>>Welcome back everyone to the Cube's live coverage here in San Francisco of VMware Explorer, 2022. I'm John furrier with Dave ante coast of the hub. We're two sets, three days of wall to wall coverage. Our 12 year covering VMware's annual conference day, formerly world. Now VMware Explorer. We're kicking off day tube, no Sharma director of product management at Google cloud GCP. No Thankss for coming on the cube. Good to see you. >>Yeah. Very nice to see you as well. >>It's been a while. Google next cloud. Next is your event. We haven't been there cuz of the pandemic. Now you got an event coming up in October. You wanna give that plug out there in October 11th, UHS gonna be kind of a hybrid show. You guys with GCP, doing great. Getting up, coming up on in, in the rear with third place, Amazon Azure GCP, you guys have really nailed the developer and the AI and the data piece in the cloud. And now with VMware, with multicloud, you guys are in the mix in the universal program that they got here had been, been a partnership. Talk about the Google VMware relationship real quick. >>Yeah, no, I wanna first address, you know, us being in third place. I think when, when customers think about cloud transformation, you know, they, they, for them, it's all about how you can extract value from the data, you know, how you can transform your business with AI. And as far as that's concerned, we are in first place. Now coming to the VMware partnership, what we observed was, you know, you know, first of all, like there's a lot of data gravity built over the past, you know, 20 years in it, you know, and you know, VMware has, you know, really standardized it platforms. And when it comes to the data gravity, what we found was that, you know, customers want to extract the value that, you know, lives in that data as I was just talking about, but they find it hard to change architectures and, you know, bring those architectures into, you know, the cloud native world, you know, with microservices and so forth. >>Especially when, you know, these applications have been built over the last 20 years with off the shelf, you know, commercial off the shelf in, you know, systems you don't even know who wrote the code. You don't know what the IP address configuration is. And it's, you know, if you change anything, it can break your production. But at the same time, they want to take advantage of what the cloud has to offer. You know, the self-service the elasticity, you know, the, the economies of scale efficiencies of operation. So we wanted to, you know, bring CU, you know, bring the cloud to where the customer is with this service. And, you know, with, like I said, you know, VMware was the defacto it platform. So it was a no brainer for us to say, you know what, we'll give VMware in a native manner yeah. For our customers and bring all the benefits of the cloud into it to help them transform and take advantage of the cloud. >>It's interesting. And you called out that the, the advantages of Google cloud, one of the things that we've observed is, you know, VMware trying to be much more cloud native in their messaging and their positioning. They're trying to connect into that developer world for cloud native. I mean, Google, I mean, you guys have been cloud native literally from day one, just as a company. Yeah. Infrastructure wise, I mean, DevOps was an infrastructures code was Google's DNA. I, you had Borg, which became Kubernetes. Everyone kind of knows that in the history, if you, if you're in, in the, inside the ropes. Yeah. So as you guys have that core competency of essentially infrastructures code, which is basically cloud, how are you guys bringing that into the enterprise with the VMware, because that's where the puck is going. Right. That's where the use cases are. Okay. You got data clearly an advantage there, developers, you guys do really well with developers. We see that at say Coon and CNCF. Where's the use cases as the enterprise start to really figure out that this is now happening with hybrid and they gotta be more cloud native. Are they ramping up certain use cases? Can you share and connect the dots between what you guys had as your core competency and where the enterprise use cases are? >>Yeah. Yeah. You know, I think transformation means a lot of things, especially when you get into the cloud, you want to be not only efficient, but you also wanna make sure you're secure, right. And that you can manage and maintain your infrastructure in a way that you can reason about it. When, you know, when things go wrong, we took a very unique approach with Google cloud VMware engine. When we brought it to the cloud to Google cloud, what we did was we, we took like a cloud native approach. You know, it would seem like, you know, we are to say that, okay, VMware is cloud native, but in fact that's what we've done with this service from the ground up. One of the things we wanted to do was make sure we meet all the enterprise needs availability. We are the only service that gives four nines of SLA in a single site. >>We are the only service that has fully redundant networking so that, you know, some of the pets that you run on the VMware platform with your operational databases and the keys to the kingdom, you know, they can be run in a efficient manner and in a, in a, in a stable manner and, and, you know, in a highly available fashion, but we also paid attention to performance. One of our customers Mitel runs a unified communication service. And what they found was, you know, the high performance infrastructure, low latency infrastructure actually helps them deliver, you know, highly reliable, you know, communication experience to their customers. Right. And so, you know, we, you know, while, you know, so we developed the service from the ground up, making sure we meet the needs of these enterprise applications, but also wanted to make sure it's positioned for the future. >>Well, integrated into Google cloud VPC, networking, billing, identities, access control, you know, support all of that with a one stop shop. Right? And so this completely changes the game for, for enterprises on the outset, but what's more like we also have built in integration to cloud operations, you know, a single pane of glass for managing all your cloud