated over years and I think what we have learned is that potentially we have decentralized some assets to earlier, this is not actually taking into account the maturity of the team where we are actually distributed to and now we actually in the face of correcting pieces of that one. Right? But I think if you if you if you start from scratch you have to understand, okay, is are my team is actually ready for taking on this new uh, this news capabilities and you have to make sure that business decentralization, you build up these >>capabilities and the >>teams and as Clements has mentioned, right, make sure that you take the people on your journey. I think these are the pieces that also here, it comes with this knowledge gap, right? That we need to think about hiring and literacy the technical depth I just talked about and I think the last piece that I would add now which is not here on the flight deck is also from our perspective, we started on the analytical layer because that's kind of where things are exploding, right, this is the thing that people feel the pain but I think a lot of the efforts that we have started to actually modernize the current state uh, towards data product towards data Mash. We've understood that it always comes down basically to a proper shape of our operational plane and I think what needs to happen is is I think we got through a lot of pains but the learning here is this need to really be a commitment from the company that needs to happen and to act. >>I think that point that last point you made it so critical because I I hear a lot from the vendor community about how they're gonna make analytics better and that's that's not unimportant, but but through data product thinking and decentralized data organizations really have to operationalize in order to scale. So these decisions around data architecture an organization, their fundamental and lasting, it's not necessarily about an individual project are why they're gonna be project sub projects within this architecture. But the architectural decision itself is an organizational, its cultural and what's the best approach to support your business at scale. It really speaks to to to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data driven driven companies is yields tremendous results. So I'll ask each of you to give give us your final thoughts and then we'll wrap maybe >>maybe it quickly, please. Yeah, maybe just just jumping on this piece that you have mentioned, right, the target architecture. If we talk about these pieces right, people often have this picture of mind like OK, there are different kind of stages, we have sources, we have actually ingestion layer, we have historical transformation presentation layer and then we're basically putting a lot of technology on top of that kind of our target architecture. However, I think what we really need to make sure is that we have these different kind of viewers, right? We need to understand what are actually the capabilities that we need in our new goals. How does it look and feel from the different kind of personas and experience view? And then finally, that should actually go to the to the target architecture from a technical perspective um maybe just to give an outlook but what we're what we're planning to do, how we want to move that forward. We have actually based on our strategy in the in the sense of we would like to increase that to maturity as a whole across the entire company and this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data, cultural, data literacy, data organizational structure and so on that. We're talking about governance as Clements has actually mentioned that, right, compliance, governance, data management and so on. You talk about technology and I think we could talk for hours for that one. It's around data platform, better science platform and then finally also about enablement through data, meaning we need to understand that a quality data accessibility and the science and data monetization. >>Great, thank you christophe clement. Once you bring us home give us your final thoughts. >>Can't can just agree with christoph that uh important is to understand what kind of maturity people have to understand what the maturity level, where the company where where people organization is and really understand what does kind of some kind of a change replies to that those four pillars for example, um what needs to be taken first and this is not very clear from the very first beginning of course them it's kind of like Greenfield you come up with must wins to come up with things that we really want to do out of theory and out of different white papers. Um only if you really start conducting the first initiatives you do understand. Okay, where we have to put the starts together and where do I missed out on one of those four different pillars? People, process technology and governance. Right? And then that kind of an integration. Doing step by step, small steps by small steps not boiling the ocean where you're capable ready to identify the gaps and see where either you can fill um the gaps are where you have to increase maturity first and train people or increase your text text, >>you know Hello Fresh is an excellent example of a company that is innovating. It was not born in Silicon Valley which I love. It's a global company. Uh and I gotta ask you guys, it seems like this is an amazing place to work you guys hiring? >>Yes, >>definitely. We do >>uh as many rights as was one of these aspects distributing. And actually we are hiring as an entire company specifically for data. I think there are a lot of open roles serious. Please visit or our page from better engineering, data, product management and Clemens has a lot of rules that you can speak about. But yes >>guys, thanks so much for sharing with the cube audience, your, your pioneers and we look forward to collaborations in the future to track progress and really want to thank you for your time. >>Thank you very much. Thank you very much. Dave >>thank you for watching the cubes startup showcase made possible by A W. S. This is Dave Volonte. We'll see you next time. >>Yeah.

(upbeat music) >> Hello everyone. We're here at theCUBE startup showcase made possible by AWS. Thanks so much for joining us today. You know, when Zhamak Dehghani was formulating her ideas around data mesh, she wasn't the only one thinking about decentralized data architectures. HelloFresh was going into hyper-growth mode and realized that in order to support its scale, it needed to rethink how it thought about data. Like many companies that started in the early part of the last decade, HelloFresh relied on a monolithic data architecture and the internal team it had concerns about its ability to support continued innovation at high velocity. The company's data team began to think about the future and work backwards from a target architecture, which possessed many principles of so-called data mesh, even though they didn't use that term specifically. The company is a strong example of an early but practical pioneer of data mesh. Now, there are many practitioners and stakeholders involved in evolving the company's data architecture many of whom are listed here on this slide. Two are highlighted in red and joining us today. We're really excited to welcome you to theCUBE, Clemence Chee, who is the global senior director for data at HelloFresh, and Christoph Sawade, who's the global senior director of data also of course at HelloFresh. Folks, welcome. Thanks so much for making some time today and sharing your story. >> Thank you very much. >> Thanks, Dave. >> All right, let's start with HelloFresh. You guys are number one in the world in your field. You deliver hundreds of millions of meals each year to many, many millions of people around the globe. You're scaling. Christoph, tell us a little bit more about your company and its vision. >> Yeah. Should I start or Clemence? Maybe take over the first piece because Clemence has actually been longer a director at HelloFresh. >> Yeah go ahead Clemence. >> I mean, yes, about approximately six years ago I joined and HelloFresh, and I didn't think about the startup I was joining would eventually IPO. And just two years later, HelloFresh went public. And approximately three years and 10 months after HelloFresh was listed on the German stock exchange which was just last week, HelloFresh was included in the DAX Germany's leading stock market index and that, to mind a great, great milestone, and I'm really looking forward and I'm very excited for the future for HelloFresh and also our data. The vision that we have is to become the world's leading food solution group. And there are a lot of attractive opportunities. So recently we did launch and expand in Norway. This was in July. And earlier this year, we launched the US brand, Green Chef, in the UK as well. We're committed to launch continuously different geographies in the next coming years and have a strong path ahead of us. With the acquisition of ready to eat companies like factor in the US and the plant acquisition of Youfoodz in Australia, we are diversifying our offer, now reaching even more and more untapped customer segments and increase our total address for the market. So by offering customers and growing range of different alternatives to shop food and to consume meals, we are charging towards this vision and this goal to become the world's leading integrated food solutions group. >> Love it. You guys are on a rocket ship. You're really transforming the industry. And as you expand your TAM, it brings us to sort of the data as a core part of that strategy. So maybe you guys could talk a little bit about your journey as a company, specifically as it relates to your data journey. I mean, you began as a startup, you had a basic architecture and like everyone, you've made extensive use of spreadsheets, you built a Hadoop based system that started to grow. And when the company IPO'd, you really started to explode. So maybe describe that journey from a data perspective. >> Yes, Dave. So HelloFresh by 2015, approximately had evolved what amount, a classical centralized data management set up. So we grew very organically over the years, and there were a lot of very smart people around the globe, really building the company and building our infrastructure. This also means that there were a small number of internal and external sources, data sources, and a centralized BI team with a number of people producing different reports, different dashboards and, and products for our executives, for example, or for different operations teams to see a company's performance and knowledge was transferred just by our talking to each other face-to-face conversations. And the people in the data warehouse team were considered as the data wizard or as the ETL wizard. Very classical challenges. And it was ETL, who reserved, indicated the kind of like a style of knowledge of data management, right? So our central data warehouse team then was responsible for different type of verticals in different domains, different geographies. And all this setup gave us in the beginning, the flexibility to grow fast as a company in 2015. >> Christoph, anything to add to that? >> Yes, not explicitly to that one, but as, as Clemence said, right, this was kind of the setup that actually worked for us quite a while. And then in 2017, when HelloFresh went public, the company also grew rapidly. And just to give you an idea how that looked like as well, the tech departments have actually increased from about 40 people to almost 300 engineers. And in the same way as the business units, as there Clemence has described, also grew sustainably. So we continue to launch HelloFresh in new countries, launched new brands like Every Plate, and also acquired other brands like we have Factor. And that grows also from a data perspective, the number of data requests that the central (mumbles), we're getting become more and more and more, and also more and more complex. So that for the team meant that they had a fairly high mental load. So they had to achieve a very, or basically get a very deep understanding about the business and also suffered a lot from this context, switching back and forth. Essentially, they had to prioritize across our product requests from our physical product, digital product, from a physical, from, sorry, from the marketing perspective, and also from the central reporting teams. And in a nutshell, this was very hard for these people, and that altered situations that let's say the solution that we have built. We can not really optimal. So in a, in a, in a, in a nutshell, the central function became a bottleneck and slow down of all the innovation of the company. >> It's a classic case. Isn't it? I mean, Clemence, you see, you see the central team becomes a bottleneck, and so the lines of business, the marketing team, sales teams say "Okay, we're going to take things into our own hands." And then of course IT and the technical team is called in later to clean up the mess. Maybe, maybe I'm overstating it, but, but that's a common situation. Isn't it? >> Yeah this is what exactly happened. Right. So we had a bottleneck, we had those central teams, there was always a bit of tension. Analytics teams then started in those business domains like marketing, supply chain, finance, HR, and so on started really to build their own data solutions. At some point you have to get the ball rolling, right? And then continue the trajectory, which means then that the data pipelines didn't meet the engineering standards. And there was an increased need for maintenance and support from central teams. Hence over time, the knowledge about those pipelines and how to maintain a particular infrastructure, for example, left the company, such that most of those data assets and data sets that turned into a huge debt with decreasing data quality, also decreasing lack of trust, decreasing transparency. And this was an increasing challenge where a majority of time was spent in meeting rooms to align on, on data quality for example. >> Yeah. And the point you were making Christoph about context switching, and this is, this is a point that Zhamak makes quite often as we've, we've, we've contextualized our operational systems like our sales systems, our marketing systems, but not our, our data systems. So you're asking the data team, okay, be an expert in sales, be an expert in marketing, be an expert in logistics, be an expert in supply chain and it's start, stop, start, stop. It's a paper cut environment, and it's just not as productive. But, but, and the flip side of that is when you think about a centralized organization, you think, hey, this is going to be a very efficient way across functional team to support the organization, but it's not necessarily the highest velocity, most effective organizational structure. >> Yeah. So, so I agree with that piece, that's up to a certain scale. A centralized function has a lot of advantages, right? So it's a tool for everyone, which would go to a destined kind of expert team. However, if you see that you actually would like to accelerate that in specific as the type of growth. But you want to actually have autonomy on certain teams and move the teams, or let's say the data to the experts in these teams. And this, as you have mentioned, right, that increases mental load. And you can either internally start splitting your team into different kinds of sub teams focusing on different areas, however, that is then again, just adding another piece where actually collaboration needs to happen because the external seized, so why not bridging that gap immediately and actually move these teams end to end into the, into the function themselves. So maybe just to continue what Clemence was saying, and this is actually where our, so, Clemence and my journey started to become one joint journey. So Clemence was coming actually from one of these teams who builds their own solutions. I was basically heading the platform team called data warehouse team these days. And in 2019, where (mumbles) become more and more serious, I would say, so more and more people have recognized that this model does not really scale, in 2019, basically the leadership of the company came together and identified data as a key strategic asset. And what we mean by that, that if he leveraged it in a, in a, an appropriate way, it gives us a unique, competitive advantage, which could help us to, to support and actually fully automate our decision making process across the entire value chain. So once we, what we're trying to do now, or what we would be aiming for is that HelloFresh is able to build data products that have a purpose. We're moving away from the idea that it's just a bi-product. We have a purpose why we would like to collect this data. There's a clear business need behind that. And because it's so important to, for the company as a business, we also want to provide them as a trustworthy asset to the rest of the organization. We'd say, this is the best customer experience, but at least in a way that users can easily discover, understand and securely access, high quality data. >> Yeah. So, and, and, and Clemence, when you see Zhamak's writing, you see, you know, she has the four pillars and the principles. As practitioners, you look at that say, okay, hey, that's pretty good thinking. And then now we have to apply it. And that's where the devil meets the details. So it's the for, the decentralized data ownership, data as a product, which we'll talk about a little bit, self-serve, which you guys have spent a lot of time on, and Clemence your wheelhouse, which is, which is governance and a federated governance model. And it's almost like if you, if you achieve the first two, then you have to solve for the second two, it almost creates a new challenges, but maybe you could talk about that a little bit as to how it relates to HelloFresh. >> Yes. So Chris has mentioned that we identified kind of a challenge beforehand and said, how can we actually decentralized and actually empower the different colleagues of ours? And this was more a, we realized that it was more an organizational or a cultural change. And this is something that someone also mentioned. I think ThoughtWorks mentioned one of the white papers, it's more of an organizational or a cultural impact. And we kicked off a phased reorganization, or different phases we're currently on, in the middle of still, but we kicked off different phases of organizational restructuring or reorganization trying to lock this data at scale. And the idea was really moving away from ever growing complex matrix organizations or matrix setups and split between two different things. One is the value creation. So basically when people ask the question, what can we actually do? What should we do? This is value creation and the how, which is capability building, and both are equal in authority. This actually then creates a high urge in collaboration and this collaboration breaks up the different silos that were built. And of course, this also includes different needs of staffing for teams staffing with more, let's say data scientists or data engineers, data professionals into those business domains, enhance, or some more capability building. >> Okay, go ahead. Sorry. >> So back to Zhamak Dehghani. So we, the idea also then crossed over when she published her papers in May, 2019. And we thought, well, the four pillars that she described were around decentralized data ownership, product, data as a product mindset, we have a self-service infrastructure. And as you mentioned, federated computational governance. And this suited very much with our thinking at that point of time to reorganize the different teams and this then that to not only organizational restructure, but also in completely new approach of how we need to manage data, through data. >> Got it. Okay. So your businesses is exploding. The data team was having to become domain experts to many areas, constantly context switching as we said, people started to take things into their own hands. So again, we said classic story, but, but you didn't let it get out of control and that's important. And so we, we actually have a picture of kind of where you're going today and it's evolved into this, Pat, if you could bring up the picture with the, the elephant, here we go. So I will talk a little bit about the architecture. It doesn't show it here, the spreadsheet era, but Christoph, maybe you could talk about that. It does show the Hadoop monolith, which exists today. I think that's in a managed hosting service, but, but you, you preserve that piece of it. But if I understand it correctly, everything is evolving to the cloud. I think you're running a lot of this or all of it in AWS. You've got, everybody's got their own data sources. You've got a data hub, which I think is enabled by a master catalog for discovery and all this underlying technical infrastructure that is, is really not the focus of this conversation today. But the key here, if I understand correctly is these domains are autonomous and that not only this required technical thinking, but really supportive organizational mindset, which we're going to talk about today. But, but Christoph, maybe you could address, you know, at a high level, some of the architectural evolution that you guys went through. >> Yeah, sure. Yeah. Maybe it's also a good summary about the entire history. So as you have mentioned, right, we started in the very beginning, it's a monolith on the operational plan, right? Actually it wasn't just one model it was two, one for the backend and one for the front end. And our analytical plan was essentially a couple of spreadsheets. And I think there's nothing wrong with spreadsheets, but it allows you to store information, it allows you to transform data, it allows you to share this information, it allows you to visualize this data, but all kind of, it's not actually separating concern, right? Every single one tool. And this means that it's obviously not scalable, right? You reach the point where this kind of management's set up in, or data management is in one tool, reached elements. So what we have started is we created our data lake, as we have seen here on our dupe. And just in the very beginning actually reflected very much our operation upon this. On top of that, we used Impala as a data warehouse, but there was not really a distinction between what is our data warehouse and what is our data lakes as the Impala was used as kind of both as a kind of engine to create a warehouse and data lake constructed itself. And this organic growth actually led to a situation. As I think it's clear now that we had the centralized model as, for all the domains that were really lose Kimball, the modeling standards and there's new uniformity we used to actually build, in-house, a base of building materialized use, of use that we have used for the presentation there. There was a lot of duplication of effort. And in the end, essentially the amendments and feedback tool, which helped us to, to improve of what we, have built during the end in a natural, as you said, the lack of trust. And this basically was a starting point for us to understand, okay, how can we move away? And there are a lot of different things that we can discuss of apart from this organizational structure that we have set up here, we have three or four pillars from Zhamak. However, there's also the next, extra question around, how do we implement product, right? What are the implications on that level and I think that is, that's something that we are, that we are currently still in progress. >> Got it. Okay. So I wonder if we could talk about, switch gears a little bit, and talk about the organizational and cultural challenges that you faced. What were those conversations like? And let's, let's dig into that a little bit. I want to get into governance as well. >> The conversations on the cultural change. I mean, yes, we went through a hyper growth through the last year, and obviously there were a lot of new joiners, a lot of different, very, very smart people joining the company, which then results that collaborations got a bit more difficult. Of course, the time zone changes. You have different, different artifacts that you had recreated in documentation that were flying around. So we were, we had to build the company from scratch, right? Of course, this then resulted always this tension, which I described before. But the most important part here is that data has always been a very important factor at HelloFresh, and we collected more of this data and continued to improve, use data to improve the different key areas of our business. Even when organizational struggles like the central (mumbles) struggles, data somehow always helped us to grow through this kind of change, right? In the end, those decentralized teams in our local geographies started with solutions that serve the business, which was very, very important. Otherwise, we wouldn't be at the place where we are today, but they did violate best practices and standards. And I always use the sports analogy, Dave. So like any sport, there are different rules and regulations that need to be followed. These routes are defined by, I'll call it, the sports association. And this is what you can think about other data governance and then our compliance team. Now we add the players to it who need to follow those rules and abide by them. This is what we then call data management. Now we have the different players, the professionals they also need to be trained and understand the strategy and the rules before they can play. And this is what I then called data literacy. So we realized that we need to focus on helping our teams to develop those capabilities and teach the standards for how work is being done to truly drive functional excellence in the different domains. And one of our ambition of our data literacy program for example, is to really empower every employee at HelloFresh, everyone, to make the right data-informed decisions by providing data education that scales (mumbles), and that can be different things. Different things like including data capabilities with, in the learning path for example, right? So help them to create and deploy data products, connecting data, producers, and data consumers, and create a common sense and more understanding of each other's dependencies, which is important. For example, SIS, SLO, state of contracts, et cetera, people get more of a sense of ownership and responsibility. Of course, we have to define what it means. What does ownership means? What does responsibility mean? But we are teaching this to our colleagues via individual learning patterns and help them upscale to use also their shared infrastructure, and those self-service data applications. And of all to summarize, we are still in this progress of learning. We're still learning as well. So learning never stops at Hello Fresh, but we are really trying this to make it as much fun as possible. And in the end, we all know user behavior is changed through positive experience. So instead of having massive training programs over endless courses of workshops, leaving our new joiners and colleagues confused and overwhelmed, we're applying gamification, right? So split different levels of certification where our colleagues, can access, have had access points. They can earn badges along the way, which then simplifies the process of learning and engagement of the users. And this is what we see in surveys, for example, where our employees value this gamification approach a lot and are even competing to collect those learning pet badges, to become the number one on the leaderboard. >> I love the gamification. I mean, we've seen it work so well in so many different industries, not the least of which is crypto. So you've identified some of the process gaps that you, you saw, you just gloss over them. Sometimes I say, pave the cow path. You didn't try to force. In other words, a new architecture into the legacy processes, you really had to rethink your approach to data management. So what did that entail? >> To rethink the way of data management, 100%. So if I take the example of revolution, industrial revolution or classical supply chain revolution, but just imagine that you have been riding a horse, for example, your whole life, and suddenly you can operate a car or you suddenly receive just a complete new way of transporting assets from A to B. So we needed to establish a new set of cross-functional business processes to run faster, drive faster, more robustly, and deliver data products which can be trusted and used by downstream processes and systems. Hence we had a subset of new standards and new procedures that would fall into the internal data governance and compliance sector. With internal, I'm always referring to the data operations around new things like data catalog, how to identify ownership, how to change ownership, how to certify data assets, everything around classical is software development, which we now apply to data. This, this is some old and new thinking, right? Deployment, versioning, QA, all the different things, ingestion policies, the deletion procedures, all the things that software development has been doing, we do it now with data as well. And it's simple terms, it's a whole redesign of the supply chain of our data with new procedures and new processes in asset creation, asset management and asset consumption. >> So data's become kind of the new development kit, if you will. I want to shift gears and talk about the notion of data product, and we have a slide that, that we pulled from your deck. And I'd like to unpack it a little bit. I'll just, if you can bring that up, I'll, I'll read it. A data product is a product whose primary objective is to leverage on data to solve customer problems, where customers are both internal and external. so pretty straightforward. I know you've, you've gone much deeper in your thinking and into your organization, but how do you think about that and how do you determine for instance, who owns what, how did you get everybody to agree? >> I can take that one. Maybe let me start as a data product. So I think that's an ongoing debate, right? And I think the debate itself is the important piece here, right? You mentioned the debate, you've clarified what we actually mean by that, a product, and what is actually the mindset. So I think just from a definition perspective, right? I think we find the common denominator that we say, okay, that our product is something which is important for the company that comes with value. What do you mean by that? Okay. It's a solution to a customer problem that delivers ideally maximum value to the business. And yes, leverage is the power of data. And we have a couple of examples, and I'll hit refresh here, the historical and classical ones around dashboards, for example, to monitor our error rates, but also more sophisticated based for example, to incorporate machine learning algorithms in our recipe recommendation. However, I think the important aspects of a data product is A: there is an owner, right? There's someone accountable for making sure that the product that you're providing is actually served and has maintained. And there are, there's someone who's making sure that this actually keeps the value of what we are promising. Combined with the idea of the proper documentation, like a product description, right? The people understand how to use it. What is this about? And related to that piece is the idea of, there's a purpose, right? We need to understand or ask ourselves, okay, why does a thing exist? Does it provide the value that we think it does? Then it leads in to a good understanding of what the life cycle of the data product and product life cycle. What do we mean? Okay. From the beginning, from the creation, you need to have a good understanding. You need to collect feedback. We need to learn about that, you need to rework, and actually finally, also to think about, okay, when is it time to decommission that piece So overall I think the core of this data product is product thinking 101, right? That we start, the point is, the starting point needs to be the problem and not the solution. And this is essentially what we have seen, what was missing, what brought us to this kind of data spaghetti that we have built there in Rush, essentially, we built it. Certain data assets develop in isolation and continuously patch the solution just to fulfill these ad hoc requests that we got and actually really understanding what the stakeholder needs. And the interesting piece as a results in duplication of (mumbled) And this is not just frustrating and probably not the most efficient way, how the company should work. But also if I build the same data assets, but slightly different assumption across the company and multiple teams that leads to data inconsistency. And imagine the following scenario. You, as a management, for management perspective, you're asking basically a specific question and you get essentially from a couple of different teams, different kinds of graphs, different kinds of data and numbers. And in the end, you do not know which ones to trust. So there's actually much (mumbles) but good. You do not know what actually is it noise for times of observing or is it just actually, is there actually a signal that I'm looking for? And the same as if I'm running an AB test, right? I have a new feature, I would like to understand what is the business impact of this feature? I run that with a specific source and an unfortunate scenario. Your