infrastructure. You know, you have the ability to easily ELT into BigQuery and, you know, get a data transformation going that way from your operational databases. So, so I think we took a very like clean room ground from the ground of approach to make sure we get the best of both worlds to our customers. So >>Essentially made the VMware stack of first class citizen connecting to all the go Google tool. Did you build a bare metal instance to be able to support >>That? We, we actually have a very customized infrastructure to make sure that, you know, the experience that customers looking for in the VMware context is what we can deliver to them. And, and like I said, you know, being able to manage the pets in, in addition to the cattle that, that we are, we are getting with the modern containerized workloads. >>And, and it's not likely you did that as a one off, I, I would presume that other partners can potentially take advantage of that, that approach as well. Is that >>True? Absolutely. So one of our other examples is, is SAP, you know, our SAP infrastructure runs on very similar kind of, you know, highly redundant infrastructure, some, some parts of it. And, and then, you know, we also have in the same context partners such as NetApp. So, so customers want to, you know, truly, so, so there's two parts to it, right? One is to meet customers where they already are, but also take them to the future. And partner NetApp has delivered a cloud service that is well integrated into the platform, serves use cases like VDI serves use cases for, you know, tier two data protection scenarios, Dr. And also high performance context that customers are looking for, explain >>To people because think a lot of times people understand say, oh, NetApp, but doesn't Google have storage. Yeah. So explain that relationship and why that, that is complimentary. Yeah. And not just some kind of divergence from your strategy. >>Yeah. Yeah. No. So I think the, the idea here is NetApp, the NetApp platform living on-prem, you know, for, for so many years, it's, it's built a lot of capabilities that customers take advantage of. Right. So for example, it has the sta snap mirror capabilities that enable, you know, instant Dr. Of between locations and customers. When they think of the cloud, they are also thinking of heterogeneous context where some of the infrastructure is still needs to live on prem. So, you know, they have the Dr going on from the on-prem side using snap mirror, into Google cloud. And so, you know, it enables that entry point into the cloud. And so we believe, you know, partnering with NetApp kind of enables these high performance, you know, high, you know, reliability and also enables the customers to meet regulatory needs for, you know, the Dr. And data protection that they're looking for. And, >>And NetApp, obviously a big VMware partner as well. So I can take that partnership with VMware and NetApp into the Google cloud. >>Correct. Yeah. Yeah. It's all about leverage. Like I said, you know, meeting customers where they already are and ensuring that we smoothen their journey into the future rather than making it like a single step, you know, quantum leap. So to speak between two words, you know, I think, you know, I like to say like for the, for the longest time the cloud was being presented as a false choice between, you know, the infrastructure as of, of the past and the infrastructure of the future, like the red pill and the blue pill. Right. And, you know, we've, I like to say, like, I've, you know, we've brought, brought into the, into this context, the purple pill. Right. Which gives you really the best of both tools. >>Yeah. And this is a tailwind for you guys now, and I wanna get your thoughts on this and your differentiation around multi-cloud that's around the corner. Yeah. I mean, everyone now recognizes at least multi clouds of reality. People have workloads on AWS, Azure and GCP. That is technically multi-cloud. Yeah. Now the notion of spanning applications across clouds is coming certainly hybrid cloud is a steady state, which essentially DevOps on prem or edge in the cloud. So, so you have, now the recognition that's here, you guys are positioned well for this. How is that evolving and how are you positioning yourself with, and how you're differentiating around as clients start thinking, Hey, you know what, I can start running things on AWS and GCP. Yeah. And OnPrem in a really kind of a distributed way. Yeah. With abstractions and these things that people are talking about super cloud, what we call it. And, and this is really the conversations. Okay. What does that next future around the corner architecture look like? And how do you guys fit in, because this is an opportunity for you guys. It's almost, it's almost, it's like Wayne Gretsky, the puck is coming to you. Yeah. Yeah. It seems that way to me. What, how do you respond to >>That? Yeah, no, I think, you know, Raghu said, yes, I did yesterday. Right. It's all about being cloud smart in this new heterogeneous world. I think Google cloud has always been the most open and the most customer oriented cloud. And the reason I say that is because, you know, looking at like our Kubernetes platform, right. What we've enabled with Kubernetes and Antho is the ability for a customer to run containerized infrastructure in the same consistent manner, no matter what the platform. So while, you know, Kubernetes runs on GKE, you can run using Anthos on the VMware platform and you can run using Anthos on any other cloud on the planet in including AWS Azure. And, and so it's, you know, we, we take a very open, we've taken an open approach with Kubernetes to begin with, but, you know, the, the fact that, you know, with Anthos and this multicloud management experience that we can provide customers, we are, we are letting customers get the full freedom of an advantage of what multicloud has to has to offer. And I like to say, you know, VMware is the ES of ISAs, right. Cause cuz if you think about it, it's the only hypervisor that you can run in the same consistent manner, take the same image and run it on any of the providers. Right. And you can, you know, link it, you know, with the L two extensions and create a fabric that spans the world and, and, and multiple >>Products with, with almost every company using VMware. >>That's pretty much that's right. It's the largest, like the VMware network of, of infrastructure is the largest network on the planet. Right. And so, so it's, it's truly about enabling customer choice. We believe that every cloud, you know, brings its advantages and, you know, at the end of their day, the technology of, you know, capabilities of the provider, the differentiation of the provider need to stand on its merit. And so, you know, we truly embrace this notion of money. Those ops guys >>Have to connect to opportunities to connect to you, you guys in yeah. In, in the cloud. >>Yeah. Absolutely >>Like to ask you a question sort of about database philosophy and maybe, maybe futures a little bit, there seems to be two camps. I mean, you've got multiple databases, you got span for, you know, kind of global distributed database. You've got big query for analytics. There seems to be a trend in the industry for some providers to say, okay, let's, let's converge the transactions and analytics and kind of maybe eliminate the need to do a lot of Elting and others are saying, no, no, we want to be, be, you know, really precise and distinct with our capabilities and, and, and have be spoke set of capability, right. Tool for the right job. Let's call it. What's Google's philosophy in that regard. And, and how do you think about database in the future? >>So, so I think, you know, when it comes to, you know, something as general and as complex as data, right, you know, data lives in all ships and forms, it, it moves at various velocities that moves at various scale. And so, you know, we truly believe that, you know, customers should have the flexibility and freedom to put things together using, you know, these various contexts and, and, you know, build the right set of outcomes for themselves. So, you know, we, we provide cloud SQL, right, where customers can run their own, you know, dedicated infrastructure, fully managed and operated by Google at a high level of SLA compared to any other way of doing it. We have a database born in the cloud, a data warehouse born in the cloud BigQuery, which enables zero ops, you know, zero touch, you know, instant, you know, know high performance analytics at scale, you know, span gives customers high levels of reliability and redundancy in, in, in a worldwide context. So with, with, with extreme levels of innovation coming from, you know, the, the, the NTP, you know, that happen across different instances. Right? So I, you know, I, we, we do think that, you know, data moves a different scale and, and different velocity and, and, you know, customers have a complex set of needs. And, and so our portfolio of database services put together can truly address all ends of the spectrum. >>Yeah. And we've certainly been following you guys at CNCF and the work that Google cloud's doing extremely strong technical people. Yeah. Really open source focused, great products, technology. You guys do a great job. And I, I would imagine, and it's clear that VMware is an opportunity for you guys, given the DNA of their customer base. The installed base is huge. You guys have that nice potential connection where these customers are kind of going where its puck is going. You guys are there now for the next couple minutes, give a, give a plug for Google cloud to the VMware customer base out there. Yeah. Why Google cloud, why now what's in it for them? What's the, what's the value parts? Give the, give the plug for Google cloud to the VMware community. >>Absolutely. So, so I think, you know, especially with VMware engine, what we've built, you know, is truly like a cloud native next generation enterprise platform. Right. And it does three specific things, right? It gives you a cloud optimized experience, right? Like the, the idea being, you know, self-service efficiencies, economies, you know, operational benefits, you get that from the platform and a customer like Mitel was able to take advantage of that. Being able to use the same platform that they were running in their co-located context and migrate more than a thousand VMs in less than 90 days, something that they weren't able to do for, for over two years. The second aspect of our, you know, our transformation journey that we enable with this service is cloud integration. What that means is the same VPC experience that you get in the, the, the networking global networking that Google cloud has to offer. >>The VMware platform is fully integrated into that. And so the benefits of, you know, having a subnet that can live anywhere in the world, you know, having multi VPC, but more importantly, the benefits of having these Google cloud services like BigQuery and span and cloud operations management at your fingertips in the same layer, three domain, you know, just make an IP call and your data is transformed into BigQuery from your operational databases and car four. The retailer in Europe actually was able to do that with our service. And not only that, you know, do do the operational transform into BigQuery, you know, from their, the data gravity living in VMware on, on VMware engine, but they were able to do it in, you know, cost effective, a manner. They, they saved, you know, over 40% compared to the, the current context and also lower the co increase the agility of operations at the same time. >>Right. And so for them, this was extremely transf transformative. And lastly, we believe in the context of being open, we are also a very partner friendly cloud. And so, you know, customers come bring VMware platform because of all the, it, you know, ecosystem that comes along with it, right. You've got your VM or your Zerto or your rubric, or your capacity for data protection and, and backup. You've got security from Forex, tha fortunate, you know, you've got, you know, like we'd already talked about NetApp storage. So we, you know, we are open in that technology context, ISVs, you know, fully supported >>Integrations key. Yeah, >>Yeah, exactly. And, and, you know, that's how you build a platform, right? Yeah. And so, so we enable that, but, but, you know, we also enable customers getting into the future, going into the future, through their AI, through the AI capabilities and services that are once