production system is actually running on a different source. You see different numbers. What you have seen in the AB test is actually not what you see then in production, typical thing. Then as you asking some analytics team to actually do a deep dive, to understand where the discrepancies are coming from, worst case scenario again, there's a different kind of source. So in the end, it's a pretty frustrating scenario. And it's actually a waste of time of people that have to identify the root cause of this type of divergence. So in a nutshell, the highest degree of consistency is actually achieved if people are just reusing data assets. And also in the end, the meetup talk they've given, right? We start trying to establish this approach by AB testing. So we have a team, but just providing, or is kind of owning their target metric associated business teams, and they're providing that as a product also to other services, including the AB testing team. The AB testing team can use this information to find an interface say, okay, I'm drawing information for the metadata of an experiment. And in the end, after the assignment, after this data collection phase, they can easily add a graph to a dashboard just grouped by the AB testing barrier. And we have seen that also in other companies. So it's not just a nice dream that we have, right? I have actually looked at other companies maybe looked on search and we established a complete KPI pipeline that was computing all these information and this information both hosted by the team and those that (mumbles) AB testing, deep dives and, and regular reporting again. So just one last second, the, the important piece, Now, why I'm coming back to that is that it requires that we are treating this data as a product, right? If we want to have multiple people using the thing that I am owning and building, we have to provide this as a trust (mumbles) asset and in a way that it's easy for people to discover and to actually work with. >> Yeah. And coming back to that. So this is, to me this is why I get so excited about data mesh, because I really do think it's the right direction for organizations. When people hear data product, they think, "Well, what does that mean?" But then when you start to sort of define it as you did, it's using data to add value that could be cutting costs, that could be generating revenue, it could be actually directly creating a product that you monetize. So it's sort of in the eyes of the beholder, but I think the other point that we've made, is you made it earlier on too, and again, context. So when you have a centralized data team and you have all these P&L managers, a lot of times they'll question the data 'cause they don't own it. They're like, "Well, wait a minute." If it doesn't agree with their agenda, they'll attack the data. But if they own the data, then they're responsible for defending that. And that is a mindset change that's really important. And I'm curious is how you got to that ownership. Was it a top-down or was somebody providing leadership? Was it more organic bottom up? Was it a sort of a combination? How do you decide who owned what? In other words, you know, did you get, how did you get the business to take ownership of the data and what does owning the data actually mean? >> That's a very good question, Dave. I think that one of the pieces where I think we have a lot of learning and basically if you ask me how we could stop the filling, I think that would be the first piece that we need to start. Really think about how that should be approached. If it's staff has ownership, right? That means somehow that the team has the responsibility to host themselves the data assets to minimum acceptable standards. That's minimum dependencies up and down stream. The interesting piece has to be looking backwards. What was happening is that under that definition, this extra process that we have to go through is not actually transferring ownership from a central team to the other teams, but actually in most cases to establish ownership. I make this difference because saying we have to transfer ownership actually would erroneously suggest that the dataset was owned before, but this platform team, yes, they had the capability to make the change, but actually the analytics team, but always once we had the business understand the use cases and what no one actually bought, it's actually expensive, expected. So we had to go through this very lengthy process and establishing ownership, how we have done that as in the beginning, very naively started, here's a document, here are all the data assets, what is probably the nearest neighbor who can actually take care of that. And then we, we moved it over. But the problem here is that all these things is kind of technical debt, right? It's not really properly documented, pretty unstable. It was built in a very inconsistent way over years. And these people that built this thing have already left the company. So this is actually not a nice thing that you want to see and people build up a certain resistance, even if they have actually bought into this idea of domain ownership. So if you ask me these learnings, what needs to happen is first, the company needs to really understand what our core business concept that we have the need to have this mapping from this other core business concept that we have. These are the domain teams who are owning this concept, and then actually linked that to the, the assets and integrate that better, but suppose understanding how we can evolve, actually the data assets and new data builds things new and the, in this piece and the domain, but also how can we address reduction of technical depth and stabilizing what we have already. >> Thank you for that Christoph. So I want to turn a direction here and talk Clemence about governance. And I know that's an area that's passionate, you're passionate about. I pulled this slide from your deck, which I kind of messed up a little bit, sorry for that. But, but, but by the way, we're going to publish a link to the full video that you guys did. So we'll share that with folks, but it's one of the most challenging aspects of data mesh. If you're going to decentralize, you, you quickly realize this could be the wild west, as we talked about all over again. So how are you approaching governance? There's a lot of items on this slide that are, you know, underscore the complexity, whether it's privacy compliance, et cetera. So, so how did you approach this? >> It's yeah, it's about connecting those dots, right? So the aim of the data governance program is to promote the autonomy of every team while still ensuring that everybody has the right interoperability. So when we want to move from the wild west, riding horses to a civilized way of transport, I can take the example of modern street traffic. Like when all participants can maneuver independently, and as long as they follow the same rules and standards, everybody can remain compatible with each other and understand and learn from each other so we can avoid car crashes. So when I go from country to country, I do understand what the street infrastructure means. How do I drive my car? I can also read the traffic lights and the different signals. So likewise, as a business in HelloFresh we do operate autonomously and consequently need to follow those external and internal rules and standards set forth by the tradition in which we operate. So in order to prevent a, a car crash, we need to at least ensure compliance with regulations, to account for societies and our customers' increasing concern with data protection and privacy. So teaching and advocating this imaging, evangelizing this to everyone in the company was a key community or communication strategy. And of course, I mean, I mentioned data privacy, external factors, the same goes for internal regulations and processes to help our colleagues to adapt for this very new environment. So when I mentioned before, the new way of thinking, the new way of dealing and managing data, this of course implies that we need new processes and regulations for our colleagues as well. In a nutshell, then this means that data governance provides a framework for managing our people, the processes and technology and culture around our data traffic. And that governance must come together in order to have this effective program providing at least a common denominator is especially critical for shared data sets, which we have across our different geographies managed, and shared applications on shared infrastructure and applications. And as then consumed by centralized processes, for example, master data, everything, and all the metrics and KPIs, which are also used for a central steering. It's a big change, right? And our ultimate goal is to have this non-invasive federated, automated and computational governance. And for that, we can't just talk about it. We actually have to go deep and use case by use case and QC by PUC and generate learnings and learnings with the different teams. And this would be a classical approach of identifying the target structure, the target status, match it with the current status, by identifying together with the business teams, with the different domains and have a risk assessment, for example, to increase transparency because a lot of teams, they might not even know what kind of situation they might be. And this is where this training and this piece of data literacy comes into place, where we go in and trade based on the findings, based on the most valuable use case. And based on that, help our teams to do this change, to increase their capability. I just told a little bit more, I wouldn't say hand-holding, but a lot of guidance. >> Can I kind of kind of chime in quickly and (mumbled) below me, I mean, there's a lot of governance piece, but I think that is important. And if you're talking about documentation, for example, yes, we can go from team to team and tell these people, hey, you have to document your data assets and data catalog, or you have to establish a data contract and so on and forth. But if we would like to build data products at scale, following actual governance, we need to think about automation, right? We need to think about a lot of things that we can learn from engineering before, and just starts as simple things. Like if we would like to build up trust in our data products, right? And actually want to apply the same rigor and the best practices that we know from engineering. There are things that we can do. And we should probably think about what we can copy. And one example might be so the level of service level agreements, so that level objectives. So the level of indicators, right, that represent on a, on an engineering level, right? Are we providing services? They're representing the promises we make to our customer and to our consumers. These are the internal objectives that help us to keep those promises. And actually these audits of, of how we are tracking ourselves, how we are doing. And this is just one example of where I think the federated governance, governance comes into play, right? In an ideal world, you should not just talk about data as a product, but also data product that's code. That'd be say, okay, as most, as much as possible, right? Give the engineers the tool that they are familiar with, and actually not ask the product managers, for example, to document the data assets in the data catalog, but make it part of the configuration has as, as a, as a CDCI continuous delivery pipeline, as we typically see in other engineering, tasks through it and services maybe say, okay, there is configuration, we can think about PII, we can think about data quality monitoring, we can think about the ingestion data catalog and so on and forth. But I think ideally in a data product goals become a sort of templates that can be deployed and are actually rejected or verified at build time before we actually make them and deploy them to production. >> Yeah so it's like DevOps for data product. So, so I'm envisioning almost a three-phase approach to governance. And you're kind of, it sounds like you're in the early phase of it, call it phase zero, where there's learning, there's literacy, there's training education, there's kind of self-governance. And then there's some kind of oversight, some, a lot of manual stuff going on, and then you, you're trying to process builders at this phase and then you codify it and then you can automate it. Is that fair? >> Yeah. I would rather think, think about automation as early as possible in a way, and yes, it needs to be separate rules, but then actually start actually use case by use case. Is there anything that small piece that we can already automate? If just possible roll that out at the next extended step-by-step. >> Is there a role though, that adjudicates that? Is there a central, you know, chief state officer who's responsible for making sure people are complying or is it, how do you handle it? >> I mean, from a, from a, from a platform perspective, yes. This applies in to, to implement certain pieces, that we are saying are important and actually would like to implement, however, that is actually working very closely with the governance department, So it's Clemence's piece to understand that defy the policies that needs to be implemented. >> So good. So Clemence essentially, it's, it's, it's your responsibility to make sure that the policy is being followed. And then as you were saying, Christoph, you want to compress the time to automation as fast as possible. Is that, is that-- >> Yeah, so it's a really, it's a, what needs to be really clear is that it's always a split effort, right? So you can't just do one or the other thing, but there is some that really goes hand in hand because for the right information, for the right engineering tooling, we need to have the transparency first. I mean, code needs to be coded. So we kind of need to operate on the same level with the right understanding. So there's actually two things that are important, which is one it's policies and guidelines, but not only that, because more importantly or equally important is to align with the end-user and tech teams and engineering and really bridge between business value business teams and the engineering teams. >> Got it. So just a couple more questions, because we got to wrap up, I want to talk a little bit about the business outcome. I know it's hard to quantify and I'll talk about that in a moment, but, but major learnings, we've got some of the challenges that, that you cited. I'll just put them up here. We don't have to go detailed into this, but I just wanted to share with some folks, but my question, I mean, this is the advice for your peers question. If you had to do it differently, if you had a do over or a Mulligan, as we like to say for you, golfers, what, what would you do differently? >> I mean, I, can we start with, from, from the transformational challenge that understanding that it's also high load of cultural exchange. I think this is, this is important that a particular communication strategy needs to be put into place and people really need to be supported, right? So it's not that we go in and say, well, we have to change into, towards data mash, but naturally it's the human nature, nature, nature, we are kind of resistant to change, right? And (mumbles) uncomfortable. So we need to take that away by training and by communicating. Chris, you might want to add something to that. >> Definitely. I think the point that I've also made before, right? We need to acknowledge that data mesh it's an architectural scale, right? If you're looking for something which is necessary by huge companies who are vulnerable, that are product at scale. I mean, Dave, you mentioned that right, there are a lot of advantages to have a centralized team, but at some point it may make sense to actually decentralize here. And at this point, right, if you think about data mesh, you have to recognize that you're not building something on a green field. And I think there's a big learning, which is also reflected on the slide is, don't underestimate your baggage. It's typically is you come to a point where the old model doesn't work anymore. And as had a fresh write, we lost the trust in our data. And actually we have seen certain risks of slowing down our innovation. So we triggered that, this was triggering the need to actually change something. So at this transition applies that you took, we have a lot of technical depth accumulated over years. And I think what we have learned is that potentially we have, de-centralized some assets too early. This is not actually taking into account the maturity of the team. We are actually investigating too. And now we'll be actually in the face of correcting pieces of that one, right? But I think if you, if you, if you start from scratch, you have to understand, okay, is all my teams actually ready for taking on this new, this new capability? And you have to make sure that this is decentralization. You build up these capabilities and the teams, and as Clemence has mentioned, right? Make sure that you take the, the people on your journey. I think these are the pieces that also here it comes with this knowledge gap, right? That we need to think about hiring literacy, the technical depth I just talked about. And I think the, the last piece that I would add now, which is not here on the slide deck is also from our perspective, we started on the analytical layer because it was kind of where things are exploding, right? This is the bit where people feel the pain. But I think a lot of the efforts that we have started to actually modernize the current stage and data products, towards data mesh, we've understood that it always comes down basically to a proper shape of our operational plan. And I think what needs to happen is I think we got through a lot of pains, but the learning here is this needs to really be an, a commitment from the company. It needs to have an end to end. >> I think that point, that last point you made is so critical because I, I, I hear a lot from the vendor community about how they're going to make analytics better. And that's not, that's not unimportant, but, but true data product thinking and decentralized data organizations really have to operationalize in order to scale it. So these decisions around data architecture and organization, they're fundamental and lasting, it's not necessarily about an individual project ROI. They're going to be projects, sub projects, you know, within this architecture. But the architectural decision itself is organizational it's cultural and, and what's the best approach to support your business at scale. It really speaks to, to, to what you are, who you are as a company, how you operate and getting that right, as we've seen in the success of data-driven companies is, yields tremendous results. So I'll, I'll, I'll ask each of you to give, give us your final thoughts and then we'll wrap. Maybe. >> Just can I quickly, maybe just jumping on this piece, what you have mentioned, right, the target architecture. If you talk about these pieces, right, people often have this picture of (mumbled). Okay. There are different kinds of stages. We have (incomprehensible speech), we have actually a gesture layer, we have a storage layer, transformation layer, presentation data, and then we are basically putting a lot of technology on top of that. That's kind of our target architecture. However, I think what we really need to make sure is that we have these different kinds of views, right? We need to understand what are actually the capabilities that we need to know, what new goals, how does it look and feel from the different kinds of personas and experience view. And then finally that should actually go to the, to the target architecture from a technical perspective. Maybe just to give an outlook what we are planning to do, how we want to move that forward. Yes. Actually based on our strategy in the, in the sense of we would like to increase the maturity as a whole across the entire company. And this is kind of a framework around the business strategy and it's breaking down into four pillars as well. People meaning the data culture, data literacy, data organizational structure and so on. If you're talking about governance, as Clemence had actually mentioned that right, compliance, governance, data management, and so on, you're talking about technology. And I think we could talk for hours for that one it's around data platform, data science platform. And then finally also about enablements through data. Meaning we need to understand data quality, data accessibility and applied science and data monetization. >> Great. Thank you, Christoph. Clemence why don't you bring us home. Give us your final thoughts. >> Okay. I can just agree with Christoph that important is to understand what kind of maturity people have, but I understand we're at the maturity level, where a company, where people, our organization is, and really understand what does kind of, it's just kind of a change applies to that, those four pillars, for example, what needs to be tackled first. And this is not very clear from the very first beginning (mumbles). It's kind of like green field, you come up with must wins to come up with things that you really want to do out of theory and out of different white papers. Only if you really start conducting the first initiatives, you do understand that you are going to have to put those thoughts together. And where do I miss out on one of those four different pillars, people process technology and governance, but, and then that can often the integration like doing step by step, small steps, by small steps, not pulling the ocean where you're capable, really to identify the gaps and see where either you can fill the gaps or where you have to increase maturity first and train people or increase your tech stack. >> You know, HelloFresh is an excellent example of a company that is innovating. It was not born in Silicon Valley, which I love. It's a global company. And, and I got to ask you guys, it seems like it's just an amazing place to work. Are you guys hiring? >> Yes, definitely. We do. As, as mentioned right as well as one of these aspects distributing and actually hiring as an entire company, specifically for data. I think there are a lot of open roles, so yes, please visit or our page from data engineering, data, product management, and Clemence has a lot of roles that you can speak to about. But yes. >> Guys, thanks so much for sharing with theCUBE audience, you're, you're pioneers, and we look forward to collaborations in the future to track progress, and really want to thank you for your time. >> Thank you very much. >> Thank you very much Dave. >> And thank you for watching theCUBE's startup showcase made possible by AWS. This is Dave Volante. We'll see you next time. (cheerful music)

>> Hello. My name is Ron Kafka, and I'm the senior director for Partner Scale Initiatives for HBE Ezmeral. Thanks for joining us today at Analytics Unleashed. By now, you've heard a lot about the Ezmeral portfolio and how it can help you accomplish objectives around big data analytics and containerization. I want to shift gears a bit and then discuss our Ezmeral Technology Partner Program. I've got two great guest speakers here with me today. And together, We're going to discuss how jointly we are solving data analytic challenges for our customers. Before I introduce them, I want to take a minute to talk to provide a little bit more insight into our ecosystem program. We've created a program with a realization based on customer feedback that even the most mature organizations are struggling with their data-driven transformation efforts. It turns out this is largely due to the pace of innovation with application vendors or ICS supporting data science and advanced analytic workloads. Their advancements are simply outpacing organization's ability to move workloads into production rapidly. Bottom line, organizations want a unified experience across environments where their entire application portfolio in essence provide a comprehensive application stack and not piece parts. So, let's talk about how our ecosystem program helps solve for this. For starters, we were leveraging HPEs long track record of forging technology partnerships and it created a best in class ISB partner program specific for the Ezmeral portfolio. We were doing this by developing an open concept marketplace where customers and partners can explore, learn, engage and collaborate with our strategic technology partners. This enables our customers to adopt, deploy validated applications from industry leading software vendors on HPE Ezmeral with a high degree of confidence. Also, it provides a very deep bench of leading ISVs for other groups inside of HPE to leverage for their solutioning efforts. Speaking of industry leading ISV, it's about time and introduce you to two of those industry leaders right now. Let me welcome Daniel Hladky from Dataiku, and Omri Geller from Run:AI. So I'd like to introduce Daniel Hladky. Daniel is with Dataiku. He's a great partner for HPE. Daniel, welcome. >> Thank you for having me here. >> That's great. Hey, would you mind just talking a bit about how your partnership journey has been with HPE? >> Yes, pleasure. So the journey started about five years ago and in 2018 we signed a worldwide reseller agreement with HPE. And in 2020, we actually started to work jointly on the integration between the Dataiku Data Science Studio called DSS and integrated that with the Ezmeral Container platform, and was a great success. And it was on behalf of some clear customer projects. >> It's been a long partnership journey with you for sure with HPE. And we welcome your partnership extremely well. Just a brief question about the Container Platform and really what that's meant for Dataiku. >> Yes, Ron. Thanks. So, basically I'd like the quote here Florian Douetteau, which is the CEO of Dataiku, who said that the combination of Dataiku with the HPE Ezmeral Container Platform will help the customers to successfully scale and put machine learning projects into production. And this basically is going to deliver real impact for their business. So, the combination of the two of us is a great success. >> That's great. Can you talk about what Dataiku is doing and how HPE Ezmeral Container Platform fits in a solution offering a bit more? >> Great. So basically Dataiku DSS is our product which is a end to end data science platform, and basically brings value to the project of customers on their past enterprise AI. In simple ways, we can say it could be as simple as building data pipelines, but it could be also very complex by having machine and deep learning models at scale. So the fast track to value is by having collaboration, orchestration online technologies and the models in production. So, all of that is part of the Data Science Studio and Ezmeral fits perfectly into the part where we design and then basically put at scale those project and put it into product. >> That's perfect. Can you be a bit more specific about how you see HPE and Dataiku really tightening up a customer outcome and value proposition? >> Yes. So what we see is also the challenge of the market that probably about 80% of the use cases really never make it to production. And this is of course a big challenge and we need to change that. And I think the combination of the two of us is actually addressing exactly this need. What we can say is part of the MLOps approach, Dataiku and the Ezmeral Container Platform will provide a frictionless approach, which means without scripting and coding, customers can put all those projects into the productive environment and don't have to worry any more and be more business oriented. >> That's great. So you mentioned you're seeing customers be a lot more mature with their AI workloads and deployment. What do you suggest for the other customers out there that are just starting this journey or just thinking about how to get started? >> Yeah. That's a very good question, Ron. So what we see there is actually the challenge that people need to go on a pass of maturity. And this starts with a simple data pipelines, et cetera, and then basically move up the ladder and basically build large complex project. And here I see a very interesting offer coming now from HPE which is called D3S, which is the data science startup pack. That's something I discussed together with HPE back in early 2020. And basically, it solves the three stages, which is explore, experiment and evolve and builds quickly MVPs for the customers. By doing so, basically you addressed business objectives, lay out in the proper architecture and also setting up the proper organization around it. So, this is a great combination by HPE and Dataiku through the D3S. >> And it's a perfect example of what I mentioned earlier about leveraging the ecosystem program that we built to do deeper solutioning efforts inside of HPE in this case with our AI business unit. So, congratulations on that and thanks for joining us today. I'm going to shift gears. I'm going to bring in Omri Geller from Run:AI. Omri, welcome. It's great to have you. You guys are killing it out there in the market today. And I just thought we could spend a few minutes talking about what is so unique and differentiated from your offerings. >> Thank you, Ron. It's a pleasure to be here. Run:AI creates a virtualization and orchestration layer for AI infrastructure. We help organizations to gain visibility and control over their GPO resources and help them deliver AI solutions to market faster. And we do that by managing granular scheduling, prioritization, allocation of compute power, together with the HPE Ezmeral Container Platform. >> That's great. And your partnership with HPE is a bit newer than Daniel's, right? Maybe about the last year or so we've been working together a lot more closely. Can you just talk about the HPE partnership, what it's meant for you and how do you see it impacting your business? >> Sure. First of all, Run:AI is excited to partner with HPE Ezmeral Container Platform and help customers manage appeals for their AI workloads. We chose HPE since HPE has years of experience partnering with AI use cases and outcomes with vendors who have strong footprint in this markets. HPE works with many partners that are complimentary for our use case such as Nvidia, and HPE Container Platform together with Run:AI and Nvidia deliver a world class solutions for AI accelerated workloads. And as you can understand, for AI speed is critical. Companies want to gather important AI initiatives into production as soon as they can. And the HPE Ezmeral Container Platform, running IGP orchestration solution enables that by enabling dynamic provisioning of GPU so that resources can be easily shared, efficiently orchestrated and optimal used. >> That's great. And you talked a lot about the efficiency of the solution. What about from a customer perspective? What is the real benefit that our customers are going to be able to gain from an HPE and Run:AI offering? >> So first, it is important to understand how data scientists and AI researchers actually build solution. They do it by running experiments. And if a data scientist is able to run more experiments per given time, they will get to the solution faster. With HPE Ezmeral Container Platform, Run:AI and users such as data scientists can actually do that and seamlessly and efficiently consume large amounts of GPU resources, run more experiments or given time and therefore accelerate their research. Together, we actually saw a customer that is running almost 7,000 jobs in parallel over GPUs with efficient utilization of those GPUs. And by running more experiments, those customers can be much more effective and efficient when it comes to bringing solutions to market >> Couldn't agree more. And I think we're starting to see a lot of joint success together as we go out and talk to the story. Hey, I want to thank you both one last time for being here with me today. It was very enlightening for our team to have you as part of the program. And I'm excited to extend this customer value proposition out to the rest of our communities. With that, I'd like to close today's session. I appreciate everyone's time. And keep an eye out on our ISP marketplace for Ezmeral We're continuing to expand and add new capabilities and new partners to our marketplace. We're excited to do a lot of great things and help you guys all be successful. Thanks for joining. >> Thank you, Ron. >> What a great panel discussion. And these partners they really do have a good understanding of the possibilities, working on the platform, and I hope and expect we'll see this ecosystem continue to grow. That concludes the main program, which means you can now pick one of three live demos to attend and chat live with experts. Now those three include day in the life of IT Admin, day in the life of a data scientist, and even a day in the life of the HPE Ezmeral Data Fabric, where you can see the many ways the data fabric is used in your life today. Wish you could attend all three, no worries. The recordings will be available on demand for you and your teams. Moreover, the show doesn't stop here, HPE has a growing and thriving tech community, you should check it out. It's really a solid starting point for learning more, talking to smart people about great ideas and seeing how Ezmeral can be part of your own data journey. Again, thanks very much to all of you for joining, until next time, keep unleashing the power of your data.