again available at, at their fingertips. >>Soo, thanks for coming on. Really appreciate it. And, you know, as super clouds, we call it, our multi-cloud comes around the corner, you got the edge exploding, you guys do a great job in networking and security, which is well known. What's your view of this super cloud multi-cloud world. What's different about it? Why isn't it just sass on cloud what's, what's this next gen cloud really about it. You had to kind of kind explain that to, to business folks and technical folks out there. Is it, is it something unique? Do you see a, a refactoring? Is it something that does something different? Yeah. What, what doesn't make it just SAS. >>Yeah. Yeah. No, I think that, you know, there's, there's different use cases that customers have have in mind when they, when they think about multi-cloud. I think the first thing is they don't want to have, you know, all eggs in a single basket. Right. And, and so, you know, it, it helps diversify their risk. I mean, and it's a real problem. Like you, you see outages in, you know, in, in availability zones that take out entire businesses. So customers do wanna make sure that they're not, they're, they're able to increase their availability, increase their resiliency through the use of multiple providers, but I think so, so that's like getting the same thing in different contexts, but at the same time, the context is shifting right. There is some, there's some data sources that originate, you know, elsewhere and there, the scale and the velocity of those sources is so vast, you know, you might be producing video from retail stores and, you know, you wanna make sure, you know, this, this security and there's, you know, information awareness built about those sources. >>And so you want to process that data, add the source and take instant decisions with that proximity. And that's why we believe with the GC and, you know, with, with both, both the edge versions and the hosted versions, GDC stands for Google, Google distributed cloud, where we bring the benefit and value of Google cloud to different locations on the edge, as well as on-prem. And so I think, you know, those kinds of contexts become important. And so I think, you know, we, you know, we are not only do we need to be open and pervasive, you know, but we also need to be compatible and, and, and also have the proximity to where information lives and value lives. >>Minish. Thanks for coming on the cube here at VMware Explorer, formerly world. Thanks for your time. Thank >>You so much. Okay. >>This is the cube. I'm John for Dave ante live day two coverage here on Moscone west lobby for VMware Explorer. We'll be right back with more after the short break.

>> Instructor: "theCUBE" presents KubeCon and CloudNativeCon Europe 2022, brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partners. >> Welcome to Valencia, Spain and we're at KubeCon, CloudNativeCon Europe 2022. I'm Keith Townsend, and my co-host, Enrico Signoretti. Enrico's really proud of me. I've called him Enrico instead of Enrique every session. >> Every day. >> Senior IT analyst at GigaOm. We're talking to fantastic builders at KubeCon, CloudNativeCon Europe 2022 about the projects and their efforts. Enrico, up to this point, it's been all about provisioning, insecurity, what conversation have we been missing? >> Well, I mean, I think that we passed the point of having the conversation of deployment, of provisioning. Everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem a and in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their cluster work, why it is happening and all the questions that come with it. And the more I talk with people in the show floor here or even in the various sessions is about, we are growing so that our clusters are becoming bigger and bigger, applications are becoming bigger as well. So we need to now understand better what is happening. As it's not only about cost, it's about everything at the end. >> So I think that's a great set up for our guests, Matt Provo, founder and CEO of StormForge and Patrick Brixton? >> Bergstrom. >> Bergstrom. >> Yeah. >> I spelled it right, I didn't say it right, Bergstrom, CTO. We're at KubeCon, CloudNativeCon where projects are discussed, built and StormForge, I've heard the pitch before, so forgive me. And I'm kind of torn. I have service mesh. What do I need more, like what problem is StormForge solving? >> You want to take it? >> Sure, absolutely. So it's interesting because, my background is in the enterprise, right? I was an executive at UnitedHealth Group before that I worked at Best Buy and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky-dory, right? But then we run into the issue like you and I were just talking about, where it gets very very expensive very quickly. And so my first conversations with Matt and the StormForge group, and they were telling me about the product and what we're dealing with. I said, that is the problem statement that I have always struggled with and I wish this existed 10 years ago when I was dealing with EC2 costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically what it is, is we take your raw telemetry data and we essentially monitor the performance of your application, and then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over-provisioning. So we reduce your consumption of CPU, of memory and production which ultimately nine times out of 10, actually I would say 10 out of 10, reduces your cost significantly without sacrificing reliability. >> So can your solution also help to optimize the application in the long run? Because, yes, of course-- >> Yep. >> The lowering fluid as you know optimize the deployment. >> Yeah. >> But actually the long-term is optimizing the application. >> Yes. >> Which is the real problem. >> Yep. >> So, we're fine with the former of what you just said, but we exist to do the latter. And so, we're squarely and completely focused at the application layer. As long as you can track or understand the metrics you care about for your application, we can optimize against it. We love that we don't know your application, we don't know what the SLA and SLO requirements are for your app, you do, and so, in our world it's about empowering the developer into the process, not automating them out of it and I think sometimes AI and machine learning sort of gets a bad rap from that standpoint. And so, at this point the company's been around since 2016, kind of from the very early days of Kubernetes, we've always been, squarely focused on Kubernetes, using our core machine learning engine to optimize metrics at the application layer that people care about and need to go after. And the truth of the matter is today and over time, setting a cluster up on Kubernetes has largely been solved. And yet the promise of Kubernetes around portability and flexibility, downstream when you operationalize, the complexity smacks you in the face and that's where StormForge comes in. And so we're a vertical, kind of vertically oriented solution, that's absolutely focused on solving that problem. >> Well, I don't want to play, actually. I want to play the devils advocate here and-- >> You wouldn't be a good analyst if you didn't. >> So the problem is when you talk with clients, users, there are many of them still working with Java, something that is really tough. I mean, all of us loved Java. >> Yeah, absolutely. >> Maybe 20 years ago. Yeah, but not anymore, but still they have developers, they have porting applications, microservices. Yes, but not very optimized, et cetera, cetera, et cetera. So it's becoming tough. So how you can interact with this kind of old hybrid or anyway, not well engineered applications. >> Yeah. >> We do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage and we, like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So perfect example is Java, you have to worry about your heap size, your garbage collection tuning and one of the things that really struck me very early on about the StormForge product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and performance tuning, we were only as good as our humans were because of what they knew. And so, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that the machine will recommend things you never would've dreamed of. And you get amazing results out of that. >> So both me and Enrico have been doing this for a long time. Like, I have battled to my last breath the argument when it's a bare metal or a VM, look, I cannot give you any more memory. >> Yeah. >> And the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources are expensive, buy bigger box. >> Yeah. >> Yap. >> Buying a bigger box in the cloud to your point is no longer a option because it's just expensive. >> Yeah. >> Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? Is it the shift in responsibility? >> I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially as the development of applications becomes more and more rapid and the management of them. Our charge and our belief wholeheartedly is that you shouldn't have to choose. You should not have to choose between costs or performance. You should not have to choose where your applications live, in a public private or hybrid cloud environment. And so, we want to empower people to be able to sit in the middle of all of that chaos and for those trade offs and those difficult interactions to no longer be a thing. We're at a place now where we've done hundreds of deployments and never once have we met a developer who said, "I'm really excited to get out of bed and come to work every day and manually tune my application." One side, secondly, we've never met, a manager or someone with budget that said, please don't increase the value of my investment that I've made to lift and shift us over to the cloud or to Kubernetes or some combination of both. And so what we're seeing is the converging of these groups, their happy place is the lack of needing to be able to make those trade offs, and that's been exciting for us. >> So, I'm listening and looks like that your solution is right in the middle in application performance, management, observability. >> Yeah. >> And, monitoring. >> Yeah. >> So it's a little bit of all of this. >> Yeah, so we want to be, the intel inside of all of that, we often get lumped into one of those categories, it used to be APM a lot, we sometimes get, are you observability or and we're really not any of those things, in and of themselves, but we instead we've invested in deep integrations and partnerships with a lot of that tooling 'cause in a lot of ways, the tool chain is hardening in a cloud native and in Kubernetes world. And so, integrating in intelligently, staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for our users who have already invested likely, in a APM or observability. >> So to go a little bit deeper. What does it mean integration? I mean, do you provide data to this, other applications in the environment or are they supporting you in the work that you do. >> Yeah, we're a data consumer for the most part. In fact, one of our big taglines is take your observability and turn it into action ability, right? Like how do you take that, it's one thing to collect all of the data, but then how do you know what to do with it, right? So to Matt's point, we integrate with folks like Datadog, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >> But also we want Datadog customers, for example, we have a very close partnership with Datadog so that in your existing Datadog dashboard, now you have-- >> Yeah. >> The StormForge capability showing up in the same location. >> Yep. >> And so you don't have to switch out. >> So I was just going to ask, is it a push pull? What is the developer experience when you say you provide developer this resolve ML learnings about performance, how do they receive it? Like, what's the developer experience. >> They can receive it, for a while we were CLI only, like any good developer tool. >> Right. >> And, we have our own UI. And so it is a push in a lot of cases where I can come to one spot, I've got my applications and every time I'm going to release or plan for a release or I have released and I want to pull in observability data from a production standpoint, I can visualize all of that within the StormForge UI and platform, make decisions, we allow you to set your, kind of comfort level of automation that you're okay with. You can be completely set and forget or you can be somewhere along that spectrum and you can say, as long as it's within, these