>> Hello. My name is Ron Kafka, and I'm the senior director for Partner Scale Initiatives for HBE Ezmeral. Thanks for joining us today at Analytics Unleashed. By now, you've heard a lot about the Ezmeral portfolio and how it can help you accomplish objectives around big data analytics and containerization. I want to shift gears a bit and then discuss our Ezmeral Technology Partner Program. I've got two great guest speakers here with me today. And together, We're going to discuss how jointly we are solving data analytic challenges for our customers. Before I introduce them, I want to take a minute to talk to provide a little bit more insight into our ecosystem program. We've created a program with a realization based on customer feedback that even the most mature organizations are struggling with their data-driven transformation efforts. It turns out this is largely due to the pace of innovation with application vendors or ICS supporting data science and advanced analytic workloads. Their advancements are simply outpacing organization's ability to move workloads into production rapidly. Bottom line, organizations want a unified experience across environments where their entire application portfolio in essence provide a comprehensive application stack and not piece parts. So, let's talk about how our ecosystem program helps solve for this. For starters, we were leveraging HPEs long track record of forging technology partnerships and it created a best in class ISB partner program specific for the Ezmeral portfolio. We were doing this by developing an open concept marketplace where customers and partners can explore, learn, engage and collaborate with our strategic technology partners. This enables our customers to adopt, deploy validated applications from industry leading software vendors on HPE Ezmeral with a high degree of confidence. Also, it provides a very deep bench of leading ISVs for other groups inside of HPE to leverage for their solutioning efforts. Speaking of industry leading ISV, it's about time and introduce you to two of those industry leaders right now. Let me welcome Daniel Hladky from Dataiku, and Omri Geller from Run:AI. So I'd like to introduce Daniel Hladky. Daniel is with Dataiku. He's a great partner for HPE. Daniel, welcome. >> Thank you for having me here. >> That's great. Hey, would you mind just talking a bit about how your partnership journey has been with HPE? >> Yes, pleasure. So the journey started about five years ago and in 2018 we signed a worldwide reseller agreement with HPE. And in 2020, we actually started to work jointly on the integration between the Dataiku Data Science Studio called DSS and integrated that with the Ezmeral Container platform, and was a great success. And it was on behalf of some clear customer projects. >> It's been a long partnership journey with you for sure with HPE. And we welcome your partnership extremely well. Just a brief question about the Container Platform and really what that's meant for Dataiku. >> Yes, Ron. Thanks. So, basically I like the quote here Florian Douetteau, which is the CEO of Dataiku, who said that the combination of Dataiku with the HPE Ezmeral Container Platform will help the customers to successfully scale and put machine learning projects into production. And this basically is going to deliver real impact for their business. So, the combination of the two of us is a great success. >> That's great. Can you talk about what Dataiku is doing and how HPE Ezmeral Container Platform fits in a solution offering a bit more? >> Great. So basically Dataiku DSS is our product which is a end to end data science platform, and basically brings value to the project of customers on their past enterprise AI. In simple ways, we can say it could be as simple as building data pipelines, but it could be also very complex by having machine and deep learning models at scale. So the fast track to value is by having collaboration, orchestration online technologies and the models in production. So, all of that is part of the Data Science Studio and Ezmeral fits perfectly into the part where we design and then basically put at scale those project and put it into product. >> That's perfect. Can you be a bit more specific about how you see HPE and Dataiku really tightening up a customer outcome and value proposition? >> Yes. So what we see is also the challenge of the market that probably about 80% of the use cases really never make it to production. And this is of course a big challenge and we need to change that. And I think the combination of the two of us is actually addressing exactly this need. What we can say is part of the MLOps approach, Dataiku and the Ezmeral Container Platform will provide a frictionless approach, which means without scripting and coding, customers can put all those projects into the productive environment and don't have to worry any more and be more business oriented. >> That's great. So you mentioned you're seeing customers be a lot more mature with their AI workloads and deployment. What do you suggest for the other customers out there that are just starting this journey or just thinking about how to get started? >> Yeah. That's a very good question, Ron. So what we see there is actually the challenge that people need to go on a pass of maturity. And this starts with a simple data pipelines, et cetera, and then basically move up the ladder and basically build large complex project. And here I see a very interesting offer coming now from HPE which is called D3S, which is the data science startup pack. That's something I discussed together with HPE back in early 2020. And basically, it solves the three stages, which is explore, experiment and evolve and builds quickly MVPs for the customers. By doing so, basically you addressed business objectives, lay out in the proper architecture and also setting up the proper organization around it. So, this is a great combination by HPE and Dataiku through the D3S. >> And it's a perfect example of what I mentioned earlier about leveraging the ecosystem program that we built to do deeper solutioning efforts inside of HPE in this case with our AI business unit. So, congratulations on that and thanks for joining us today. I'm going to shift gears. I'm going to bring in Omri Geller from Run:AI. Omri, welcome. It's great to have you. You guys are killing it out there in the market today. And I just thought we could spend a few minutes talking about what is so unique and differentiated from your offerings. >> Thank you, Ron. It's a pleasure to be here. Run:AI creates a virtualization and orchestration layer for AI infrastructure. We help organizations to gain visibility and control over their GPO resources and help them deliver AI solutions to market faster. And we do that by managing granular scheduling, prioritization, allocation of compute power, together with the HPE Ezmeral Container Platform. >> That's great. And your partnership with HPE is a bit newer than Daniel's, right? Maybe about the last year or so we've been working together a lot more closely. Can you just talk about the HPE partnership, what it's meant for you and how do you see it impacting your business? >> Sure. First of all, Run:AI is excited to partner with HPE Ezmeral Container Platform and help customers manage appeals for their AI workloads. We chose HPE since HPE has years of experience partnering with AI use cases and outcomes with vendors who have strong footprint in this markets. HPE works with many partners that are complimentary for our use case such as Nvidia, and HPE Ezmeral Container Platform together with Run:AI and Nvidia deliver a word about solution for AI accelerated workloads. And as you can understand, for AI speed is critical. Companies want to gather important AI initiatives into production as soon as they can. And the HPE Ezmeral Container Platform, running IGP orchestration solution enables that by enabling dynamic provisioning of GPU so that resources can be easily shared, efficiently orchestrated and optimal used. >> That's great. And you talked a lot about the efficiency of the solution. What about from a customer perspective? What is the real benefit that our customers are going to be able to gain from an HPE and Run:AI offering? >> So first, it is important to understand how data scientists and AI researchers actually build solution. They do it by running experiments. And if a data scientist is able to run more experiments per given time, they will get to the solution faster. With HPE Ezmeral Container Platform, Run:AI and users such as data scientists can actually do that and seamlessly and efficiently consume large amounts of GPU resources, run more experiments or given time and therefore accelerate their research. Together, we actually saw a customer that is running almost 7,000 jobs in parallel over GPUs with efficient utilization of those GPUs. And by running more experiments, those customers can be much more effective and efficient when it comes to bringing solutions to market >> Couldn't agree more. And I think we're starting to see a lot of joint success together as we go out and talk to the story. Hey, I want to thank you both one last time for being here with me today. It was very enlightening for our team to have you as part of the program. And I'm excited to extend this customer value proposition out to the rest of our communities. With that, I'd like to close today's session. I appreciate everyone's time. And keep an eye out on our ISP marketplace for Ezmeral We're continuing to expand and add new capabilities and new partners to our marketplace. We're excited to do a lot of great things and help you guys all be successful. Thanks for joining. >> Thank you, Ron. (bright upbeat music)

from around the globe it's thecube covering space and cyber security symposium 2020 hosted by cal poly hello and welcome to thecube's coverage we're here hosting with cal poly an amazing event space in the intersection of cyber security this session is defending satellite and space infrastructure from cyber threats got two great guests we've got major general john shaw combined four space component commander u.s space command and vandenberg air force base in california and roland cuello who's the ceo of maverick space systems gentlemen thank you for spending the time to come on to this session for the cal poly space and cyber security symposium appreciate it absolutely um guys defending satellites and space infrastructure is the new domain obviously it's a war warfighting domain it's also the future of the world and this is an important topic because we rely on space now for our everyday life and it's becoming more and more critical everyone knows how their phones work and gps just small examples of all the impacts i'd like to discuss with this hour this topic with you guys so if we can have you guys do an opening statement general if you can start with your opening statement we'll take it from there thanks john and greetings from vandenberg air force base we are just down the road from cal poly here on the central coast of california and uh very proud to be part of this uh effort and part of the partnership that we have with with cal poly on a number of fronts um i should uh so in in my job here i actually uh have two hats that i wear and it's i think worth talking briefly about those to set the context for our discussion you know we had two major organizational events within our department of defense with regard to space last year in 2019 and probably the one that made the most headlines was the stand-up of the united states space force that happened uh december 20th last year and again momentous the first new branch in our military since 1947 uh and uh it is a it's just over nine months old now as we're making this recording uh and already we're seeing a lot of change uh with regard to how we're approaching uh organizing training and equipping on a service side or space capabilities and so i uh in that with regard to the space force the hat i wear there is commander of space operations command that was what was once 14th air force when we were still part of the air force here at vandenberg and in that role i'm responsible for the operational capabilities that we bring to the joint warfighter and to the world from a space perspective didn't make quite as many headlines but another major change that happened last year was the uh the reincarnation i guess i would say of united states space command and that is a combatant command it's how our department of defense organizes to actually conduct warfighting operations um most people are more familiar perhaps with uh central command centcom or northern command northcom or even strategic command stratcom well now we have a space com we actually had one from 1985 until 2002 and then stood it down in the wake of the 9 11 attacks and a reorganization of homeland security but we've now stood up a separate command again operationally to conduct joint space operations and in that organization i wear a hat as a component commander and that's the combined force-based component command uh working with other all the additional capabilities that other services bring as well as our allies that combined in that title means that uh i under certain circumstances i would lead an allied effort uh in space operations and so it's actually a terrific job to have here on the central coast of california uh both working the uh how we bring space capabilities to the fight on the space force side and then how we actually operate those capabilities it's a point of joint in support of joint warfighters around the world um and and national security interests so that's the context now what el i i also should mention you kind of alluded to john you're beginning that we're kind of in a change situation than we were a number of years ago and that space we now see space as a warfighting domain for most of my career going back a little ways most of my my focus in my jobs was making sure i could bring space capabilities to those that needed them bringing gps to that special operations uh soldier on the ground somewhere in the world bringing satellite communications for our nuclear command and control bringing those capabilities for other uses but i didn't have to worry in most of my career about actually defending those space capabilities themselves well now we do we've actually gone to a point where we're are being threatened in space we now are treating it more like any other domain normalizing in that regard as a warfighting domain and so we're going through some relatively emergent efforts to protect and defend our capabilities in space to to design our capabilities to be defended and perhaps most of all to train our people for this new mission set so it's a very exciting time and i know we'll get into it but you can't get very far into talking about all these space capabilities and how we want to protect and defend them and how we're going to continue their ability to deliver to warfighters around the globe without talking about cyber because they fit together very closely so anyway thanks for the chance to be here today and i look forward to the discussion general thank you so much for that opening statement and i would just say that not only is it historic with the space force it's super exciting because it opens up so much more challenges and opportunities for to do more and to do things differently so i appreciate that statement roland your opening statement your your job is to put stuff in space faster cheaper smaller better your opening statement please um yes um thank you john um and yes you know to um general shaw's point you know with with the space domain and the need to protect it now um is incredibly important and i hope that we are more of a help um than a thorn in your side um in terms of you know building satellites smaller faster cheaper um you know and um definitely looking forward to this discussion and you know figuring out ways where um the entire space domain can work together you know from industry to to us government even to the academic environment as well so first would like to say and preface this by saying i am not a cyber security expert um we you know we build satellites um and uh we launch them into orbit um but we are by no means you know cyber security experts and that's why um you know we like to partner with organizations like the california cyber security institute because they help us you know navigate these requirements um so um so i'm the ceo of um of maverick space systems we are a small aerospace business in san luis obispo california and we provide small satellite hardware and service solutions to a wide range of customers all the way from the academic environment to the us government and everything in between we support customers through an entire you know program life cycle from mission architecture and formulation all the way to getting these customer satellites in orbit and so what we try to do is um provide hardware and services that basically make it easier for customers to get their satellites into orbit and to operate so whether it be reducing mass or volume um creating greater launch opportunities or providing um the infrastructure and the technology um to help those innovations you know mature in orbit you know that's you know that's what we do our team has experienced over the last 20 years working with small satellites and definitely fortunate to be part of the team that invented the cubesat standard by cal poly and stanford uh back in 2000 and so you know we are in you know vandenberg's backyard um we came from cal poly san luis obispo um and you know our um our hearts are fond you know of this area and working with the local community um a lot of that success um that we have had is directly attributable um to the experiences that we learned as students um working on satellite programs from our professors and mentors um you know that's you know all you know thanks to cal poly so just wanted to tell a quick story so you know back in 2000 just imagine a small group of undergraduate students you know myself included with the daunting task of launching multiple satellites from five different countries on a russian launch vehicle um you know many of us were only 18 or 19 not even at the legal age to drink yet um but as you know essentially teenagers we're managing million dollar budgets um and we're coordinating groups um from around the world um and we knew that we knew what we needed to accomplish um yet we didn't really know um what we were doing when we first started um the university was extremely supportive um and you know that's the cal poly learn by doing philosophy um i remember you know the first time we had a meeting with our university chief legal counsel and we were discussing the need to to register with the state department for itar nobody really knew what itar was back then um and you know discussing this with the chief legal counsel um you know she was asking what is itar um and we essentially had to explain you know this is um launching satellites as part of the um the u.s munitions list and essentially we have a similar situation you know exporting munitions um you know we are in similar categories um you know as you know as weapons um and so you know after that initial shock um everybody jumped in you know both feet forward um the university um you know our head legal counsel professors mentors and the students um you know knew we needed to tackle this problem um because you know the the need was there um to launch these small satellites and um you know the the reason you know this is important to capture the entire spectrum of users of the community um is that the technology and the you know innovation of the small satellite industry occurs at all levels you know so we have academia commercial national governments we even have high schools and middle schools getting involved and you know building satellite hardware um and the thing is you know the the importance of cyber security is incredibly important because it touches all of these programs and it touches you know people um at a very young age um and so you know we hope to have a conversation today um to figure out you know how do we um create an environment where we allow these programs to thrive but we also you know protect and you know keep their data safe as well thank you very much roland appreciate that uh story too as well thanks for your opening statement gentlemen i mean i love this topic because defending the assets in space is is as obvious um you look at it but there's a bigger picture going on in our world right now and generally you kind of pointed out the historic nature of space force and how it's changing already operationally training skills tools all that stuff is revolving you know in the tech world that i live in you know change the world is a topic they use that's thrown around a lot you can change the world a lot of young people we have just other panels on this where we're talking about how to motivate young people changing the world is what it's all about with technology for the better evolution is just an extension of another domain in this case space is just an extension of other domains similar things are happening but it's different there's a huge opportunity to change the world so it's faster there's an expanded commercial landscape out there certainly government space systems are moving and changing how do we address the importance of cyber security in space general we'll start with you because this is real it's exciting if you're a young person there's touch points of things to jump into tech building hardware to changing laws and and everything in between is an opportunity and it's exciting and it's truly a chance to change the world how does the commercial government space systems teams address the importance of cyber security so john i think it starts with with the realization that as i like to say that cyber and space are bffs uh there's nothing that we do on the cutting edge of space that isn't heavy reliant heavily reliant on the cutting edge of cyber and frankly there's probably nothing on the cutting edge of cyber that doesn't have a space application and when you realize that you see how how closely those are intertwined as we need to move forward at at speed it becomes fundamental to to the to answering your question let me give a couple examples we one of the biggest challenges i have on a daily basis is understanding what's going on in the space domain those on the on the on the surface of the planet talk about tyranny of distance across the oceans across large land masses and i talk about the tyranny of volume and you know right now we're looking out as far as the lunar sphere there's activity that's extending out to the out there we expect nasa to be conducting uh perhaps uh human operations in the lunar environment in the next few years so it extends out that far when you do the math that's a huge volume how do you do that how do you understand what's happening in real time in within that volume it is a big data problem by the very definition of that that kind of effort to that kind of challenge and to do it successfully in the years ahead it's going to require many many sensors and the fusion of data of all kinds to present a picture and then analytics and predictive analytics that are going to deliver an idea of what's going on in the space arena and that's just if people are not up to mischief once you have threats introduced into that environment it is even more challenging so i'd say it's a big data problem that we'll be enjoying uh tackling in the years ahead a second example is you know we if i if i had to if we had to take a vote of what were the most uh amazing robots that have ever been designed by humans i think that spacecraft would have to be up there on the list whether it's the nasa spacecraft that explore other planets or the ones that we or gps satellites that that amazingly uh provide a wonderful service to the entire globe uh and beyond they are amazing technological machines that's not going to stop i mean all the work that roland talked about at the at the even even that we're doing it at the kind of the microsoft level is is putting cutting-edge technology into smaller packages you can to get some sort of capability out of that as we expand our activities further and further into space for national security purposes or for exploration or commercial or civil the the cutting edge technologies of uh artificial intelligence uh and machine to machine engagements and machine learning are going to be part of that design work moving forward um and then there's the threat piece as we try to as we operate these these capabilities how these constellations grow that's going to be done via networks and as i've already pointed out space is a warfighting domain that means those networks will come under attack we expect that they will and that may happen early on in a conflict it may happen during peace time in the same way that we see cyber attacks all the time everywhere in many sectors of of activity and so by painting that picture you kind of get you we start to see how it's intertwined at the very very base most basic level the cutting edge of cyber and cutting edge of space with that then comes the need to any cutting edge cyber security capability that we have is naturally going to be needed as we develop space capabilities and we're going to have to bake that in from the very beginning we haven't done that in the past as well as we should but moving forward from this point on it will be an essential ingredient that we work into all of our new capability roland we're talking about now critical infrastructure we're talking about new capabilities being addressed really fast so it's kind of chaotic now there's threats so it's not as easy as just having capabilities because you've got to deal with the threats the general just pointed out but now you've got critical infrastructure which then will enable other things down down the line how do you protect it how do we address this how do you see this being addressed from a security standpoint because you know malware these techniques can be mapped in as extended into into space and takeovers wartime peacetime these things are all going to be under threat that's pretty well understood i think people kind of get that how do we address it what's your what's your take yeah you know absolutely and you know i couldn't agree more with general shaw you know with cyber security and space being so intertwined um and you know i think with fast and rapid innovation um comes you know the opportunity for threats especially um if you have bad actors um that you know want to cause harm and so you know as a technology innovator and you're pushing the bounds um you kind of have a common goal of um you know doing the best you can um and you know pushing the technology balance making it smaller faster cheaper um but a lot of times what entrepreneurs and you know small businesses and supply chains um are doing and don't realize it is a lot of these components are dual use right i mean you could have a very benign commercial application but then a small you know modification to it and turn it into a military application and if you do have these bad actors they can exploit that and so you know i think the the big thing is um creating a organization that is you know non-biased that just wants to kind of level the playing field for everybody to create a set standard for cyber security in space i think you know one group that would be perfect for that you know is um cci um you know they understand both the cybersecurity side of things and they also have you know at cal poly um you know the the small satellite group um and you know just having kind of a a clearinghouse or um an agency where um can provide information that is free um you know you don't need a membership for and to be able to kind of collect that but also you know reach out to the entire value chain you know for a mission and um making them aware um of you know what potential capabilities are and then how it might um be you know potentially used as a weapon um and you know keeping them informed because i think you know the the vast majority of people in the space industry just want to do the right thing and so how do we get that information free flowing to you know to the us government so that they can take that information create assessments and be able to not necessarily um stop threats from occurring presently but identify them long before that they would ever even happen um yeah that's you know general i want to i want to follow up on that real quick before we go to the next talk track critical infrastructure um you mentioned you know across the oceans long distance volume you know when you look at the physical world you know you had you know power grids here united states you had geography you had perimeters uh the notion of a perimeter and the moat this is and then you had digital comes in then you have we saw software open up and essentially take down this idea of a perimeter and from a defense standpoint and that everything changed and we had to fortify those critical assets uh in the u.s space increases the same problem statement significantly because it's you can't just have a perimeter you can't have a moat it's open it's everywhere like what digital's done and that's why we've seen a slurge of cyber in the past two decades attacks with software so this isn't going to go away you need the critical infrastructure you're putting it up there you're formulating it and you've got to protect it how do you view that because it's going to be an ongoing problem statement what's the current thinking yeah i i think my sense is a mindset that you can build a a firewall or a defense or some other uh system that isn't dynamic in his own right is probably not heading in the right direction i think cyber security in the future whether it's for our space systems or for other critical infrastructure is going to be a dynamic fight that happens at a machine-to-machine um a speed and dynamic um i don't think it's too far off where we will have uh machines writing their own code in real time to fight off attacks that are coming at them and by the way the offense will probably be doing the same kind of thing and so i i guess i would not want to think that the answer is something that you just build it and you leave it alone and it's good enough it's probably going to be a constantly evolving capability constantly reacting to new threats and staying ahead of those threats that's the kind of use case just to kind of you know as you were kind of anecdotal example is the exciting new software opportunities for computer science majors i mean i tell my young kids and everyone man it's more exciting now i wish i was 18 again it's so so exciting with ai bro i want to get your thoughts we were joking on another panel with the dod around space and the importance of it obviously and we're going to have that here and then we had a joke it's like oh software's defined everything it says software's everything ai and and i said well here in the united states companies had data centers and they went to the cloud and they said you can't do break fix it's hard to do break fix in space you can't just send a tech up i get that today but soon maybe robotics the general mentions robotics technologies and referencing some of the accomplishments fixing things is almost impossible in space but maybe form factors might get better certainly software will play a role what's your thoughts on that that landscape yeah absolutely you know for for software in orbit um you know there's there's a push for you know software-defined radios um to basically go from hardware to software um and you know that's that that's a critical link um if you can infiltrate that and a small satellite has propulsion on board you could you know take control of that satellite and cause a lot of havoc and so you know creating standards and you know that kind of um initial threshold of security um you know for let's say you know these radios you know communications and making that um available um to the entire supply chain to the satellite builders um and operators you know is incredibly key and you know that's again one of the initiatives that um that cci is um is tackling right now as well general i want to get your thoughts on