thresholds, go ahead and release the application or go ahead and apply the configuration. But we also allow you to experience the same, a lot of the same functionality right now, in Grafana, in Datadog and a bunch of others that are coming. >> So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges or if not, one of the biggest challenges CIOs are facing are resource constraints. >> Yeah. >> They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs-- >> Yeah.6 >> And developers? >> You should take that one. >> Yeah, absolutely. So like my background, like I said at UnitedHealth Group, right. It's not always just about cost savings. In fact, the way that I look about at some of these tech challenges, especially when we talk about scalability there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece 'cause you can only throw money at a problem for so long and it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small, and so, we are absolutely squarely in that footprint of we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. Like, you were talking about private cloud for instance and so having a physical data center, I've worked with physical data centers that companies I've worked for have owned where it is literally full, wall to wall. You can't rack any more servers in it, and so their biggest option is, well, I could spend $1.2 billion to build a new one if I wanted to, or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster, like that's a huge opportunity. >> So I have another question. I mean, maybe it doesn't sound very intelligent at this point, but, so is it an ongoing process or is it something that you do at the very beginning, I mean you start deploying this. >> Yeah. >> And maybe as a service. >> Yep. >> Once in a year I say, okay, let's do it again and see if something change it. >> Sure. >> So one spot, one single.. >> Yeah, would you recommend somebody performance test just once a year? Like, so that's my thing is, at previous roles, my role was to do performance test every single release, and that was at a minimum once a week and if your thing did not get faster, you had to have an executive exception to get it into production and that's the space that we want to live in as well as part of your CICD process, like this should be continuous verification, every time you deploy, we want to make sure that we're recommending the perfect configuration for your application in the name space that you're deploying into. >> And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the CICD process that's connected to optimization and that no application should be released, monitored, and sort of analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, but for cost and performance. >> Almost a couple of hundred vendors on this floor. You mentioned some of the big ones Datadog, et cetera, but what happens when one of the up and comings out of nowhere, completely new data structure, some imaginative way to click to telemetry data. >> Yeah. >> How do, how do you react to that? >> Yeah, to us it's zeros and ones. >> Yeah. >> And, we really are data agnostic from the standpoint of, we're fortunate enough from the design of our algorithm standpoint, it doesn't get caught up on data structure issues, as long as you can capture it and make it available through one of a series of inputs, one would be load or performance tests, could be telemetry, could be observability, if we have access to it. Honestly, the messier the better from time to time from a machine learning standpoint, it's pretty powerful to see. We've never had a deployment where we saved less than 30%, while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and 30 to 40% improvement in performance. >> And what happens if the application is, I mean, yes Kubernetes is the best thing of the world but sometimes we have to, external data sources or, we have to connect with external services anyway. >> Yeah. >> So, can you provide an indication also on this particular application, like, where the problem could be? >> Yeah. >> Yeah, and that's absolutely one of the things that we look at too, 'cause it's, especially when you talk about resource consumption it's never a flat line, right? Like depending on your application, depending on the workloads that you're running it varies from sometimes minute to minute, day to day, or it could be week to week even. And so, especially with some of the products that we have coming out with what we want to do, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like, or, what your disc looks like, right. Like 'cause with our low environment testing, any metric you throw at us, we can optimize for. >> So Matt and Patrick, thank you for stopping by. >> Yeah. >> Yes. >> We can go all day because day two is I think the biggest challenge right now, not just in Kubernetes but application re-platforming and transformation, very, very difficult. Most CTOs and EASs that I talked to, this is the challenge space. From Valencia, Spain, I'm Keith Townsend, along with my host Enrico Signoretti and you're watching "theCube" the leader in high-tech coverage. (whimsical music)

>>The cube presents, Coon and cloud native con Europe 22, brought to you by the cloud native computing foundation. >>Welcome to Melissa Spain. And we're at cuon cloud native con Europe, 2022. I'm Keith Townsend. And my co-host en Rico senior Etti en Rico's really proud of me. I've called him en Rico and said IK, every session, senior it analyst giga, O we're talking to fantastic builders at Cuban cloud native con about the projects and the efforts en Rico up to this point, it's been all about provisioning insecurity. What, what conversation have we been missing? >>Well, I mean, I, I think, I think that, uh, uh, we passed the point of having the conversation of deployment of provisioning. You know, everybody's very skilled, actually everything is done at day two. They are discovering that, well, there is a security problem. There is an observability problem. And in fact, we are meeting with a lot of people and there are a lot of conversation with people really needing to understand what is happening. I mean, in their classroom, what, why it is happening and all the, the questions that come with it. I mean, and, uh, the more I talk