best practices around cyber security um state of the art today uh and then some guiding principles and kind of how the if you shoot the trajectory forward what what might happen uh around um supply chain there's been many stories where oh we outsourced the chips and there's a little chip sitting in a thing and it's built by someone else in china and the software is written from someone in europe and the united states assembles it it gets shipped and it's it's corrupt and it has some cyber crime making i'm oversimplifying the the statement but this is what when you have space systems that involve intellectual property uh from multiple partners whether it's from software to creation and then deployment you get supply chain tiers what are some of the best practices that you see involving that don't stunt the innovation but continues to innovate but people can operate safely what's your thoughts yeah so on supply chain i think i think the symposium here is going to get to hear from lieutenant general jt thompson uh from space missile system center down in los angeles and and uh he's a he's just down the road from us there uh on the coast um and his team is is the one that we look to really focus on as he acquires and develop again bake in cyber security from the beginning and knowing where the components are coming from and and properly assessing those as you as you put together your space systems is a key uh piece of what his team is focused on so i expect we'll hear him talk about that when it talks to i think she asked the question a little more deeply about how do the best practices in terms of how we now develop moving forward well another way that we don't do it right is if we take a long time to build something and then you know general general jt thompson's folks take a while to build something and then they hand it over to to to me and my team to operate and then they go hands-free and and then and then that's you know that's what i have for for years to operate until the next thing comes along that's a little old school what we're going to have to do moving forward with our space capabilities and with the cyber piece baked in is continually developing new capability sets as we go we actually have partnership between general thompson's team and mine here at vandenberg on our ops floor or our combined space operations center that are actually working in real time together better tools that we can use to understand what's going on the space environment to better command and control our capabilities anywhere from military satellite communications to space domain awareness sensors and such and so and we're developing those capabilities in real time it's a dev and and with the security pieces so devsecops is we're practicing that in in real time i think that is probably the standard today that we're trying to live up to as we continue to evolve but it has to be done again in close partnership all the time it's not a sequential industrial age process while i'm on the subject of partnerships so general thompson's and team and mine have good partnerships it's part partnerships across the board are going to be another way that we are successful and that uh it means with with academia in some of the relationships that we have here with cal poly it's with the commercial sector in ways that we haven't done before the old style business was to work with just a few large um companies that had a lot of space experience well we need we need a lot of kinds of different experience and technologies now in order to really field good space capabilities and i expect we'll see more and more non-traditional companies being part of and and organizations being part of that partnership that will work going forward i mentioned at the beginning that um uh allies are important to us so everything that uh that role and i've been talking about i think you have to extrapolate out to allied partnerships right it doesn't help me uh as a combined force component commander which is again one of my jobs it doesn't help me if the united states capabilities are cyber secure but i'm trying to integrate them with capabilities from an ally that are not cyber secure so that partnership has to be dynamic and continually evolving together so again close partnering continually developing together from the acquisition to the operational sectors with as many um different sectors of our economy uh as possible are the ingredients to success general i'd love to just follow up real quick i was having just a quick reminder for a conversation i had with last year with general keith alexander who was does a lot of cyber security work and he was talking about the need to share faster and the new school is you got to share faster and to get the data you mentioned observability earlier you need to see what everything's out there he's a real passionate person around getting the data getting it fast and having trusted partners so that's not it's kind of evolving as i mean sharing is a well-known practice but with cyber it's sensitive data potentially so there's a trust relationship there's now a new ecosystem that's new for uh government how do you view all that and your thoughts on that trend of the sharing piece of it on cyber so it's i don't know if it's necessarily new but it's at a scale that we've never seen before and by the way it's vastly more complicated and complex when you overlay from a national security perspective classification of data and information at various levels and then that is again complicated by the fact you have different sharing relationships with different actors whether it's commercial academic or allies so it gets very very uh a complex web very quickly um so that's part of the challenge we're working through how can we how can we effectively share information at multiple classification levels with multiple partners in an optimal fashion it is certainly not optimal today it's it's very difficult even with maybe one industry partner for me to be able to talk about data at an unclassified level and then various other levels of classification to have the traditional networks in place to do that i could see a solution in the future where our cyber security is good enough that maybe i only really need one network and the information that is allowed to flow to the players within the right security environment um to uh to make that all happen as quickly as possible so you've actually uh john you've hit on yet another big challenge that we have is um is evolving our networks to properly share with the right people at the right uh clearance levels as at speed of war which is what we're going to need yeah and i wanted to call that out because this is an opportunity again this discussion here at cal poly and around the world is for new capabilities and new people to solve the problems and um it's again it's super exciting if you you know you're geeking out on this it's if you have a tech degree or you're interested in changing the world there's so many new things that could be applied right now roland will get your thoughts on this because one of the things in the tech trends we're seeing this is a massive shift all the theaters of the tech industry are are changing rapidly at the same time okay and it affects policy law but also deep tech the startup communities are super important in all this too we can't forget them obviously the big trusted players that are partnering certainly on these initiatives but your story about being in the dorm room now you got the boardroom and now you got everything in between you have startups out there that want to and can contribute and you know what's an itar i mean i got all these acronym certifications is there a community motion to bring startups in in a safe way but also give them a ability to contribute because you look at open source that proved everyone wrong on software that's happening now with this now open network concept the general is kind of alluding to which is it's a changing landscape your thoughts i know you're passionate about this yeah absolutely you know and i think um you know as general shaw mentioned you know we need to get information out there faster more timely and to the right people um and involving not only just stakeholders in the us but um internationally as well you know and as entrepreneurs um you know we have this very lofty vision or goal uh to change the world and um oftentimes um you know entrepreneurs including myself you know we put our heads down and we just run as fast as we can and we don't necessarily always kind of take a breath and take a step back and kind of look at what we're doing and how it's touching um you know other folks and in terms of a community i don't know of any formal community out there it's mostly ad hoc and you know these ad hoc communities are folks who let's say have you know was was a student working on a satellite um you know in college and they love that entrepreneurial spirit and so they said well i'm gonna start my own company and so you know a lot of the these ad hoc networks are just from relationships um that are that have been built over the last two decades um you know from from colleagues that you know at the university um i do think formalizing this and creating um kind of a you know clearinghouse to to handle all of this is incredibly important yeah um yeah there's gonna be a lot of entrepreneurial activity no doubt i mean just i mean there's too many things to work on and not enough time so i mean this brings up the question though while we're on this topic um you got the remote work with covid everyone's working remotely we're doing this remote um interview rather than being on stage works changing how people work and engage certainly physical will come back but if you looked at historically the space industry and the talent you know they're all clustered around the bases and there's always been these areas where you're you're a space person you're kind of working there and there's jobs there and if you were cyber you were 10 in other areas over the past decade there's been a cross-pollination of talent and location as you see the intersection of space general start with you you know first of all central coast is a great place to live i know that's where you guys live but you can start to bring together these two cultures sometimes they're you know not the same maybe they're getting better we know they're being integrated so general can you just share your thoughts because this is uh one of those topics that everyone's talking about but no one's actually kind of addressed directly um yeah john i i think so i think i want to answer this by talking about where i think the space force is going because i think if there was ever an opportunity or inflection point in our department of defense to sort of change culture and and try to bring in non-traditional kinds of thinking and and really kind of change uh maybe uh some of the ways that the department of defense has does things that are probably archaic space force is an inflection point for that uh general raymond our our chief of space operations has said publicly for a while now he wants the us space force to be the first truly digital service and uh you know what we what we mean by that is you know we want the folks that are in the space force to be the ones that are the first adopters or the early adopters of of technology um to be the ones most fluent in the cutting edge technological developments on space and cyber and and other um other sectors of the of of the of the economy that are technologically focused uh and i think there's some can that can generate some excitement i think and it means that we probably end up recruiting people into the space force that are not from the traditional recruiting areas that the rest of the department of defense looks to and i think it allows us to bring in a diversity of thought and diversity of perspective and a new kind of motivation um into the service that i think is frankly is is really exciting so if you put together everything i mentioned about how space and cyber are going to be best friends forever and i think there's always been an excitement in them you know from the very beginning in the american psyche about space you start to put all these ingredients together and i think you see where i'm going with this that really changed that cultural uh mindset that you were describing it's an exciting time for sure and again changing the world and this is what you're seeing today people do want to change world they want a modern world that's changing roy look at your thoughts on this i was having an interview a few years back with a tech entrepreneur um techie and we were joking we were just kind of riffing and we and i said everything that's on star trek will be invented and we're almost there actually if you think about it except for the transporter room you got video you got communicators so you know not to bring in the star trek reference with space force this is digital and you start thinking about some of the important trends it's going to be up and down the stack from hardware to software to user experience everything your thoughts and reaction yeah abs absolutely and so you know what we're seeing is um timeline timelines shrinking dramatically um because of the barrier to entry for you know um new entrants and you know even your existing aerospace companies is incredibly low right so if you take um previously where you had a technology on the ground and you wanted it in orbit it would take years because you would test it on the ground you would verify that it can operate in space in a space environment and then you would go ahead and launch it and you know we're talking tens if not hundreds of millions of dollars to do that now um we've cut that down from years to months when you have a prototype on the ground and you want to get it launched you don't necessarily care if it fails on orbit the first time because you're getting valuable data back and so you know we're seeing technology being developed you know for the first time on the ground and in orbit in a matter of a few months um and the whole kind of process um you know that that we're doing as a small business is you know trying to enable that and so allowing these entrepreneurs and small small companies to to get their technology in orbit at a price that is sometimes even cheaper than you know testing on the ground you know this is a great point i think this is really an important point to call out because we mentioned partnerships earlier the economics and the business model of space is doable i mean you do a mission study you get paid for that you have technology you can get stuff up up quickly and there's a cost structure there and again the alternative was waterfall planning years and millions now the form factors are different now again there may be different payloads involved but you can standardize payloads you got robotic arms all this is all available this brings up the congestion problem this is going to be on the top of mind the generals of course but you got the proliferation okay of these constellation systems you have more and more tech vectors i mean essentially that's malware i mean that's a probe you throw something up in space that could cause some interference maybe a takeover general this is the this is the real elephant in the room the threat matrix from new stuff and new configurations so general how does the proliferation of constellation systems change the threat matrix so i i think the uh you know i guess i'm gonna i'm gonna be a little more optimistic john than i think you pitched that i'm actually excited about these uh new mega constellations in leo um i'm excited about the the growing number of actors that are that are going into space for various reasons and why is that it's because we're starting to realize a new economic engine uh for the nation and for human society so the question is so so i think we want that to happen right when uh um when uh we could go to almost any any other domain in history and and and you know there when when air traffic air air travel started to become much much more commonplace with many kinds of uh actors from from private pilots flying their small planes all the way up to large airliners uh you know there there was a problem with congestion there was a problem about um challenges about uh behavior and are we gonna be able to manage this and yes we did and it was for the great benefit of society i could probably look to the maritime domain for similar kinds of things and so this is actually exciting about space we are just going to have to find the ways as a society and it's not just the department of defense it's going to be civil it's going to be international find the mechanisms to encourage this continued investment in the space domain i do think the space force uh will play a role in in providing security in the space environment as we venture further out as as economic opportunities emerge uh wherever they are um in the in the lunar earth lunar system or even within the solar system space force is going to play a role in that but i'm actually really excited about the those possibilities hey by the way i got to say you made me think of this when you talked about star trek and and and space force and our technologies i remember when i was younger watching the the next generation series i thought one of the coolest things because being a musician in my in my spare time i thought one of the coolest things was when um commander riker would walk into his quarters and and say computer play soft jazz and there would just be the computer would just play music you know and this was an age when you know we had we had hard uh um uh media right like how will that that is awesome man i can't wait for the 23rd century when i can do that and where we are today is is so incredible on those lines the things that i can ask alexa or siri to play um well that's the thing everything that's on star trek think about it almost invented i mean you got the computers you got the only thing really is the holograms are starting to come in you got now the transporter room now that's physics we'll work on that right right so there's a there is this uh a balance between physics and imagination but uh we have not exhausted either well um personally everyone that knows me knows i'm a huge star trek fan all the series of course i'm an original purist but at that level but this is about economic incentive as well roland i want to get your thoughts because you know the gloom and doom you got to think about the the bad stuff to make it good if i if i put my glass half full on the table there's economic incentives just like the example of the plane and the air traffic there's there's actors that are more actors that are incented to have a secure system what's your thoughts to general's comments around the optimism and and the potential threat matrix that needs to be managed absolutely so and you know one of the things that we've seen over the years um as you know we build these small satellites is a lot of the technology you know that the general is talking about um you know voice recognition miniaturized chips and sensors um started on the ground and i mean you know you have you know your iphone um that about 15 years ago before the first iphone came out um you know we were building small satellites in the lab and we were looking at cutting-edge state-of-the-art magnetometers and sensors um that we were putting in our satellites back then we didn't know if they were going to work and then um a few years later as these students graduate they go off and they go out to under you know other industries and so um some of the technology that was first kind of put in these cubesats in the early 2000s you know kind of ended up in the first generation iphone smartphones um and so being able to take that technology rapidly you know incorporate that into space and vice versa gives you an incredible economic advantage because um not only are your costs going down um because you know you're mass producing you know these types of terrestrial technologies um but then you can also um you know increase you know revenue and profit um you know by by having you know smaller and cheaper systems general let's talk about that for real quickly it's a good point i want to just shift it into the playbook i mean everyone talks about playbooks for management for tech for startups for success i mean one of the playbooks that's clear from in history is investment in r d around military and or innovation that has a long view spurs innovation commercially i mean just there's a huge many decades of history that shows that hey we got to start thinking about these these challenges and you know next you know it's in an iphone this is history this is not like a one-off and now with space force you get you're driving you're driving the main engine of innovation to be all digital you know we we riff about star trek which is fun but the reality is you're going to be on the front lines of some really new cool mind-blowing things could you share your thoughts on how you sell that people who write the checks or recruit more talent well so i first i totally agree with your thesis that the that you know national security well could probably go back an awful long way hundreds to thousands of years that security matters tend to drive an awful lot of innovation and creativity because um you know i think the the probably the two things that drive drive people the most are probably an opportunity to make money uh but only by beating that out are trying to stay alive um and uh and so i don't think that's going to go away and i do think that space force can play a role um as it pursues uh security uh structures you know within the space domain to further encourage economic investment and to protect our space capabilities for national security purposes are going to be at the cutting edge this isn't the first time um i think we can point back to the origins of the internet really started in the department of defense and with a partnership i should add with academia that's how the internet got started that was the creativity in order to to meet some needs there cryptography has its roots in security but we use it uh in in national security but now we use it in for economic reasons and meant and a host of other kinds of reasons and then space itself right i mean we still look back to uh apollo era as an inspiration for so many things that inspired people to to either begin careers in in technical areas or in space and and so on so i think i think in that same spirit you're absolutely right i guess i'm totally agreeing with your thesis the space force uh will be and a uh will have a positive inspirational influence in that way and we need to to realize that so when we are asking for when we're looking for how we need to meet capability needs we need to spread that net very far look for the most creative solutions and partner early and often with those that that can that can work on those when you're on the new frontier you've got to have a team sport it's a team effort you mentioned the internet just anecdotally i'm old enough to remember this because i remember the days that was going on and said the government if the policy decisions that the u.s made at that time was to let it go a little bit invisible hand they didn't try to commercialize it too fast and but there was some policy work that was done that had a direct effect to the innovation versus take it over and next you know it's out of control so i think you know i think this this just a cross-disciplinary skill set becomes a big thing where you need to have more people involved and that's one of the big themes of this symposium so it's a great point thank you for sharing that roland your thoughts on this because you know you got policy decisions we all want to run faster we want to be more innovative but you got to have some ops view now mostly ops people want things very tight very buttoned up secure the innovators want to go faster it's the yin and yang that's that's the world we live in how's it all balanced in your mind yeah um you know one of the things um that may not be apparently obvious is that you know the us government and department of um of defense is one of the biggest investors in technology in the aerospace sector um you know they're not the traditional venture capitalists but they're the ones that are driving technology innovation because there's funding um you know and when companies see that the us governments is interested in something businesses will will re-vector um you know to provide that capability and in the i would say the more recent years we've had a huge influx of private equity venture capital um coming into the markets to kind of help augment um you know the government investment and i think having a good partnership and a relationship with these private equity venture capitalists and the us government is incredibly important because the two sides you know can can help collaborate and kind of see a common goal but then also too on um you know the other side is you know there's that human element um and as general shaw was saying it's like not you know not only do companies you know obviously want to thrive and do really well some companies just want to stay alive um to see their technology kind of you know grow into what they've always dreamed of and you know oftentimes entrepreneurs um are put in a very difficult position because they have to make payroll they have to you know keep the lights on and so sometimes they'll take investment um from places where they may normally would not have you know from potentially foreign investment that could potentially you know cause issues with you know the you know the us supply chain well my final question is the best i wanted to say for last because i love the idea of human space flight i'd love to be on mars i'm not sure i'll be able to make it someday but how do you guys see the possible impacts of cyber security on expanding human space flight operations i mean general this is your wheelhouse this is urine command putting humans in space and certainly robots will be there because they're easy to go because they're not human but humans in space i mean you're starting to see the momentum the discussion uh people are are scratching that itch what's your take on that how do we see making this more possible well i i think we will see we will see uh commercial space tourism uh in the future i'm not sure how wide and large a scale it will become but we'll we will see that and um part of uh i think the mission of the space force is going to be probably to again do what we're doing today is have really good awareness of what's going on the domain to uh to to to ensure that that is done safely and i think a lot of what we do today will end up in civil organizations to do space traffic management and safety uh in in that uh arena um and uh um it is only a matter of time uh before we see um humans going even beyond the you know nasa has their plan the the artemis program to get back to the moon and the gateway initiative to establish a a space station there and that's going to be an exploration initiative but it is only a matter of time before we have um private citizens or private corporations putting people in space and not only for tourism but for economic activity and so it'll be really exciting to watch it would be really exciting and space force will be a part of it general roland i want to thank you for your valuable time to come on this symposium i really appreciate it final uh comment i'd love to you to spend a minute to share your personal thoughts on the importance of cyber security to space and we'll close it out we'll start with you roland yeah so i think that the biggest thing um i would like to try to get out of this you know from my own personal perspective is um creating that environment that allows um you know the the aerospace supply chain small businesses you know like ourselves be able to meet all the requirements um to protect um and safeguard our data but also um create a way that you know we can still thrive and it won't stifle innovation um you know i'm looking forward um to comments and questions um you know from the audience um to really kind of help um you know you know basically drive to that next step general final thoughts the importance of cyber security to space i'll just i'll go back to how i started i think john and say that space and cyber are forever intertwined they're bffs and whoever has my job 50 years from now or 100 years from now i predict they're going to be saying the exact same thing cyber and space are are intertwined for good we will always need the cutting edge cyber security capabilities that we develop as a nation or as a as a society to protect our space capabilities and our cyber capabilities are going to need space capabilities in the future as well general john shaw thank you very much roland cleo thank you very much for your great insight thank you to cal poly for putting this together i want to shout out to the team over there we couldn't be in person but we're doing a virtual remote event i'm john furrier with thecube and siliconangle here in silicon valley thanks for watching