with, uh, people in the, in the show floor here, or even in the, you know, in the various sessions is about, you know, we are growing, the, our clusters are becoming bigger and bigger. Uh, applications are becoming, you know, bigger as well. So we need to know, understand better what is happening. It's not only, you know, about cost it's about everything at the >>End. So I think that's a great set up for our guests, max, Provo, founder, and CEO of storm for forge and Patrick Britton, Bergstrom, Brookstone. Yeah, I spelled it right. I didn't say it right. Berg storm CTO. We're at Q con cloud native con we're projects are discussed, built and storm forge. I I've heard the pitch before, so forgive me. And I'm, I'm, I'm, I'm, I'm, I'm kind of torn. I have service mesh. What do I need more like, what problem is storm for solving? >>You wanna take it? >>Sure, absolutely. So it it's interesting because, uh, my background is in the enterprise, right? I was an executive at United health group. Um, before that I worked at best buy. Um, and one of the issues that we always had was, especially as you migrate to the cloud, it seems like the CPU dial or the memory dial is your reliability dial. So it's like, oh, I just turned that all the way to the right and everything's hunky Dory. Right. Uh, but then we run into the issue like you and I were just talking about where it gets very, very expensive, very quickly. Uh, and so my first conversations with Matt and the storm forge group, and they were telling me about the product and, and what we're dealing with. I said, that is the problem statement that I have always struggled with. And I wish this existed 10 years ago when I was dealing with EC two costs, right? And now with Kubernetes, it's the same thing. It's so easy to provision. So realistically, what it is is we take your raw telemetry data and we essentially monitor the performance of your application. And then we can tell you using our machine learning algorithms, the exact configuration that you should be using for your application to achieve the results that you're looking for without over provisioning. So we reduce your consumption of CPU of memory and production, which ultimately nine times outta 10, actually I would say 10 out of 10 reduces your cost significantly without sacrificing reliability. >>So can your solution also help to optimize the application in the long run? Because yes, of course, yep. You know, the lowing fluid is, you know, optimize the deployment. Yeah. But actually the long term is optimizing the application. Yes. Which is the real problem. >>Yep. So we actually, um, we're fine with the, the former of what you just said, but we exist to do the latter. And so we're squarely and completely focused at the application layer. Um, we are, uh, as long as you can track or understand the metrics you care about for your application, uh, we can optimize against it. Um, we love that we don't know your application. We don't know what the SLA and SLO requirements are for your app. You do. And so in, in our world, it's about empowering the developer into the process, not automating them out of it. And I think sometimes AI and machine learning sort of gets a bad wrap from that standpoint. And so, uh, we've at this point, the company's been around, you know, since 2016, uh, kind of from the very early days of Kubernetes, we've always been, you know, squarely focused on Kubernetes using our core machine learning, uh, engine to optimize metrics at the application layer, uh, that people care about and, and need to need to go after. And the truth of the matter is today. And over time, you know, setting a cluster up on Kubernetes has largely been solved. Um, and yet the promise of, of Kubernetes around portability and flexibility, uh, downstream when you operationalize the complexity, smacks you in the face. And, uh, and that's where, where storm forge comes in. And so we're a vertical, you know, kind of vertically oriented solution. Um, that's, that's absolutely focused on solving that problem. >>Well, I don't want to play, actually. I want to play the, uh, devils advocate here and, you know, >>You wouldn't be a good analyst if you didn't. >>So the, the problem is when you talk with clients, users, they, there are many of them still working with Java with, you know, something that is really tough. Mm-hmm <affirmative>, I mean, we loved all of us loved Java. Yeah, absolutely. Maybe 20 years ago. Yeah. But not anymore, but still they have developers. They are porting applications, microservices. Yes. But not very optimized, etcetera. C cetera. So it's becoming tough. So how you can interact with these kind of yeah. Old hybrid or anyway, not well in generic applications. >>Yeah. We, we do that today. We actually, part of our platform is we offer performance testing in a lower environment and stage. And we like Matt was saying, we can use any metric that you care about and we can work with any configuration for that application. So the perfect example is Java, you know, you have to worry about your heap size, your garbage collection tuning. Um, and one of the things that really struck, struck me very early on about the storm forage product is because it is true machine learning. You remove the human bias from that. So like a lot of what I did in the past, especially around SRE and, and performance tuning, we were only as good as our humans were because of what they knew. And so we were, we kind of got stuck in these paths of making the same configuration adjustments, making the same changes to the application, hoping for different results. But then when you apply machine learning capability to that, the machine will recommend things you never would've dreamed of. And you get amazing results out of >>That. So both me and an Rico have been doing this for a long time. Like I have battled to my last breath, the, the argument when it's a bare metal or a VM. Yeah. Look, I cannot give you any more memory. Yeah. And the, the argument going all the way up to the CIO and the CIO basically saying, you know what, Keith you're cheap, my developer resources expensive, my bigger box. Yep. Uh, buying a bigger box in the cloud to your point is no longer a option because it's just expensive. Talk to me about the carrot or the stick as