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this a CUBE conversation. >> Hello, and welcome to this CUBE conversation, I'm John Furrier, host of theCUBE here, in our Palo Alto, California studios, here with our quarantine crew. We're getting all the remote interviews during this time of COVID-19. We've got two great remote guests here, Rachini Moosavi who's the Executive Director of Analytical Services and Data Governance at UNC Healthcare, and Sonya Jordan, Enterprise Analytics Manager of Data Governance at UNC Health. Welcome to theCUBE, thanks for coming on. >> Thank you. >> Thanks for having us. >> So, I'm super excited. University of North Carolina, my daughter will be a freshman this year, and she is coming, so hopefully she won't have to visit UNC Health, but looking forward to having more visits down there, it's a great place. So, thanks for coming on, really appreciate it. Okay, so the conversation today is going to be about how data and how analytics are helping solve problems, and ultimately, in your case, serve the community, and this is a super important conversation. So, before we get started, talk about UNC Health, what's going on there, how you guys organize, how big is it, what are some of the challenges that you have? >> SO UNC Health is comprised of about 12 different entities within our hospital system. We have physician groups as well as hospitals, and we serve, we're spread throughout all of North Carolina, and so we serve the patients of North Carolina, and that is our primary focus and responsibility for our mission. As part of the offices Sonya and I are in, we are in the Enterprise Analytics and Data Sciences Office that serves all of those entities and so we are centrally located in the triangle area of North Carolina, which is pretty central to the state, and we serve all of our entities equally from our Analytics and Data Governance needs. >> John: You guys got a different customer base, obviously you've got the clinical support, and you got the business applications, you got to be agile, that's what it's all about today, you don't need to rely on IT support. How do you guys do that? What's the framework? How do you guys tackle that problem of being agile, having the data be available, and you got two different customers, you got all the compliance issues with clinical, I can only imagine all the regulations involved, and you've got the business applications. How do you handle those? >> Yeah, so for us in the roles that we are in, we are fully responsible for more of the data and analytics needs of the organization, and so we provide services that truly are balanced across our clinician group, so we have physicians, and nurses, and all of the other ancillary clinical staff that we support, as well as the operational needs as well, so revenue cycle, finance, pharmacy, any of those groups that are required in order to run a healthcare system. So, we balance our time amongst all of those and for the work that we take on and how we continuously support them is really based on governance at the end of the day. How we make decisions around what the priorities are and what needs to happen next, and requires the best insights, is really how we focus on what work we do next. As for the applications that we build, in our office, we truly only build analytical applications or products like visualizations within Tableau as well as we support data governance platforms and services and so we provide some of the tools that enable our end users to be able to interact with the information that we're providing around analytics and insights, at the end of the day. >> Sonya, what's your job? Your title is Analytics Manager of Data Governance, obviously that sounds broad but governance is obviously required in all things. What is your job, what is your day-to-day roles like? What's your focus? >> Well, my day-to-day operations is first around building a data governance program. I try to work with identifying customers who we can start partnering with so that we can start getting documentation and utilizing a lot of the programs that we currently have, such as certification, so when we talk about initiatives, this is one of the initiatives that we use to partner with our stakeholders in order to start bringing visibilities to the various assets, such as metrics, or universes that we want to certify, or dashboards, algorithm, just various lists of different types of assets that we certify that we like to partner with the customers in order for them to start documenting within the tools, so that we can bring visibility to what's available, really focusing on data literacy, helping people to understand what assets are available, not only what assets are available, but who owns them, and who own the asset, and what can they do with it, making sure that we have great documentation in order to be able to leverage literacy as well. >> So, I can only imagine with how much volume you guys are dealing from a data standpoint, and the diversity, that the data warehouse must be massive, or it must be architected in a way that it can be agile because the needs, of the diverse needs. Can you guys share your thoughts on how you guys look on the data warehouse challenge and opportunity, and what you guys are currently doing? >> Well, so- >> Yeah you go ahead, Rachini. >> Go ahead, Sonya. >> Well, last year we implemented a tool, an enterprise warehouse, basically behind a tool that we implemented, and that was an opportunity for Data Governance to really lay some foundation and really bring visibility to the work that we could provide for the enterprise. We were able to embed into probably about six or seven of the 13 initiatives, I was actually within that project, and with that we were able to develop our stewardship committee, our data governance council, and because Rachini managed Data Solutions, our data solution manager was able to really help with the architect and integration of the tools. >> Rachini, your thoughts on running the data warehouse, because you've got to have flexibility for new types of data sources. How do you look at that? >> So, as Sonya just mentioned, we upgraded our data warehouse platform just recently because of these evolving needs, and like a lot of healthcare providers out there, a lot of them are either one or the other EMRs that are top in the market. With our EMR, they provide their own data warehouse, so you have to factor almost the impact of what they bring to the table in with an addition to all of those other sources of data that you're trying to co-mingle and bring together into the same data warehouse, and so for us, it was time for us to evolve our data warehouse. We ended up deciding on trying to create a virtual data warehouse, and in doing so, with virtualization, we had to upgrade our platform, which is what created that opportunity that Sonya was mentioning. And by moving to this new platform we are now able to bring all of that into one space and it's enabled us to think about how does the community of analysts interact with the data? How do we make that available to them in a secure way? In a way that they can take advantage of reusable master data files that could be our source of truth within our data warehouse, while also being able to have the flexibility to build what they need in their own functional spaces so that they can get the wealth of information that they need out of the same source and it's available to everyone. >> Okay, so I got to ask the question, and I was trying to get the good stuff out first, but let's get at the reality of COVID-19. You got pre-COVID-19 pandemic, we're kind of in the middle of it, and people are looking at strategies to come out of it, obviously the world will be changed, higher with a lot of virtualization, virtual meetings, and virtual workforce, but the data still needs to be, the business still needs to run, but data will be changing different sources, how are you guys responding to that crisis because you're going to be leaned on heavily for more and more support? >> Yeah it's been non-stop since March (laughs). So, I'm going to tell you about the reporting aspects of it, and then I'd love to turn it over to Sonya to tell you about some of the great things that we've actually been able to do to it and enhance our data governance program by not wasting this terrible event and this opportunity that's come up. So, with COVID, when it kicked off back in March, we actually formed a war room to address the needs around reporting analytics and just insights that our executives needed, and so in doing so, we created within the first week, our first weekend actually, our first dashboard, and within the next two weeks we had about eight or nine other dashboards that were available. And we continuously add to that. Information is so critical to our executives, to our clinicians, to be able to know how to address the evolving needs of COVID-19 and how we need to respond. We literally, and I'm not even exaggerating, at this very moment we have probably, let's see, I think it's seven different forecasts that we're trying to build all at the same time to try and help us prepare for this new recovery, this sort of ramp up efforts, so to your point, it started off as we're shutting down so that we can flatten the curve, but now as we try to also reopen at the same time while we're still meeting the needs of our COVID patients, there's this balancing act that we're trying to keep up with and so analytics is playing a critical factor in doing that. >> Sonya, your thoughts. First of all, congratulations, and action is what defines the players from the pretenders in my mind, you're seeing that play out, so congratulations for taking great action, I know you're working hard. Sonya, your thoughts, COVID, it's putting a lot of pressure? It highlights the weaknesses and strengths of what's kind of out there, what's your thoughts? >> Well, it just requires a great deal of collaboration and making sure that you're documenting metrics in a way where you're factoring true definition because at the end of the day, this information can go into a dashboard that's going to be visualized across the organization, I think what COVID has done was really enhanced the need and the understanding of why data governance is important and also it has allowed us to create a lot of standardization, where we we're standardizing a lot of processes that we currently had in correct place but just enhancing them. >> You know, not to go on a tangent, but I will, it's funny how the reality has kind of pulled back, exposed a lot of things, whether it's the remote work situation, people are VPNing, not under provision with the IT side. On the data side, everyone now understands the quality of the data. I mean, I got my kids talking progression analysis, "Oh, the curves are all wrong," I mean people are now seeing the science behind the data and they're looking at graphs all the time, you guys are in the visualization piece, this really highlights the need of data as a story, because there's an impact, and two, quality data. And if you don't have the data, the story isn't being told and then misinformation comes out of it, and this is actually playing out in real time, so it's not like it's just a use case for the most analytics but this again highlights the value of proposition of what you guys do. What's your personal thoughts on all this because this really is playing out globally. >> Yeah, it's been amazing how much information is out there. So, we have been extremely blessed at times but also burdened at times by that amount of information. So, there's the data that's going through our healthcare system that we're trying to manage and wrangle and do that data storytelling so that people can drive those insights to very effective decisions. But there's also all of this external data that we're trying to be able to leverage as well. And this is where the whole sharing of information can sometimes become really hard to try and get ahead of, we leverage the Johns Hopkins data for some time, but even that, too, can have some hiccups in terms of what's available. We try to use our State Department of Health and Human Services data and they just about updated their website and how information was being shared every other week and it was making it impossible for us to ingest that into our dashboards that we were providing, and so there's really great opportunities but also risks in some of the information that we're pulling. >> Sonya, what's your thoughts? I was just having a conversation this morning with the Chief of Analytics and Insight from NOA which is the National Oceanic Administration, about weather data and forecasting weather, and they've got this community model where they're trying to get the edges to kind of come in, this teases out a template. You guys have multiple locations. As you get more democratized in the connection points, whether it's third-party data, having a system managing that is hard, and again, this is a new trend that's emerging, this community connection points, where I think you guys might also might be a template, and your multiple locations, what's your general thoughts on that because the data's coming in, it's now connected in, whether it's first-party to the healthcare system or third-party. >> Yeah, well we have been leveraging our data governance tool to try to get that centralized location, making sure that we obtain the documentations. Due to COVID, everything is moving very fast, so it requires us to really sit down and capture the information and when you don't have enough resources in order to do that, it's easy to miss some very important information, so really trying to encourage people to understand the reason why we have data governance tools in order for them to leverage, in order to capture the documentation in a way that it can tell the story about the data, but most of all, to be able to capture it in a way so that if that person happened to leave the organization, we're not spending a lot of time trying to figure out how was this information created, how was this dashboard designed, where are the requirements, where are the specifications, where are the key elements, where does that information live, and making sure we capture that up front. >> So, guys, you guys are using Informatica, how are they helping you? Obviously, they have a system they're getting some great feedback on, how are you using Informatica, how is it going, and how has that enabled you guys to be successful? >> Yeah, so we decided on Informatica after doing a really thorough vetting of all of the other vendors in the industry that could provide us these services. We've really loved the capabilities that we've been able to provide to our customers at this point. It's evolving, I think, for us, the ability to partner with a group like Prominence, to be able to really leverage the capabilities of Informatica and then be really super, super hyper focused on providing data literacy back to our end users and making that the full intent of what we're doing within data governance has really enabled us to take the tools and make it something that's specific to UNC Health and the needs that our end users are verbalizing and provide that to them in a very positive way. >> Sonya, they talk about this master catalog, and I've talked to the CEO of Informatica and all their leaders, governance is a big part of it, and I've always said, I've always kind of had a hard time, I'm an entrepreneur, I like to innovate, move fast, break things, which is kind of not the way you work in the data world, you don't want to be breaking anything, so how do you balance governance and compliance with innovation? This has been a key topic and I know that you guys are using their enterprise data catolog. Is that helping? How does that fit in, is that part of it? >> Well, yeah, so during our COVID initiatives and building these telos dashboards, these visualizations and forecast models for executive leaders, we were able to document and EMPower you, which we rebranded Axon to EMPower, we were able to document a lot of our dashboards, which is a data set, and pretty much document attributes and show lineage from EMPower to EDC, so that users would know exactly when they start looking at the visualization not only what does this information mean, but they're also able to see what other sources that that information impacts as well as the data lineage, where did the information come from in EDC. >> So I got to ask the question to kind of wrap things up, has Informatica helped you guys out now that you're in this crisis? Obviously you've implemented before, now that you're in the middle of it, have you seen any things that jumped out at you that's been helpful, and are there areas that need to be worked on so that you guys continue to fight the good fight, come out of this thing stronger than before you came in? >> Yeah, there is a lot of new information, what we consider as "aha" moments that we've been learning about, and how EMPower, yes there's definitely a learning curve because we implemented EDC and EMPower last year doing our warehouse implementation, and so there's a lot of work that still needs to be done, but based on where we were the first of the year, I can say we have evolved tremendously due to a lot of the pandemic issues that arised, and we're looking to really evolve even greater, and pilot across the entire organization so that they can start leveraging these tools for their needs. >> Rachini you got any thoughts on your end on what's worked, what you see improvements coming, anything to share? >> Yeah, so we're excited about some of the new capabilities like the marketplace for example that's available in Axon, we're looking forward to being able to take advantage of some of these great new aspects of the tool so that we can really focus more on providing those insights back to our end users. I think for us, during COVID, it's really been about how do we take advantage of the immediate needs that are surfacing. How do we build all of these dashboards in record-breaking time but also make sure that folks understand exactly what's being represented within those dashboards, and so being able to provide that through our Informatica tools and service it back to our end users, almost in a seamless way like it's built into our dashboards, has been a really critical factor for us, and feeling like we can provide that level of transparency, and so I think that's where as we evolve that we would look for more opportunities, too. How do we make it simple for people to get that immediate answers to their questions, of what does the information need without it feeling like they're going elsewhere for the information. >> Rachini, thank you so much for your insight, Sonya as well, thanks for the insight, and stay safe. Sonya, behind you, I was pointing out, that's your artwork, you painted that picture. >> Yes. >> Looks beautiful. >> Yes, I did. >> You got two jobs, you're an artist, and you're doing data governance. >> Yes, I am, and I enjoy painting, that's how I relax (laughs). >> Looks great, get that on the market soon, get that on the marketplace, let's get that going. Appreciate the time, thank you so much for the insights, and stay safe and again, congratulations on the hard work you're doing, I know there's still a lot more to do, thanks for your time, appreciate it. >> Thank you. >> Thank you. >> It's theCUBE conversation, I'm John Furrier at the Palo Alto studios, for the remote interviews with Informatica, I'm John Furrier, thanks for watching. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto and Boston. It's theCUBE. Covering IBM Think. Brought to you by IBM. >> Hi everybody, we're back this is Dave Vellante from theCUBE and you're watching our wall-to-wall coverage of IBM's Digital Think 2020 event and we are really pleased to have Guillermo Miranda here. He's the Vice President of Corporate and Social Responsibility. Guillermo thanks for coming on theCUBE. >> Absolutely, good afternoon to you. Good evening, wherever you are. >> So, you know this notion of corporate responsibility, it really has gained steam lately and of course with COVID-19, companies like IBM really have to take the lead on this. The tech industry actually has been one of those industries that has been less hard hit and IBM as a leader along with some other companies are really being looked at to step up. So talk a little bit about social responsibility in the context of the current COVID climate. >> Absolutely. Now thank you for the question. Look, first our responsibility is with the safety of our employees and the continuity of business for our clients. In this frame what we have done is see what is the most adequate areas to respond to the emergency of the pandemic and using what we know in terms of expertise and the talent that we have is why we decided to work first with high performance computing. IBM design and produce the fastest computers in the world. So Summit and a consortium of providers of high performance computing is helping on the discovery of vaccinations and drugs for the pandemic. The second thing that we are doing is related with data and insights. We own The Weather Company which is at 80 million people connected to check the weather every morning, every afternoon. So through The Weather Company, we are providing insights and data about county level information on COVID-19. Another thing that we are doing is we are offering some of our products for free. Watson, it is a chatbot to inform about what is adequate, what is needed in the middle of a pandemic if you are a consumer. We are also helping with our volunteers. IBM volunteers are helping teachers and school districts to rapidly flip into remote learning and get used to the tools of working on a remote environment. And finally we have a micro volunteering opportunity for anybody that has a computer or an android phone. So with the world community grid, you can help with the discovery also of drugs and vaccinations for COVID-19. >> Wow that's great, those are four awesome initiatives. They can't get the vaccine fast enough. Getting good quality information in the hands of people in this era of fake news also very very important. Students missing out on some of the key parts of their learning so remote learning is key. I love this idea of kind of micro crowd sourcing solutions. Really kind of opening that up and hopefully we'll have some big wins there Guillermo. Thank you for that. I want to ask you people talk about blue collar jobs, they talk about white collar jobs, you guys talk about new collar jobs. You and others. What are new collar jobs and why are they important? >> Look, in this data, digital, artificial intelligence driven economy, it's important not to have a digital divide between the haves and the have nots on the foundational skills to be operational in a digital economy. So new collar jobs are precisely the intersection of the skills that you need to operate in this digital driven economy with the basic knowledge to be a user of technology. So think about a cyber security analyst. You don't need a masters degree in industrial engineering to be a cyber security analyst. You just need the basic things about operating an environment on a security control center for instance. Or talk about blockchain or talk about software engineering, full stack developer. There are many roles that you can do in this economy where you don't need to have a full four-year degree in a university to have a decent paying job for the digital economy. These are the new collar jobs and what we are attempting to do with the new collar job definition is to get rid of the paradigm that the university degree is the only passport to a successful career in the marketplace. You can start in different, having the opportunity to have a job in a high tech area. Not necessarily with a PhD in engineering as I said, it's something important for us, for our clients and for the community. >> Yeah, so that's a very interesting concept that a lot of us can relate to. To go back to our university days, many of the courses that we took, we shook our heads and said, "okay, why do I have to take this?" Okay, I get it, well rounded liberal arts experience, that's all good but it's almost like you're implying that the notion of specialization that we've known for years like for instance, in vocations, auto-mechanic, woodworking, etc. Planning that have really critical aspect of the economy. Applying that to the technology business. It's genius and very simple. >> Absolutely. Look, this is the reinvention of vocational education for the 21st century where you continue to need the plumber, you continue to need the hairdresser but also you need people that operate the digital platforms and are comfortable with this environment and they don't need to pass at the beginning through full university. And it's also the concept that we have divided the secondary education, high school from college, university etc., like a Chinese wall. Here is high school, here is college. No! There can be a clear integration because you can start to get ready without finishing high school yet. So there are several paradigms that we have evolved in the previous century that now we need to change and be ready for this 21st century digital driven economy. >> Yeah, very refreshing. Really about time that this thinking came into practice. Talk about P-Tech. How does P-Tech fit into acquiring these skills? And maybe you could give us a sense as to the sort of profile of the folks and there backgrounds and give us a sense as to and add some color to how that's all working. >> Absolutely, so look, the P-Tech model started 10 years ago in a high school in New York City, in Brooklyn. And the whole idea is to go to an under-served area and create a ramp onto success that will help you to first finish high school. Finishing high school is very important and has a lot connotations for your future. And then at the same time, they start getting an associate degree in an area of high growth. The third component is the industry partner. An industry partner that works with the school district and the community college in order to bring the knowledge of what is needed in that community in order to create real job opportunities and we will send you the people and then you will use it. No! We need to work together in order to train the talent for the future. And you just go to the middle age and the guilds were the ones that were preparing the workers. So the industry was preparing the workforce. Why in the 20th century we renounced to that? Having real, relevant skills starting in high school, helping the kids to graduate with a dual diploma. High school, college and practice in real life what it is to be in a workplace environment. So we have more than 220 schools. In this school year, we have more than 150,000 kids in 24 countries already working through the P-Tech model. >> Love it and really scaling that up. So let's say I'm an individual. I'm a young person, I'm from a diverse background, maybe my parents came to this country and I'm a first generation American. Of course, it's not just the United States, it's global but let's say I'm from a background that's less advantaged, how do I take advantage? How hard is it for me to tap in to something like P-Tech and get these skills? >> Well, first one of the characteristics of the model is this is free admission. So there is not a barrier fence. If your school district offers P-Tech, you can apply to P-Tech and get into the P-Tech model education without any barrier without any account. And the second thing that you need to have is curiosity. Because it's not going to be the typical high school where you have math, science, gym, whatever. This is more of an integration of how the look of a career will be in the future and how you have to start understanding that there are drivers into the economy that are fast tracks into well paid jobs. So curiosity on top of being ready to join a P-Tech school in the school district where you live in. >> That's great Guillermo, thank you for sharing that. Now of course corporate responsibility, that's a wide net. This is one of your passions. I'll give you the last word to kind of, where do you see this whole corporate responsibility movement going generally and specifically within IBM? >> I think that this whole pandemic will just accelerate some of the clear trends in the marketplace. Corporate responsibility cannot be an afterthought as before in the '80s or '90s. I will put a foundation. I have a little of profits that are left and then I will distribute grants and that's my whole corporate responsibility approach. Corporate responsibility needs to be within the fabric of how do you do business. It has to be embedded into the values of your company and your value proposition and you have to serve those projects with the same kind of skills and technology, in the case of IBM, that you do for your commercial engagements. And this is what we do in IBM. We help IBMers to be helpful to their communities with the same kind of quality and platforms that we offer to our clients. And we help to solve one of the most complicated problems in society through technology, innovation, time. >> Love it. Guillermo thanks so much, you're doing great work. Really appreciate you coming on theCUBE and sharing with our audience. Congratulations. >> Absolutely. Thank you for very much for having me. >> You're very welcome and thank you for watching everybody. This is Dave Vellante from theCUBE. You're watching our continuous coverage of IBM Think 2020, the digital version. Keep it right there, we'll be right back after this short break. (bright music)

>> Sue: Hello everybody. Thank you for joining us today for the Virtual Vertica BDC 2020. Today's breakout session in entitled Extending Vertica with the Latest Vertica Ecosystem and Open Source Initiatives. My name is Sue LeClaire, Director of Marketing at Vertica and I'll be your host for this webinar. Joining me is Tom Wall, a member of the Vertica engineering team. But before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait. Just type your question or comment in the question box below the slides and click submit. There will be a Q and A session at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions that we don't get to, we'll do our best to answer them offline. Alternatively, you can visit the Vertica forums to post you questions after the session. Our engineering team is planning to join the forums to keep the conversation going. Also a reminder that you can maximize your screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available to view on demand later this week. We'll send you a notification as soon as it's ready. So let's get started. Tom, over to you. >> Tom: Hello everyone and thanks for joining us today for this talk. My name is Tom Wall and I am the leader of Vertica's ecosystem engineering team. We are the team that focuses on building out all the developer tools, third party integrations that enables the SoftMaker system that surrounds Vertica to thrive. So today, we'll be talking about some of our new open source initatives and how those can be really effective for you and make things easier for you to build and integrate Vertica with the rest of your technology stack. We've got several new libraries, integration projects and examples, all open source, to share, all being built out in the open on our GitHub page. Whether you use these open source projects or not, this is a very exciting new effort that will really help to grow the developer community and enable lots of exciting new use cases. So, every developer out there has probably had to deal with the problem like this. You have some business requirements, to maybe build some new Vertica-powered application. Maybe you have to build some new system to visualize some data that's that's managed by Vertica. The various circumstances, lots of choices will might be made for you that constrain your approach to solving a particular problem. These requirements can come from all different places. Maybe your solution has to work with a specific visualization tool, or web framework, because the business has already invested in the licensing and the tooling to use it. Maybe it has to be implemented in a specific programming language, since that's what all the developers on the team know how to write code with. While Vertica has many different integrations with lots of different programming language and systems, there's a lot of them out there, and we don't have integrations for all of them. So how do you make ends meet when you don't have all the tools you need? All you have to get creative, using tools like PyODBC, for example, to bridge between programming languages and frameworks to solve the problems you need to solve. Most languages do have an ODBC-based database interface. ODBC is our C-Library and most programming languages know how to call C code, somehow. So that's doable, but it often requires lots of configuration and troubleshooting to make all those moving parts work well together. So that's enough to get the job done but native integrations are usually a lot smoother and easier. So rather than, for example, in Python trying to fight with PyODBC, to configure things and get Unicode working, and to compile all the different pieces, the right way is to make it all work smoothly. It would be much better if you could just PIP install library and get to work. And with Vertica-Python, a new Python client library, you can actually do that. So that story, I assume, probably sounds pretty familiar to you. Sounds probably familiar to a lot of the audience here because we're all using Vertica. And our challenge, as Big Data practitioners is to make sense of all this stuff, despite those technical and non-technical hurdles. Vertica powers lots of different businesses and use cases across all kinds of different industries and verticals. While there's a lot different about us, we're all here together right now for this talk because we do have some things in common. We're all using Vertica, and we're probably also using Vertica with other systems and tools too, because it's important to use the right tool for the right job. That's a founding principle of Vertica and it's true today too. In this constantly changing technology landscape, we need lots of good tools and well established patterns, approaches, and advice on how to combine them so that we can be successful doing our jobs. Luckily for us, Vertica has been designed to be easy to build with and extended in this fashion. Databases as a whole had had this goal from the very beginning. They solve the hard problems of managing data so that you don't have to worry about it. Instead of worrying about those hard problems, you can focus on what matters most to you and your domain. So implementing that business logic, solving that problem, without having to worry about all of these intense, sometimes details about what it takes to manage a database at scale. With the declarative syntax of SQL, you tell Vertica what the answer is that you want. You don't tell Vertica how to get it. Vertica will figure out the right way to do it for you so that you don't have to worry about it. So this SQL abstraction is very nice because it's a well defined boundary where lots of developers know SQL, and it allows you to express what you need without having to worry about those details. So we can be the experts in data management while you worry about your problems. This goes beyond though, what's accessible through SQL to Vertica. We've got well defined extension and integration points across the product that allow you to customize this experience even further. So if you want to do things write your own SQL functions, or extend database softwares with UDXs, you can do so. If you have a custom data format that might be a proprietary format, or some source system that Vertica doesn't natively support, we have extension points that allow you to use those. To make it very easy to do passive, parallel, massive data movement, loading into Vertica but also to export Vertica to send data to other systems. And with these new features in time, we also could do the same kinds of things with Machine Learning models, importing and exporting to tools like TensorFlow. And it's these integration points that have enabled Vertica to build out this open architecture and a rich ecosystem of tools, both open source and closed source, of different varieties that solve all different problems that are common in this big data processing world. Whether it's open source, streaming systems like Kafka or Spark, or more traditional ETL tools on the loading side, but also, BI tools and visualizers and things like that to view and use the data that you keep in your database on the right side. And then of course, Vertica needs to be flexible enough to be able to run anywhere. So you can really take Vertica and use it the way you want it to solve the problems that you need to solve. So Vertica has always employed open standards, and integrated it with all kinds of different open source systems. What we're really excited to talk about now is that we are taking our new integration projects and making those open source too. In particular, we've got two new open source client libraries that allow you to build Vertica applications for Python and Go. These libraries act as a foundation for all kinds of interesting applications and tools. Upon those libraries, we've also built some integrations ourselves. And we're using these new libraries to power some new integrations with some third party products. Finally, we've got lots of new examples and reference implementations out on our GitHub page that can show you how to combine all these moving parts and exciting ways to solve new problems. And the code for all these things is available now on our GitHub page. And so you can use it however you like, and even help us make it better too. So the first such project that we have is called Vertica-Python. Vertica-Python began at our customer, Uber. And then in late 2018, we collaborated with them and we took it over and made Vertica-Python the first official open source client for Vertica You can use this to build your own Python applications, or you can use it via tools that were written in Python. Python has grown a lot in recent years and it's very common language to solve lots of different problems and use cases in the Big Data space from things like DevOps admission and Data Science or Machine Learning, or just homegrown applications. We use Python a lot internally for our own QA testing and automation needs. And with the Python 2 End Of Life, that happened at the end of 2019, it was important that we had a robust Python solution to help migrate our internal stuff off of Python 2. And also to provide a nice migration path for all of you our users that might be worried about the same problems with their own Python code. So Vertica-Python is used already for lots of different tools, including Vertica's admintools now starting with 9.3.1. It was also used by DataDog to build a Vertica-DataDog integration that allows you to monitor your Vertica infrastructure within DataDog. So here's a little example of how you might use the Python Client to do some some work. So here we open in connection, we run a query to find out what node we've connected to, and then we do a little DataLoad by running a COPY statement. And this is designed to have a familiar look and feel if you've ever used a Python Database Client before. So we implement the DB API 2.0 standard and it feels like a Python package. So that includes things like, it's part of the centralized package manager, so you can just PIP install this right now and go start using it. We also have our client for Go length. So this is called vertica-sql-go. And this is a very similar story, just in a different context or the different programming language. So vertica-sql-go, began as a collaboration with the Microsoft Focus SecOps Group who builds microfocus' security products some of which use vertica internally to provide some of those analytics. So you can use this to build your own apps in the Go programming language but you can also use it via tools that are written Go. So most notably, we have our Grafana integration, which we'll talk a little bit more about later, that leverages this new clients to provide Grafana visualizations for vertica data. And Go is another rising popularity programming language 'cause it offers an interesting balance of different programming design trade-offs. So it's got good performance, got a good current concurrency and memory safety. And we liked all those things and we're using it to power some internal monitoring stuff of our own. And here's an example of the code you can write with this client. So this is Go code that does a similar thing. It opens a connection, it runs a little test query, and then it iterates over those rows, processing them using Go data types. You get that native look and feel just like you do in Python, except this time in the Go language. And you can go get it the way you usually package things with Go by running that command there to acquire this package. And it's important to note here for the DC projects, we're really doing open source development. We're not just putting code out on our GitHub page. So if you go out there and look, you can see that you can ask questions, you can report bugs, you can submit poll requests yourselves and you can collaborate directly with our engineering team and the other vertica users out on our GitHub page. Because it's out on our GitHub page, it allows us to be a little bit faster with the way we ship and deliver functionality compared to the core vertica release cycle. So in 2019, for example, as we were building features to prepare for the Python 3 migration, we shipped 11 different releases with 40 customer reported issues, filed on GitHub. That was done over 78 different poll requests and with lots of community engagement as we do so. So lots of people are using this already, we see as our GitHub badge last showed with about 5000 downloads of this a day of people using it in their software. And again, we want to make this easy, not just to use but also to contribute and understand and collaborate with us. So all these projects are built using the Apache 2.0 license. The master branch is always available and stable with the latest creative functionality. And you can always build it and test it the way we do so that it's easy for you to understand how it works and to submit contributions or bug fixes or even features. It uses automated testing both for locally and with poll requests. And for vertica-python, it's fully automated with Travis CI. So we're really excited about doing this and we're really excited about where it can go in the future. 'Cause this offers some exciting opportunities for us to collaborate with you more directly than we have ever before. You can contribute improvements and help us guide the direction of these projects, but you can also work with each other to share knowledge and implementation details and various best practices. And so maybe you think, "Well, I don't use Python, "I don't use go so maybe it doesn't matter to me." But I would argue it really does matter. Because even if you don't use these tools and languages, there's lots of amazing vertica developers out there who do. And these clients do act as low level building blocks for all kinds of different interesting tools, both in these Python and Go worlds, but also well beyond that. Because these implementations and examples really generalize to lots of different use cases. And we're going to do a deeper dive now into some of these to understand exactly how that's the case and what you can do with these things. So let's take a deeper look at some of the details of what it takes to build one of these open source client libraries. So these database client interfaces, what are they exactly? Well, we all know SQL, but if you look at what SQL specifies, it really only talks about how to manipulate the data within the database. So once you're connected and in, you can run commands with SQL. But these database client interfaces address the rest of those needs. So what does the programmer need to do to actually process those SQL queries? So these interfaces are specific to a particular language or a technology stack. But the use cases and the architectures and design patterns are largely the same between different languages. They all have a need to do some networking and connect and authenticate and create a session. They all need to be able to run queries and load some data and deal with problems and errors. And then they also have a lot of metadata and Type Mapping because you want to use these clients the way you use those programming languages. Which might be different than the way that vertica's data types and vertica's semantics work. So some of this client interfaces are truly standards. And they are robust enough in terms of what they design and call for to support a truly pluggable driver model. Where you might write an application that codes directly against the standard interface, and you can then plug in a different database driver, like a JDBC driver, to have that application work with any database that has a JDBC driver. So most of these interfaces aren't as robust as a JDBC or ODBC but that's okay. 'Cause it's good as a standard is, every database is unique for a reason. And so you can't really expose all of those unique properties of a database through these standard interfaces. So vertica's unique in that it can scale to the petabytes and beyond. And you can run it anywhere in any environment, whether it's on-prem or on clouds. So surely there's something about vertica that's unique, and we want to be able to take advantage of that fact in our solutions. So even though these standards might not cover everything, there's often a need and common patterns that arise to solve these problems in similar ways. When there isn't enough of a standard to define those comments, semantics that different databases might have in common, what you often see is tools will invent plug in layers or glue code to compensate by defining application wide standard to cover some of these same semantics. Later on, we'll get into some of those details and show off what exactly that means. So if you connect to a vertica database, what's actually happening under the covers? You have an application, you have a need to run some queries, so what does that actually look like? Well, probably as you would imagine, your application is going to invoke some API calls and some client library or tool. This library takes those API calls and implements them, usually by issuing some networking protocol operations, communicating over the network to ask vertica to do the heavy lifting required for that particular API call. And so these API's usually do the same kinds of things although some of the details might differ between these different interfaces. But you do things like establish a connection, run a query, iterate over your rows, manage your transactions, that sort of thing. Here's an example from vertica-python, which just goes into some of the details of what actually happens during the Connect API call. And you can see all these details in our GitHub implementation of this. There's actually a lot of moving parts in what happens during a connection. So let's walk through some of that and see what actually goes on. I might have my API call like this where I say Connect and I give it a DNS name, which is my entire cluster. And I give you my connection details, my username and password. And I tell the Python Client to get me a session, give me a connection so I can start doing some work. Well, in order to implement this, what needs to happen? First, we need to do some TCP networking to establish our connection. So we need to understand what the request is, where you're going to connect to and why, by pressing the connection string. and vertica being a distributed system, we want to provide high availability, so we might need to do some DNS look-ups to resolve that DNS name which might be an entire cluster and not just a single machine. So that you don't have to change your connection string every time you add or remove nodes to the database. So we do some high availability and DNS lookup stuff. And then once we connect, we might do Load Balancing too, to balance the connections across the different initiator nodes in the cluster, or in a sub cluster, as needed. Once we land on the node we want to be at, we might do some TLS to secure our connections. And vertica supports the industry standard TLS protocols, so this looks pretty familiar for everyone who've used TLS anywhere before. So you're going to do a certificate exchange and the client might send the server certificate too, and then you going to verify that the server is who it says it is, so that you can know that you trust it. Once you've established that connection, and secured it, then you can start actually beginning to request a session within vertica. So you going to send over your user information like, "Here's my username, "here's the database I want to connect to." You might send some information about your application like a session label, so that you can differentiate on the database with monitoring queries, what the different connections are and what their purpose is. And then you might also send over some session settings to do things like auto commit, to change the state of your session for the duration of this connection. So that you don't have to remember to do that with every query that you have. Once you've asked vertica for a session, before vertica will give you one, it has to authenticate you. and vertica has lots of different authentication mechanisms. So there's a negotiation that happens there to decide how to authenticate you. Vertica decides based on who you are, where you're coming from on the network. And then you'll do an auth-specific exchange depending on what the auth mechanism calls for until you are authenticated. Finally, vertica trusts you and lets you in, so you going to establish a session in vertica, and you might do some note keeping on the client side just to know what happened. So you might log some information, you might record what the version of the database is, you might do some protocol feature negotiation. So if you connect to a version of the database that doesn't support all these protocols, you might decide to turn some functionality off and that sort of thing. But finally, after all that, you can return from this API call and then your connection is good to go. So that connection is just one example of many different APIs. And we're excited here because with vertica-python we're really opening up the vertica client wire protocol for the first time. And so if you're a low level vertica developer and you might have used Postgres before, you might know that some of vertica's client protocol is derived from Postgres. But they do differ in many significant ways. And this is the first time we've ever revealed those details about how it works and why. So not all Postgres protocol features work with vertica because vertica doesn't support all the features that Postgres does. Postgres, for example, has a large object interface that allows you to stream very wide data values over. Whereas vertica doesn't really have very wide data values, you have 30, you have long bar charts, but that's about as wide as you can get. Similarly, the vertica protocol supports lots of features not present in Postgres. So Load Balancing, for example, which we just went through an example of, Postgres is a single node system, it doesn't really make sense for Postgres to have Load Balancing. But Load Balancing is really important for vertica because it is a distributed system. Vertica-python serves as an open reference implementation of this protocol. With all kinds of new details and extension points that we haven't revealed before. So if you look at these boxes below, all these different things are new protocol features that we've implemented since August 2019, out in the open on our GitHub page for Python. Now, the vertica-sql-go implementation of these things is still in progress, but the core protocols are there for basic query operations. There's more to do there but we'll get there soon. So this is really cool 'cause not only do you have now a Python Client implementation, and you have a Go client implementation of this, but you can use this protocol reference to do lots of other things, too. The obvious thing you could do is build more clients for other languages. So if you have a need for a client in some other language that are vertica doesn't support yet, now you have everything available to solve that problem and to go about doing so if you need to. But beyond clients, it's also used for other things. So you might use it for mocking and testing things. So rather than connecting to a real vertica database, you can simulate some of that. You can also use it to do things like query routing and proxies. So Uber, for example, this log here in this link tells a great story of how they route different queries to different vertical clusters by intercepting these protocol messages, parsing the queries in them and deciding which clusters to send them to. So a lot of these things are just ideas today, but now that you have the source code, there's no limit in sight to what you can do with this thing. And so we're very interested in hearing your ideas and requests and we're happy to offer advice and collaborate on building some of these things together. So let's take a look now at some of the things we've already built that do these things. So here's a picture of vertica's Grafana connector with some data powered from an example that we have in this blog link here. So this has an internet of things use case to it, where we have lots of different sensors recording flight data, feeding into Kafka which then gets loaded into vertica. And then finally, it gets visualized nicely here with Grafana. And Grafana's visualizations make it really easy to analyze the data with your eyes and see when something something happens. So in these highlighted sections here, you notice a drop in some of the activity, that's probably a problem worth looking into. It might be a lot harder to see that just by staring at a large table yourself. So how does a picture like that get generated with a tool like Grafana? Well, Grafana specializes in visualizing time series data. And time can be really tricky for computers to do correctly. You got time zones, daylight savings, leap seconds, negative infinity timestamps, please don't ever use those. In every system, if it wasn't hard enough, just with those problems, what makes it harder is that every system does it slightly differently. So if you're querying some time data, how do we deal with these semantic differences as we cross these domain boundaries from Vertica to Grafana's back end architecture, which is implemented in Go on it's front end, which is implemented with JavaScript? Well, you read this from bottom up in terms of the processing. First, you select the timestamp and Vertica is timestamp has to be converted to a Go time object. And we have to reconcile the differences that there might be as we translate it. So Go time has a different time zone specifier format, and it also supports nanosecond precision, while Vertica only supports microsecond precision. So that's not too big of a deal when you're querying data because you just see some extra zeros, not fractional seconds. But on the way in, if we're loading data, we have to find a way to resolve those things. Once it's into the Go process, it has to be converted further to render in the JavaScript UI. So that there, the Go time object has to be converted to a JavaScript Angular JS Date object. And there too, we have to reconcile those differences. So a lot of these differences might just be presentation, and not so much the actual data changing, but you might want to choose to render the date into a more human readable format, like we've done in this example here. Here's another picture. This is another picture of some time series data, and this one shows you can actually write your own queries with Grafana to provide answers. So if you look closely here you can see there's actually some functions that might not look too familiar with you if you know vertica's functions. Vertica doesn't have a dollar underscore underscore time function or a time filter function. So what's actually happening there? How does this actually provide an answer if it's not really real vertica syntax? Well, it's not sufficient to just know how to manipulate data, it's also really important that you know how to operate with metadata. So information about how the data works in the data source, Vertica in this case. So Grafana needs to know how time works in detail for each data source beyond doing that basic I/O that we just saw in the previous example. So it needs to know, how do you connect to the data source to get some time data? How do you know what time data types and functions there are and how they behave? How do you generate a query that references a time literal? And finally, once you've figured out how to do all that, how do you find the time in the database? How do you do know which tables have time columns and then they might be worth rendering in this kind of UI. So Go's database standard doesn't actually really offer many metadata interfaces. Nevertheless, Grafana needs to know those answers. And so it has its own plugin layer that provides a standardizing layer whereby every data source can implement hints and metadata customization needed to have an extensible data source back end. So we have another open source project, the Vertica-Grafana data source, which is a plugin that uses Grafana's extension points with JavaScript and the front end plugins and also with Go in the back end plugins to provide vertica connectivity inside Grafana. So the way this works, is that the plugin frameworks defines those standardizing functions like time and time filter, and it's our plugin that's going to rewrite them in terms of vertica syntax. So in this example, time gets rewritten to a vertica cast. And time filter becomes a BETWEEN predicate. So that's one example of how you can use Grafana, but also how you might build any arbitrary visualization tool that works with data in Vertica. So let's now look at some other examples and reference architectures that we have out in our GitHub page. For some advanced integrations, there's clearly a need to go beyond these standards. So SQL and these surrounding standards, like JDBC, and ODBC, were really critical in the early days of Vertica, because they really enabled a lot of generic database tools. And those will always continue to play a really important role, but the Big Data technology space moves a lot faster than these old database data can keep up with. So there's all kinds of new advanced analytics and query pushdown logic that were never possible 10 or 20 years ago, that Vertica can do natively. There's also all kinds of data-oriented application workflows doing things like streaming data, or Parallel Loading or Machine Learning. And all of these things, we need to build software with, but we don't really have standards to go by. So what do we do there? Well, open source implementations make for easier integrations, and applications all over the place. So even if you're not using Grafana for example, other tools have similar challenges that you need to overcome. And it helps to have an example there to show you how to do it. Take Machine Learning, for example. There's been many excellent Machine Learning tools that have arisen over the years to make data science and the task of Machine Learning lot easier. And a lot of those have basic database connectivity, but they generally only treat the database as a source of data. So they do lots of data I/O to extract data from a database like Vertica for processing in some other engine. We all know that's not the most efficient way to do it. It's much better if you can leverage Vertica scale and bring the processing to the data. So a lot of these tools don't take full advantage of Vertica because there's not really a uniform way to go do so with these standards. So instead, we have a project called vertica-ml-python. And this serves as a reference architecture of how you can do scalable machine learning with Vertica. So this project establishes a familiar machine learning workflow that scales with vertica. So it feels similar to like a scickit-learn project except all the processing and aggregation and heavy lifting and data processing happens in vertica. So this makes for a much more lightweight, scalable approach than you might otherwise be used to. So with vertica-ml-python, you can probably use this yourself. But you could also see how it works. So if it doesn't meet all your needs, you could still see the code and customize it to build your own approach. We've also got lots of examples of our UDX framework. And so this is an older GitHub project. We've actually had this for a couple of years, but it is really useful and important so I wanted to plug it here. With our User Defined eXtensions framework or UDXs, this allows you to extend the operators that vertica executes when it does a database load or a database query. So with UDXs, you can write your own domain logic in a C++, Java or Python or R. And you can call them within the context of a SQL query. And vertica brings your logic to that data, and makes it fast and scalable and fault tolerant and correct for you. So you don't have to worry about all those hard problems. So our UDX examples, demonstrate how you can use our SDK to solve interesting problems. And some of these examples might be complete, total usable packages or libraries. So for example, we have a curl source that allows you to extract data from any curlable endpoint and load into vertica. We've got things like an ODBC connector that allows you to access data in an external database via an ODBC driver within the context of a vertica query, all kinds of parsers and string processors and things like that. We also have more exciting and interesting things where you might not really think of vertica being able to do that, like a heat map generator, which takes some XY coordinates and renders it on top of an image to show you the hotspots in it. So the image on the right was actually generated from one of our intern gaming sessions a few years back. So all these things are great examples that show you not just how you can solve problems, but also how you can use this SDK to solve neat things that maybe no one else has to solve, or maybe that are unique to your business and your needs. Another exciting benefit is with testing. So the test automation strategy that we have in vertica-python these clients, really generalizes well beyond the needs of a database client. Anyone that's ever built a vertica integration or an application, probably has a need to write some integration tests. And that could be hard to do with all the moving parts, in the big data solution. But with our code being open source, you can see in vertica-python, in particular, how we've structured our tests to facilitate smooth testing that's fast, deterministic and easy to use. So we've automated the download process, the installation deployment process, of a Vertica Community Edition. And with a single click, you can run through the tests locally and part of the PR workflow via Travis CI. We also do this for multiple different python environments. So for all python versions from 2.7 up to 3.8 for different Python interpreters, and for different Linux distros, we're running through all of them very quickly with ease, thanks to all this automation. So today, you can see how we do it in vertica-python, in the future, we might want to spin that out into its own stand-alone testbed starter projects so that if you're starting any new vertica integration, this might be a good starting point for you to get going quickly. So that brings us to some of the future work we want to do here in the open source space . Well, there's a lot of it. So in terms of the the client stuff, for Python, we are marching towards our 1.0 release, which is when we aim to be protocol complete to support all of vertica's unique protocols, including COPY LOCAL and some new protocols invented to support complex types, which is our new feature in vertica 10. We have some cursor enhancements to do things like better streaming and improved performance. Beyond that we want to take it where you want to bring it. So send us your requests in the Go client fronts, just about a year behind Python in terms of its protocol implementation, but the basic operations are there. But we still have more work to do to implement things like load balancing, some of the advanced auths and other things. But they're two, we want to work with you and we want to focus on what's important to you so that we can continue to grow and be more useful and more powerful over time. Finally, this question of, "Well, what about beyond database clients? "What else might we want to do with open source?" If you're building a very deep or a robust vertica integration, you probably need to do a lot more exciting things than just run SQL queries and process the answers. Especially if you're an OEM or you're a vendor that resells vertica packaged as a black box piece of a larger solution, you might to have managed the whole operational lifecycle of vertica. There's even fewer standards for doing all these different things compared to the SQL clients. So we started with the SQL clients 'cause that's a well established pattern, there's lots of downstream work that that can enable. But there's also clearly a need for lots of other open source protocols, architectures and examples to show you how to do these things and do have real standards. So we talked a little bit about how you could do UDXs or testing or Machine Learning, but there's all sorts of other use cases too. That's why we're excited to announce here our awesome vertica, which is a new collection of open source resources available on our GitHub page. So if you haven't heard of this awesome manifesto before, I highly recommend you check out this GitHub page on the right. We're not unique here but there's lots of awesome projects for all kinds of different tools and systems out there. And it's a great way to establish a community and share different resources, whether they're open source projects, blogs, examples, references, community resources, and all that. And this tool is an open source project. So it's an open source wiki. And you can contribute to it by submitting yourself to PR. So we've seeded it with some of our favorite tools and projects out there but there's plenty more out there and we hope to see more grow over time. So definitely check this out and help us make it better. So with that, I'm going to wrap up. I wanted to thank you all. Special thanks to Siting Ren and Roger Huebner, who are the project leads for the Python and Go clients respectively. And also, thanks to all the customers out there who've already been contributing stuff. This has already been going on for a long time and we hope to keep it going and keep it growing with your help. So if you want to talk to us, you can find us at this email address here. But of course, you can also find us on the Vertica forums, or you could talk to us on GitHub too. And there you can find links to all the different projects I talked about today. And so with that, I think we're going to wrap up and now we're going to hand it off for some Q&A.

[Music] hello and welcome to this cube conversation here in Palo Alto California I'm John for your host of the cube we're here the very special guest I met while he is CEO of informatica newly appointed CEO about a month ago a little over a month ago had a product before that been with informatics in 2013 informatica went private in 2015 and has since been at the center of the digital transformation around data data transformation data privacy data everything around data and value in AI that made great to see you and congratulations on the new CEO role at informatica so thank you all it's good to be back here John it's been great to follow you and for the folks who don't know you you've been a very product centric CEO your products and CEO as they call it but also now you have a company in the middle of the transformation cloud scale is really mainstream enterprises look at multi cloud hybrid cloud this is something that you've been on for many many years we've talked about it so now that you're in charge you get the ship you get the wheel and you're in your hands were you taking it what is the update of informatica give us the update well thank you solook business couldn't be better I think to give you a little bit of color wavy coming from the last couple of years informatica went through a huge amount of transformation all things trying to transform our business model pivoting to subscription all things heavily bet into cloud the new workloads as we talked about and all things new like AI to give a little bit of color we basically exited it last year with a billion dollars of ARR not just revenue so we're a billion-dollar AR our company and as we pivot it to subscription as subscription business for the last couple of years has been growing north of 55 percent so that's the scale at which we are running multi-billion dollars and if you look at the other two metrics which we keep very click near and dear to hard one is innovation so we are participated in five magic quadrants and we are the leader in all five magic quadrants five one five as we like to call it Gartner Magic Quadrant very very critical to us because innovation in the tech is you know is very important also customer loyalty very important to us so we again were the number one in customer satisfaction continues to grow sore last IDC survey our market share continue to grow and be the number one in all our markets so business couldn't be at a better place where we are and what again some of the business discussed which first method on the Magic Quadrant front it's very difficult the folks that aren't in the club is to understand that to participate in multiple magic quadrants with many many do is hard because clouds horizontally scalable magic partners used to be old IT kind of categories but to be in multiple magic quadrants is the nature of the beast but to be a leader is very difficult because magic question doesn't truly capture that if you just appear play and then try to be cloud so you guys are truly that horizontal brand and and technology we've covered this on the cube so there's no secret but I want to get your comments on to be a leader and today in these quadrants you have to be on all the right waves you've got data warehouses are growing and changing at the rise of snowflake you guys partner with data bricks again machine learning and AI changing very rapidly and there's a huge growth wave behind it as well as the existing enterprises who were you know transforming you know analytics and operational workloads this is really really challenging can you just share your thoughts on why is it so hard what are the some of the key things behind these trends we can analytics I guess you can do if it's just analytics without great but this is a this horizontal data play is not easy can you share why no so yes first we are actually a I would say a very hidden secret we're the only software company and I'll say that again the only software company that was the leader in the traditional world traditional workloads legacy on-premise and via the leader in the cloud workloads not a single software company can say that they were the leader when they were started 27 years ago and there's still the leader in the magic quadrants today our cloud by the way runs at 10 trillion transactions a month scale and obviously we partner with all the hyper scalars across the board and our goal is to be the Switzerland of data for our customers and the question you ask is is a critical one when you think of he business drivers what a customer's trying to do one of them is all all things cloud all things the eye is obviously there but one is all data warehouses are going to cloud we just talked about that moving workloads to cloud whether it is analytical operational basically we have front and center helping customers do that second a big trend in the world of digital transformation is helping our customers customer experience and driving that fueling that is a master data management business so on and so products behind that but driving customer experiences big big driver of our growth and the third one is no large enterprise can live without data governance need a privacy man this is a thing today right you got to make sure that you deliver good governance whether it's compliance oriented or brand oriented privacy and risk management and all three of them basically span the business initiatives that feature into those five magic quadrants our goal is to play across all of them and that's what we do Pat Cal senior had a quote on the cube many years ago he said if you're not on the right wave you could be driftwood its meaning you're gonna get crashed oh sorry well a lot of people have we've seen a lot of companies have a good skill and then get washed away if you will by a wave you're seeing like AI and machine learning we talked a little bit about that you guys are in there I want to get your thoughts on this one is there whenever this executive changes there's always questions around you know what's happening with the company so I want you to talk about the state of informatica because you're now the CEO there's been some changes has there been a pivot has there been a sharpening focus what's going on with informatica so I think I'm cool right now is to scale and hyper scale that's the word I mean we're in a very strong position in fact we use this phrase internally within the company next phase of great we're at a great place and we are chartering the next phase of great for the company and the cool there is helping our customers I talked about these three big big initiatives that companies are investing in data warehousing and analytics going to the cloud transforming customer experiences and data governance and privacy and the fourth one that underpins all of them is all things a I mean as we've talked about before right all of these things are complex hard to do look at the volume and complexity of data and what we're investing in is what we call native ai ai needs data data needs AI as I always said right and we are investing in AI to make these things easy for our customers to make sure that they can scale and grow into the future and what we've also been very diligent about this partnering we partnered very well with the hyper scalars like whether it's AWS Microsoft whether it's GCP snowflake great partner of ours data brick skate part of ours tableau great partner of ours we have a variety of these partners and our cool is always customer first customers are investing in these technologies our goal is to help customers adopt these technologies not for the sake of technologies but for the sake of transforming those three business initiatives I thought you brought up I was gonna ask you the next question but snowflake and data versus data Brooks has been on the cube Holly a great that's a good friend of ours and he's got chops you Stan I'm not Stanford Berkeley he'll kill me with that if it's ow he's but beta Brooks is doing well they made some good bets and it's paying off of them snowflake a rising star Frank's Lubin's over there now they are clearly a choice for modern data warehouses as is any of us redshift how are you working with snowflake how do you take advantage of that can you just unpack your relationship with snowflake it's a it's a very deep partnership our goal is to help our customers you know as they pick these technology choices for data warehousing an example where snowflake comes into play to make sure that the underlying data infrastructure can work seamlessly for them see customers build this complex logic sitting in the old technologies as they move to anything new they want to make sure that that transition migration is seamless as seamless as it can be and typically they'll start something new before they retire something old with us they can carry all of that business logic for the last 27 years their business logic seamlessly and run natively in this case in the cloud so basically we allow them this whole from tool and also the ability to have the best of breed technology in the context of data management to power up these new infrastructures where they are going let me ask you the question around the industry trends what are the top and trends industry trends that are driving your business and your product direction and customer value look digital transformation has been a big trend and digital transformation has fueled all things like customer experiences being transformed so that remains a big vector of growth I would say cloud adoption is still relatively literally inning so no you love these balls we can still say what second third inning as much as we would like to believe cloud has been their customers mode with their analytical workloads first still happening the operational workloads are still in its very very infancy so that is still a big vector of growth and and a big trend that we see for the next five plus years and you guys in the middle of that oh absolutely yeah absolutely because if you're running a large operational workload it's all about the data at the end of the day because you can change the app but it's the data that you want to carry the logic that you've written that you want to carry and we participate in that I have ashes before but I want to ask you again because I want to get the modern update because pure cloud born in the cloud like you know startups and whatever it's easy to say that do that everyone knows that hybrid is clear now everyone that sees that as an architectural thing Multi cloud is kind of a state of I have multiple clouds but being true multi-cloud a little bit different maybe downstream conversation but certainly relevant so as cloud evolves from public cloud hybrid and maybe multi or certainly multi how do you see those things evolving for informatica well we believe in the word hybrid and I define hybrid exactly as these two things one is hybrid is multi cloud you can have hybrid clouds second is hybrid means you're gonna have ground and cloud interoperate for a period of time so to us we sit in the center of this hybrid cloud trend and our goal is to help customers go cloud native but make sure that they can run whatever was the old business that they were running as much possible in the most seamlessly before they can at some point cut over and which is why as I said I've been our cloud native business a cloud platform which we call informatica intelligent cloud services runs at scale globally across the globe by the way on all hyper scalars at ten plus trillion transactions a month but yet we will allowed customers to run their own Prem technologies as much as they can because they cannot just rip the band-aid over there right so multi cloud ground cloud our goal is to help customers large enterprise customers manage that complexity their AI plays a big role because these are all very complex environments and our investment in AI our REI being called Claire is to help them manage that as in an as automated way as seen this away and to be honest the most important thing for them is in the most governed way because that's where the biggest risk risks come into play that's where our investments let's say what customers per second I want to get your thoughts on this because at Amazon reinvent last year in December it was a meme going around on the queue that we that we start on the cube called if you think the tea out of cloud native it's cloud naive and so the the the point was is to say hey doing cloud native makes sense in certain cases but if you're not really thinking about the overall hybrid and the architecture of what's going on you kind of could get into a night naive situation so I asked any of this and I want to ask you any chat so I'll ask you the same question is that what would be naive for a customer to think about cloud so they can be cloud native or operate in a cloud what are some of the things they should avoid so they don't fall into that naive category now you've been you know I hey I'm doing cloud yeah for clouds sake I mean so there's kind of this perception have you got to do cloud right mm-hmm what's your view on cloud native and how does people avoid the cloud naive label it's it's a good question I think to me when I talk to customers and hundreds of them across the globe is I meet them in a year is to really think of their cloud as a reference architecture for at least the next five years if not I'm a technology changes think of a reference architecture for the next five years and in that you got to think of multiple best-of-breed technologies that can help you I mean you got to think best-of-breed as much as possible now you're not going to go have hundreds of different technologies running around because you got to scale them but think as much as possible that you are Best of Breed yet settled to what I call a few platforms as much as possible and then make sure that you basically have the right connection points across different workloads will be optimal for different let's say cloud environments analytical workload and operation workload a financial workload each one of them will have something that will work best in somewhere else right so to me putting the business focus on what the right business outcome is and working you will be back to what cloud environments are best suited for that and building that reference architecture thoughtfully with a five-year goal in mind then jumping to the next most exciting thing hot thing and try to experiment your way through it that will not scale would be the right way to go yeah it's not naive to be focusing on the business problems and operating it in a cloud architecture this is what you're saying okay so let's talk about like the customer journey around AI because this has become a big one you guys been on the AI way for many many years but now that it's become full mainstream enterprise how are the applications software guys looking at this because if I'm an enterprise and I want to go cloud native app to make my apps work yes apps are driving everything these days and you guys play a big role data is more important than ever for applicants what's your view on the app developer DevOps market so to me the big chains of VC in fact we're gonna talk a lot about that in a couple of months when we are at informatica world our user conference in May is how data is moving to the next phase and it's what developers today are doing is that they are building the apps with data in mind first data first apps I mean if you're building let's see a great customer service app you gotta first figure out what all data do you need to service a customer before you go build an app so that is a very fundamental shift that has happened and and in that context what happens is that in a cloud native environment obviously you have a lot of flexibility to begin with that bring data over there and DevOps is getting complemented by what we see is data ops having all kinds of data available for you to make those decisions as you build an application and in that discussion you're near having before is that there is so much data that you will not be able to understand that investing in metadata so you can understand data about the data I called metadata as the intelligent data if you're an intelligent enterprise you gotta invest in metadata those are the places where we see developers going first and from their ground up building what we call apps that are more intelligent apps of the future not just business process apps cloud native versus cloud naive connotation we were just having is interesting you talk about Best of Breed I want to get your thoughts on some trends we're seeing seeing even in cybersecurity with RSA coming up there's been consolidation you saw our Dell Jesolo RSA 2 private equity company so you starting to see a lot of these shiny new toy type companies being consolidated in because there's too much for companies to deal with you're seeing also skills gaps but also skills shortages there's not enough people oh now you have multiple clouds you got Amazon you got Azure you got Google GCP you've got Oracle IBM VMware now you have a shortage problem true so this is putting pressure on the customers so with that in mind how are the customers reacting to this and what is best to breed really mean so that is actually a very good point look we all live in Silicon Valley so we get excited about the latest technology and we have the best of skills here even though we have a skills problem over here right think about as you move away from Silicon Valley and you start flying and I fly all over the world and you start seeing that if you're in the middle of nowhere there is not a whole lot of developers who understand the latest cutting-edge technology that happens here our goal has been to solve that problem for our customers look our goal is to help the developers but as much as possible provide the customers the ability to have a handful of skilled developers but they can still take our offerings and we abstract away that complexity so that they are dealing only at a higher level the underlying technology comes and goes and you know it will come and go 100 times they don't have to worry about that so our goal is abstract away the underlying changes in technology focus at the business logic layer and you can move you can basically run your business for over the course of 20 years and that's what we've done for customers customers were invested with us have run their businesses seamlessly for two decades three decades while so much technology has changed with a period of time and the cloud is right here scaling up so I want to get your thoughts on the different clouds I see Amazon Web Services number one the cloud hyper scalar we're talking pure cloud that gets more announcements more capabilities then you got a sure again hyper scale trying to catch up to Amazon more Enterprise focused are doing very very well on the enterprise I was I said on Twitter they're mopping up the enterprise because it's easy to have an install base there they've been leveraging your very well stuff in atella has done team done a great job that you got Google trying to specialize and figure out where they're gonna fit Oracle IBM everyone else as you'd have to deal with this you're kind of an arms dealer in a way with data I would love to say no hands but not absolute I'm dealing that's the bad analogy but you get my point you have to play well you have to it's not like an aspiration show your requirements you have to play and operate with value in all the clouds one how is that going and what are the different clouds like well I always begin with the philosophy that its customer first you go with the customers a queen and customers choose different technologies for different use cases as deems fit for them our job is to make sure our customers are successful so we begin with the customer in mind and we solve from there number two that's a big market there is plenty of room for everybody to play of course there is competition across the board but plenty of room for everybody to play and our job is to make sure that we assist all of them to help at the end of the day our joint customers we have great success stories with all of them again you get in mind the end customer so that has always been informatic as philosophy customer first and we partner with a critical strategic partners in that context and and we invest and we've invested with all of them deep partnerships of all of them they've all been at informatica well you've seen them so again as I said and I think the easiest way we obviously believe they do this incident of data but keep the customer in mind all the time and everything follows from there what is multi-cloud me to your customers if your customer centric obviously we hear people say yeah I use this for that and I get that when I talk to CIOs and see says with his real dollars and interact on the business there tends to be a gravitational pull towards one cloud a lot of people are building their own stacks in house development has shifted to be very DevOps I'm cloud native and then I'll have a secondary cloud but they recognize that they have multiple clouds but they're not spreading their staff around for the reasons around skill shortage yeah are you seeing that same trend and to what do you see is multi cloud well it is 1d cloud I think I think people sometimes don't realize they're already in a multi cloud world I mean you have so many SAS applications running around right look around that so whether you have work day with your salesforce.com and I can keep going on and on and on right there are multiple similarly multi platform clouds are there right I mean people are using hash or for some use case they may want to go a dime us for certain other negative use cases so quite naturally customers begin with something to begin with and then the scale from there but they realize as we as I talk to customers I realize hey look I have use cases and they're optimally set for some things that are multi-cloud and they'll end up there but they all have to begin somewhere before they go somewhere so I have multiple clouds which I agree with you by the way and talking about this one cube a lot there's multi multiple clouds and then this interoperability among clouds I mean remember multi-vendor back in the old days multi-cloud it kind of feels like a multi vendor kind of value proposition but if I have Salesforce or workday in these different clouds in Amazon where I'm developing or Azure what is the multi cloud interoperability is it the data control plane what problems are the customers facing and the challenge that they want to turn into opportunities do a good example multi-cloud see a good example one of the biggest areas of growth for us is helping a customers transform the customer experience now if you think about an enterprise company that is thinking about having a great understanding of their customer now just think about the number of places that customer data sets one of the one of the big areas of investment viability the CRM product called salesforce.com right good customer data sits there but there could be where ticketing data sets there could be where marketing data sits there could be some legacy applications the customer data sits in so many places more often than not we realize when we talk to a customer it sits in at least 20 places within an enterprise and then there is so much customer data sitting outside of the firewalls of an enterprise right clickstream data where people had parts or shared a partner data so in that context bringing that data together becomes extremely important for you to have a full view of your customer and deliver a better customer experience from there so it is the cost the customers have the problem it's a huge problem right now huge problem right now across the board where cup a per customer like hey I want to serve my customer better but I need to know my customer better before I can serve them better so we are squarely in the middle of that helping and B being the Switzerland of data being fully understanding the application layer and the platform layer we can bring all that stuff and through the lens of our customer 360 which is fueled by our master data management product we allow customers to get to see that full view and from there you can service them better give them a next best offer or you can understand their lives either full lifetime value for customer so on and so forth so that's how we see the world and that's how we help our customers in this really fragmented cloud world that's your primary value proposition it's a huge value proposition and again as I said always think customer first I met you got your big event coming up this spring so looking forward to seeing you there I want to get your take as now that you're looking at the next great chapter of informatica what is your vision how do you see that twenty miles stare out in the marketplace as you execute again your product oriented CEO because your product chops now you're leading the team what's your vision what's the 20 mile stair well as simple as possible we're gonna double the company our goal is to double the company across the board we have a great foundation of innovation we put together and we remain paranoid all the time as to where and we always start to look where the world is going serve our customers and as long as we have great customer loyalty which we have today have the foundations of great innovation and a great team and culture at the company which we fundamentally believe in we basically right now have the vision of doubling the company that's awesome well really appreciate you taking the time one final question I want to get your thoughts on you know it's looking valley and in the industry starting to see Indian American executives become CEOs you now see you have informatica congratulations Arvind over at IBM sathi natella this has been a culture of the technology for generations I remember when I broken the business in the late 80s 90s this is the pure love of tech and the and the meritocracy of Technology is at play here this is a historic moment it's been written about but I want to get your thoughts on how you see it evolving and advice for young entrepreneurs out there future CEOs what's it take to get there what's it like what's your personal thoughts well first of all it's been a humbling moment for me to lead in from it's a great company and a great opportunity I mean I can say like it's the true Americans dream I mean I came here in 1998 I mean as a lot of immigrants Ted didn't have much in my pocket I went to business school I was deep in loans and and I believed in the opportunity and I think there is something very special about America and I would say something really special about Silicon Valley where it's all about at the end of the day value it's all about meritocracy the color of your skin and your accent and your those things don't really matter and I think we're such an embracing culture typically over here and my advice to anybody is that look believe and I genuinely use that word and I've gone through stages in my life where you sometimes doubt it but you have to believe and stay honest what you want and look there is no substitute to hard work sometimes luck does play a role but there is no substitute artwork and at the end of the day good things happen as we say that for the love of the game love attack your tech athlete love to love to interview and congratulate been great to follow your career get to know you and informatica it's great to see you at the helm thank you John pleasure being here I'm John 4 here is cube conversation in Palo Alto getting the update on the new CEO from informatics at MIT Walia friend of the cube and of course a great tech athlete and now running the great company I'm John forever here thanks for watching [Music] you [Music]

>> from Austin, Texas. It's Theo Cube covering your storage accelerate 2019. Brought to you by pure storage. How >> do you all how to do Dave Great Legal garden with you? Yes, I am Lisa Martin with David Lantana. And can you guess we're in Texas were at pure Accelerate 2019 Day one of our coverage here and the Buzzy Expo Hall. Pleased to welcome one of Pierre's customers to the Q B of Jason Thomas, the CEO of Coal, Scott Hussein or C. S K Legal Jason. Welcome to the program. So talk to us a little bit about si es que legal. You're based out of Florida. You're CEO. Give us a little bit of a picture of the law firm, your I T environment and your role. ISS leader of information >> So cold, Scott is saying, >> has been around >> 20 plus years. I joined about three and 1/2 years ago, Um, and we have now this one. We have 13 officers. We just opened up 13th office. We're the largest law firm in Florida currently, and only in Florida. Interestingly enough, I actually live and work out of Boston, but you know, these days there's no reason why you can't work remote. I go, they're off enoughto needed. >> You can avoid the hurricanes by living in >> a snowstorm over >> hurting any >> day because I've been a >> good pro sports in Boston. Better, better college sports in Florida. >> Yeah, No one cares about college sports. >> Best of both worlds. All right, so we're here Appear. You guys have been appear customer for a while. But give us this This picture of the legal landscape from a data volume perspective, I could imagine tons of documentation. I think you guys have hundreds of attorneys. What were some of the challenges three years ago when you were looking for the ideal long? You know, storage service is that you were really looking to four companies like your help eliminate and allow you to really deliver on the business needs. >> So we're heavy, heavy volume, business tons and tons of documents. Um, And when I came on board 39 years ago, the ever start of iron was basically a lot of physical servers, a lot of local storage which, quite frankly, scared me. I came from my previous company. I was that I came from a nap shop And that was when my first initiatives was bringing in a sand into the firm and centralizing all the storage on also setting up D r a cz. Well, along with that. So it started evaluation process pretty much within a few months, coming on board the firm. >> So you knew Netapp. Sorry, Dave. You knew Net up your pure customer perspective. Of what? For some of those things that you were looking for that when you found pure was, like, checks all the boxes. >> I can tell you what I wasn't looking for. It was I wasn't looking to hire a storage admin. So I want to find something super simple demand something that I could manage or any of the guys could manage any this this admits, could manage. So that was like starting point of the evaluation. >> So you had a bunch of sounds like discreet Dad asked direct access storage, and he said that concern you, presumably because it was hard to manage to get a handle on. So you wanted to consolidate >> way had if we had our sequel No sequel box go down down for a day, and, uh, do you ever stole from backups in previous night. Not really a good set up at the time >> in our most of your attorneys century, located in one location. Are they distributed there? >> They're spread out all across up and down floors. So we have 13 offices. So between there, they're all over the place. But a lot of work remote down, too. So that's becoming a big thing as well. So the >> reason I asked you to get the pendulum swinging right, you had almost ass, and then you went to a sin. And now this. You got the head you get cloud. I don't know if you're taking advantage of cloud, are you? >> Uh, we are actually we a lot of our software now that we've slowly start to move a lot of our main main line products to the cloud or a cloud edition of this product. So I would say we're probably 50 to 60% cloud now. >> Yes. So you were tied up in the keynotes this morning, but one of the things we heard in the key notice you could have the pure management experience. No matter where your data lives, bring the the pure cloud experience to your date on Prim and the public cloud hybrid. Is that something that's appealing to you? Is that resonate? Yeah. >> Absolutely. Absolutely. It makes it. Look, I can I can actually blogging appear one of my phone if I want to, you know, and check the room. Not that I ever do. Quite. I'll say I never really need to look at >> it. Well, your c i o. Right. I mean, you got other things to worry about. Get my I would like >> to be involved with fingers in it. >> It's interesting. So I mean, you know, a lot of time CEOs, they don't they let, but your tech I love your technical. See a lot of that. A lot of technical CEOs as well, but But also, you don't want to hire a storage admin. Correct. So you want general is to be able to deal this stuff. Okay, so you know your question. Why? Why pure? What would you look at? And >> so we looked at, um, way looked at HP street power. Big name. Um, we looked at fewer and we looked at 10 tree and I pretty much especially with three part I knew that would be management heavy so that when I toss that one out pretty quickly, not that it's not a great product. But it just wasn't for me or what I was >> the right fit. >> You're not right for us. So we came down the pier and 10 tree. I had a had a buddy who worked at another law firm, and he's like and he was like, Look, just don't even waste time just go pure And it's a phrase that I use Sometimes I stole from him, but he he's like, Dude, this is like storage crack. You'll love it. >> Storage crack. Wow, They need a T shirt. That first >> first hit's free. Okay, so that was the right fit for you. It was your peer was appear that that enticed you. That's obviously take a bit. I presume you take a lot of hair advice. >> Lot appeared, but we didn't even do a POC. >> Wow, this is this is a good period that you obviously trust. >> All right, how to >> see was the interface yet you showed me the interface on a phone call one time, and he's like, this is it. I'm like, That's it. >> What did you actually bring in. What are you using? >> I'm sorry, >> What products That you're actually using, What? Or with pure >> Oh, so I'm sorry. Um Exchange sequel. Um, that our main line, our bookkeeping time, time and building. All that that that's that's the meaning of >> all the legal absent all the legal dated the data stores. Which product from pure is that? Do you know a fan? Is it? Uh, it's the all flash array. Yeah. >> I'm sorry. Yes, it's the FBI. >> Yeah. Okay. And so, thinking about before and after hell kind of a as is and the to be how would you compare and contrast two when you brought it in the pre in the post >> your environment. >> Oh, for your business. >> That's Ah, good question. I felt more comfortable sleeping at night. You know why? Just the reliability of the ease of management. You know, if we need to bring up a volume or expanded volume, we could do it very quickly. It doesn't. It doesn't take a rocket science to do it. And from everyone I spoke to I mean, I can't I'm not I can't speak to it, but I can't. I don't I don't believe I've ever talked anybody that's had an outage or whether you raise gone down. In fact, it seems that they tell me before we even know if there's, you know, an issue. Andi. They jump on it right away. So we've never had never had now has never had an issue, never had an issue with an upgrade. It's been fantastic. That supports awesome. >> No need for a rocket scientist or a storage admin, >> and you're sleeping better. This is very, very good thing so far this interview. So in terms of the traditional storage model that you're well familiar with, as you said, you know, being very familiar with netapp it a previous role, the whole every three years. Allies like it. We've got to switch things out, disrupting operations here, comes along with the Evergreen model, and we go, How much of that is marketing and how much of that really actually means? And I know you're a big >> you're in my mind. So yeah, I was like, Oh, so I'm pre paying for support or, you know, But you know what? One side. Once I understood what it really waas and the advantages of of it inmate sentence. We didn't. We didn't I didn't think we would upgrade as much as we have already. We've already gone through to storage up, raising two controller upgrades. So that's really where where it really makes sense is when you're doing storage controller upgrade. So if you want to start our small, which we do is start a little bit small in the beginning. And then then our business grew like crazy and our storage needs expanded. So we went through at least two upgrades for years. >> So you you bring in a rare you paying basically perpetual license up front boom. And then and then you're doing the evergreen model. And then now you're on a subscription in perpetuity, is that correct? Okay, so you you essentially go from cap Ex Op X over the life cycle, and then when you add capacity, you're paying for that capacity, and then >> you just like you return the equipment, you get your money back, and then, uh, you get new equipment >> is truly non disruptive. >> We've been through to upgrades and to control operates with your major upgrades and, um, both of them we did at 5 p.m. Just not that the firm close. If I were anything but, you know, just to feel comfortable. I don't know how you do it at five, and it's okay because you know, if anything goes down from five and if no one's working right, so But here, obviously, we're always attorneys are always on and know they're really smooth. No problems. Every I mean, they got a great strategy and method to the upgrades way stayed up the entire time. >> I mean, it is a big issue for practitioners. We we've done some quantification over the years, and it was like the minimum to migrate. Honore was $50,000. When you add it all in people's time, the cost of the array, the complexity and you're saying first of all, sound reasonable, right kind of number, right? I mean, that's probably gonna make room for the conservative right. Is that essentially been eliminated? I mean, it gives you some planning, I guess are >> pretty much. And as far as the planning goes, you know, these these guys take care of all that. So when we're ready to make the switch, they just log in and do their thing, and then it's done, >> and in terms of training for yourself or your team. When you've done these two upgrades that what's that process been like? >> Log in and figure it out. I mean, >> it sounds pretty simple. >> There's not much to it. Yeah. >> So what's on the C I ose mind these days? Obviously, you don't stay awake at night now thinking about story. >> I stay awake for security, for >> talk about that data >> breach security seems like every every week. Now it it seems I'm on my Twitter feed and this is there's a new breech home. It just it's It's almost got to the point where, you know, it's just another thing that happens. >> So what's your challenge there? Is it managing all these tools? Is it knowing what to respond to it? Is it the skill sets all of the above? My >> biggest thing is, I believe in lots of redundancy. So, um, so one. Starting with the pure we have, we have a second array in another data center outside the state, so we replicate the to raise between each other. That's that's what we started with that side. We also running, you know, regular backups. We run rubric for that. And we also now have just oh, establishing cloud strategy for backups. Immutable. Um, long, long retention. So we also send our backup to the cloud as well. So now I'm feeling like I can sleep. Probably can sleep late now. I just gotta wait for somebody for something to happen, I guess, and makes sure, and hopefully your strategy is pretty solid here. >> Okay, so D r and backup are part of that overall data protection and security strategy that extends obviously into the perimeter device, etcetera, etcetera. So you have a SEC ops team. How do you weigh? >> Don't have a dedicated no. See. So, >> Well, you're the C cell. >> I'm exactly exactly so. Sher Sher bulls with a small group of us that are also the security team. And we've got a pretty I think we've got at this point a pretty solid security sack. Always room for improvement. Always looking at the new stuff. What's out there? I mean, there's all kinds of cool tech out there. Sometimes I get a little overboard with the team, gets a little upset with me because, you know, I just want to see I want to do another POC, and they're like we have three running. >> Okay, Like you guys have a pretty solid foundation running on pure that you stone to me, like, kind of appear customer for life. So they should at least give you a T shirt. Um, Adam, >> give me atleast >> a T shirt. >> I'll tell you one what really sold me within the first year was we had a We had a B m that wouldn't wouldn't boot up and we couldn't figure out what was going on. So we thought initially thought was a V m where issue and so we call support and you can really figure out. They said it was a pure issue. We call so decide to call Pure. One night I was 89 o'clock at night and decide to give it a shot, and the guy got on the phone and come to find. Now there was some issue with the data stores of'em where it was crossed, her data stores and one was deleted. Oh, apparently maybe me had deleted a small data store that had nothing on it, but apparently it was linked to the data store. This b m for some unknown reason known. Behold, bmr issue. But the guy on the line actually knew of resource within pure. That was That was a big bm weren't guy and he came in. He actually logged in and help us unlinked to data stores. So totally not appear issue. But, you know, he went the extra mile to help us recover that GM gotta back up the same night. >> You know, we got to go, But I ask you a question. You work. You have a lot of vendors you've experienced. What, Avengers do that really tick you off? That they should stop doing? How's your chance? >> I don't like the term road map. >> Really? >> Any time I hear road map, it means, you know >> we don't have it. You >> don't have >> yet, >> But we're gonna look into that so don't do business with people that have no road. >> Jason, thank you so much for share your candor with David. Me on the key. We appreciate it. Congratulations on all your success. >> Thank you >> for David. Dante. I'm Lisa Martin. You're watching the Cube at pure accelerate 19. Thanks for watching

>> from Bahrain. It's the Q covering AWS Public sector Bahrain brought to you by Amazon Web service is >> Okay Welcome back, everyone to the cube coverage We are hearing by rain for a W s summit where cloud computing is changing the games. The Fintech panel discussion Yasmine el Sharif, head of Fintech Innovation Unit, Central Bank of Rain Thank you for joining >> us. Thank you for having me >> Elmo Yacht. Whose founder and CEO of Ammonia Technologies Thank you for coming on. Thank you for having so We're very robust Conversation before they turn on the cameras Fit in tech is hot. I'll see in global fintech Everyone knows what that is, but it's interesting because entrepreneurship and innovation is not just for start ups. It's for countries and hearing by rain, this ecosystem and the mandate to go cloud first has had a ripple effect. We were talking about open banking, mandate, open banking versus regulation, chasing innovation, holding it back. You guys here taking a different approach. Take a minute to explain the philosophy. >> Yeah, I think there's there's benefits to being late adopters to the game. I think in the case of behind it's been a very interesting journey. I think the we started with the whole AWS. But if you look at the prerequisites of technical adoption and creating Data Pool's for analytics to run on, I think the what's interesting about Bahrain is it's really led by regulation. If you look at the prerequisites of creating a digital economy, what's happening in financial service is, or the digitization or openness of financial service. Is it really one context off the bigger picture of Bahrain's digitization plan or the economic strategy? And really, what happens here is if you look at first built the data fools and or the data centers bring a W. A s in and create the data centers. Number two is creator data or cloud First policy. Move the entire government onto the cloud and then give the ownership of the data to the people by implementing the Bahrain personal data protection laws. Once you've done that, then you've given the ownership to the people and you've created what we have is we started with a unique identifies. So the citizens of the country or the residents of the country have a unique identify our number where they're known by once you've done that and then you start mandating certain sectors to open up with a P I integrations. You're creating a very, very interesting value proposition. It creates a much faster you leap frog, a generation of technology. You're going from the classic screen scraping technologies or whatever to a very a completely open infrastructure and open a P I. Where things air cryptographic Lee signed. People are in control of their data, people can control the mobility of their date, and you're really creating a very robust data pool for a lot of algorithms to sit on. >> You know what I love about this has me were talking before he came on cameras that you guys are thinking holistically as a knocking operating system is being in a geek that I am. I love that. But it's not just one thing you're doing, it's a it's a system and it's it's a modernization view. Now we all know that financial systems, power economies and fin tech innovation unit, but you're in. This is important. You gotta have that. That leg of the stool, that pillar that's working absolutely sandbox. You have technology mechanisms to roll in tech, move things quickly moving fast. What's the strategy? What if some of the key things What's the sandbox? >> Let me start by saying The Kingdom of Bahrain has always been considered as a centre of excellence as a financial centre of excellence. And we do realize at the Central Bank in order for us to maintain that position, we have to innovate. We have to remain dynamic and agile enough to make the necessary reforms within our regulations to meet the dynamics off the digital economy. Technology is changing the paradigm off the financial system on the changes happening extremely fast. Regulators have had to come up with a mechanism whereby they can harness and test the feasibility of these innovations whilst putting the risks in a controlled environments as regulators were not typically assigned to host incubators to host startups. However, because of all this change in technology, it has become extremely essential that we come up with a regulatory approach to enable startups as well as existing financial institutions to test out their innovative financial solutions in a controlled environment. So a sandbox is really a controlled live bounds time bounds environment, enabling startups as well as existing financial institutions to test out their innovative solutions under the strict supervision off the regulator, without being required to abide by full regulatory requirements directly with volunteer customers. >> You have to put this trick standards now but means sandboxes. What developers? No, it's a collaborative approach, absolutely not being an incubator. But you're setting up a rules of engagement, Senator startups to take what they know how to do >> exactly >> end up sandboxes in the cloud. That's what everyone does >> absolutely, and our journey with the sandbox has been very successful. We've launched our sandbox back in 2000 and 17. Up to date, we have 35 companies that have been admitted into the sun box. We have been able to graduate to companies successfully. One of them has been licensed as a crypto acid provider, the other as an open biking service provider. We have four other companies in the pipeline ready to graduates. I think all in all, our experience with Sun Box has enabled us to grow and develop his regulators. It has enabled us to maintain open communication with animators, to come tea, to learn the needs of innovators and to enable innovators to live, get familiar realized. With the regulatory environment of the Kingdom of Bahrain, >> you know, you guys are doing some really pioneering work. I wouldn't want to say it's really commendable. I know it's fast and new, but if you look at the United States with Facebook there now asking to be regulated regulation if it comes too late is bad because you know things got out of control and if you're too early, you can put a clamp down and stifle innovation. So the balance between regulation and innovation has always been an art, if you will. >> Exactly. >> What do you guys, How do you view that? What's the philosophy? >> So from a regular perspective, we think that regulation and innovation goes hand in hand, and we have to embrace innovation open heartedly. However, having said that, regulators have to run all common sense checks, meaning that we don't accept an innovation that will potentially pulls more harm to the financial stability of the economy as opposed to the advantages that puzzles. We've passed the number of different regulations to support innovation in the financial services sector dating back to 2014 when we first issued our payment service provider licenses allowing more competition and innovation within the payments sector. We've issued CROWDFUNDING regulations. We've issued robo advisory regulations. We've issued insurance aggregator regulations, crypto asset service provider regulations, open banking regulations, Justin in a few. And I think that each of the regulations that we have issued solves a specific pain point, whether it's to enhance financial inclusion, whether it's to empower customers by retaining ownership back, uh, of their financial information and data, Whether it's too also empower startups and to enable them to get it gain access to funding through digital platforms. >> Have dual. I want to get you in here because as an entrepreneur, like I love all that great, I just wanna get funded. I want my product to market. I need a capital market that's going to be robust. And I need to have that's capital providers state venture capital for private equity supporting their limited partners. So I want to see that I don't wanna be standing there when I need gas for my car. I need fuel. I got to get to the next level. This is what I want And he bought >> on. I think, the one thing John that is very important that people look at in the context of fintech today. Raising money investing into fintech Regulatory uncertainty is one that defines scalability today. Once your technology is proven, where you go next really is dependent on the regulator that you'll be dealing with in the context of that specific activity that you'll be performing. In the case of Bahrain, I must say we were blown away by the receptiveness. We in what way? Yes, yes, mean mentioned open banking, for example. We got into the regulatory sandbox, which you hear a lot about sandboxes all around the world. We got into the sandbox. We got into the sandbox with contact with with with an idea of building and accounts aggregator direct FBI integration to these banks. And we got into the sandbox. We There were no regulations at the time. They like the idea. We started bouncing ideas back and forth on how to develop it. We developed the technology. We started piloting the technology. We integrated to 15 banks in the country on a sandbox environment. The consul, the white paper on open banking, was listed. They sent it out for consultation. We integrated on a production environment to more than 70% of the banks in it in the country. The central Bank of Bahrain mandated open banking across the entire nation. With every retail bank all in a period of less than 18 months. That's insane. That's the kind of context. So as a no Vester exactly so as an investor or as an entrepreneur that looks at the sector. The question is here. If anything, I think the regulator in Bahrain is the one that's leading the innovation and these air the benefits of being late adopters. We get to test out and see what's going on in the rest of the world and really develop great regulations that will embrace and and foster innovation. >> You know, I love the liquidity conversation because this neck goes to the next level. Liquidity is a wonderful thing started. Wanna go public? If that's what happens in the U. S. Mergers and acquisitions, we have an incubator that we're gonna interview here flat Six labs just had to come. One of their companies got sold to match dot com. So you're seeing a lot of cross border liquidity. Yeah, this is a new dynamic. It's only gonna get stronger, more come. He's gonna come out of my reign in the region. Liquid is important. Absent. So how do you guys want to foster that? What's the strategy? Continue to do the same. >> So from a regular perspective again, we don't really holds. Thank you. Beaters are actually two accelerators, but what we do as we refined our regulations to support startups to gain access to liquidity, for example, are crowdfunding regulations that have been passed in 2017 and they support both. Equity is one of financing crowdfunding, including conventional as well as Sharia compliant. Crowdfunding transactions were also currently working on refining our regulations for enabling venture capitalists to take roots and marine and to support these startups. >> Yeah, I think John, you mentioned two things you mentioned regulation leading. When you mandate something like open banking, you are ultimately pushing the entire sector forward, saying you better innovators fastest possible. And there's a gap that you need to you need to basically bridge, and that really loosens up a lot of liquidity when it comes to partnerships. When it comes to acquisitions, when it comes to these banks ultimately looking for better solutions, so they that's the role of the regulator. Here we are seeing a lot of VC activity come to the region right now, the region is only starting to open up. AWS just went live a few months ago. We're seeing the cloud adoption start to really take effect, and this is where you'll start seeing real scalability. But I think the most compelling thing here is Previously people would look at the Middle East with a boot with a bit of skepticism. How much innovation can really take place and the reality is here. There are a few prerequisites that have been put in place. Foreign ownership is at 100% cloud. First policy. There's a lot of things that can really foster innovation. And we're, I mean, where as an entrepreneur, where living proof off this whole Team Bahrain initiative of the fact that you can get in you can build in accounts aggregator in a country that never even had the regulations to adopted to mandate it and to be Ultimately, I think Bahrain will become the global reference point for open banking very soon because it has mandated a regulation of open AP eyes with cryptographic signatures ultimate security frameworks with a robust infrastructure across an entire nation. And don't forget, we still have a population of below the age of 30 70% of our population below. So it gives a very compelling story t test your technology. And then what we end up saying is, once you're on AWS or any cloud for that matter than the scalability of the technology just depends on where you want to go in there. >> No doubt the demographics are solid here, and I love the announcement here. The bachelor's degree. Yeah, cloud computing. We've seen some data science degrees, so new skills are coming on. My vision is interesting. I think that would interest me about the region of Amazon. Being here is these regions create revitalisation? >> Yeah, you >> guys are in perfect position with this Modernization trend is beautiful, not only to be a template for the world but a center for global banking. So I think to me, is that, you know is I'm trying to put together and connect the dots of where this goes in the next two decades. I mean, if crypto currency market continues to get matured and stabilized, that's still flowing with a lot of money. A lot of money in the relay >> absolutely >> was not just the region business to do here for couples to come here. It's you guys playing a role in global financial system. That's of interest to me. What's your vision? >> Absolutely. I think that regulators around the world are starting to realize the importance of collaborating together, to try and work on policy challenges in line with innovation within the financial service of sector and to share experiences to share lessons learned at the Central Bank of Bahrain were a member of the Global Financial Innovation Network, which is an initiative that has Bean passed by the F C A in the UK Again, we're also a member of the authentic working group of the GCC and through these two different initiatives, we work alongside other regulators to collaborate on solving policy issues, to solve, to share experiences and knowledge and to try and harmonize our regulations. Because of the end of the day, startups and innovators ultimately will want to scale up and want to serve customers across the friend jurisdictions. So it's important to have that kind of harmonization in terms of regulations to foster innovation as well as to safeguard the overall security of the international financial. Um, >> keep partnerships. Do you guys need to do to kind of go global on this 20 year vision? Is there other things they have to fall into place? That needs to happen? >> I think >> 20 years is a long time, I say in the next. Let's take five years, for example. If you say in the next five years and where I see this going, the question is, what do entrepreneurs and startups need to look at a jurisdiction and say That's where I want to test my technology. You need a robust infrastructure. You need a regulator than embraces you. You need technical subsidies and financial subsidies that are available, and then you need an independent arm that can really hand hold you and take you to that >> thrust. Its critical trust, money making absolutely ability. >> Just add to that and Byron, we take great pride in our human capital, which we believe is one of our biggest assets. And today, with having your Amazon web service is in Bahrain, this has enabled training of young Bahrainis for the data and knowledge economies which is expected Thio greet around 5000 jobs within becoming five years through different schemes such as Amazon education. For example. >> This is super exciting, which we had more time. Congratulations. Love the vision again. Occupiers like to make money. They wanted environments could be trustworthy and some scalability on behind it. So good luck. We're behind you. We'll keep following up. Thanks for having a cube coverage here and by rain for AWS. I'm John Ferrier. Stay tuned for more after this short break.