developers are realizing that they have to be more responsible. Where's the culture change coming from? So is it, that is that if it, is it the shift in responsibility? >>I think the center of the bullseye for us is within those sets of decisions, not in a static way, but in an ongoing way, especially, um, especially as the development of applications becomes more and more rapid. And the management of them, our, our charge and our belief wholeheartedly is that you shouldn't have to choose, you should not have to choose between costs or performance. You should not have to choose where your, you know, your applications live, uh, in a public private or, or hybrid cloud environment. And so we want to empower people to be able to sit in the middle of all of that chaos and for those trade-offs and those difficult interactions to no, no longer be a thing. You know, we're at, we're at a place now where we've done, you know, hundreds of deployments and never once have we met a developer who said, I'm really excited to get outta bed and come to work every day and manually tune my application. <laugh> One side, secondly, we've never met, uh, you know, uh, a manager or someone with budget that said, uh, please don't, you know, increase the value of my investment that I've made to lift and shift us over mm-hmm <affirmative>, you know, to the cloud or to Kubernetes or, or some combination of both. And so what we're seeing is the converging of these groups, um, at, you know, their happy place is the lack of needing to be able to, uh, make those trade offs. And that's been exciting for us. So, >>You know, I'm listening and looks like that your solution is right in the middle in application per performance management, observability. Yeah. And, uh, and monitoring. So it's a little bit of all of this. >>So we, we, we, we want to be, you know, the Intel inside of all of that, mm-hmm, <affirmative>, we don't, you know, we often get lumped into one of those categories. It used to be APM a lot. We sometimes get a, are you observability or, and we're really not any of those things in and of themselves, but we, instead of invested in deep integrations and partnerships with a lot of those, uh, with a lot of that tooling, cuz in a lot of ways, the, the tool chain is hardening, uh, in a cloud native and, and Kubernetes world. And so, you know, integrating in intelligently staying focused and great at what we solve for, but then seamlessly partnering and not requiring switching for, for our users who have already invested likely in a APM or observability. >>So to go a little bit deeper. Sure. What does it mean integration? I mean, do you provide data to this, you know, other applications in, in the environment or are they supporting you in the work that you >>Yeah, we're, we're a data consumer for the most part. Um, in fact, one of our big taglines is take your observability and turn it into actionability, right? Like how do you take the it's one thing to collect all of the data, but then how do you know what to do with it? Right. So to Matt's point, um, we integrate with folks like Datadog. Um, we integrate with Prometheus today. So we want to collect that telemetry data and then do something useful with it for you. >>But, but also we want Datadog customers. For example, we have a very close partnership with, with Datadog, so that in your existing data dog dashboard, now you have yeah. This, the storm for capability showing up in the same location. Yep. And so you don't have to switch out. >>So I was just gonna ask, is it a push pull? What is the developer experience? When you say you provide developer, this resolve ML, uh, learnings about performance mm-hmm <affirmative> how do they receive it? Like what, yeah, what's the, what's the, what's the developer experience >>They can receive it. So we have our own, we used to for a while we were CLI only like any good developer tool. Right. Uh, and you know, we have our own UI. And so it is a push in that, in, in a lot of cases where I can come to one spot, um, I've got my applications and every time I'm going to release or plan for a release or I have released, and I want to take, pull in, uh, observability data from a production standpoint, I can visualize all of that within the storm for UI and platform, make decisions. We allow you to, to set your, you know, kind of comfort level of automation that you're, you're okay with. You can be completely set and forget, or you can be somewhere along that spectrum. And you can say, as long as it's within, you know, these thresholds, go ahead and release the application or go ahead and apply the configuration. Um, but we also allow you to experience, uh, the same, a lot of the same functionality right now, you know, in Grafana in Datadog, uh, and a bunch of others that are coming. >>So I've talked to Tim Crawford who talks to a lot of CIOs and he's saying one of the biggest challenges, or if not, one of the biggest challenges CIOs are facing are resource constraints. Yeah. They cannot find the developers to begin with to get this feedback. How are you hoping to address this biggest pain point for CIOs? Yeah. >>Development? >>Just take that one. Yeah, absolutely. That's um, so like my background, like I said, at United health group, right. It's not always just about cost savings. In fact, um, the way that I look about at some of these tech challenges, especially when we talk about scalability, there's kind of three pillars that I consider, right? There's the tech scalability, how am I solving those challenges? There's the financial piece, cuz you can only throw money at a problem for so long. And it's the same thing with the human piece. I can only find so many bodies and right now that pool is very small. And so we are absolutely squarely in that footprint of, we enable your team to focus on the things that they matter, not manual tuning like Matt said. And then there are other resource constraints that I think that a lot of folks don't talk about too. >>Like we were, you were talking about private cloud for instance. And so having a physical data center, um, I've worked with physical data centers that companies I've worked for have owned where it is literally full wall to wall. You can't rack any more servers in it. And so their biggest option is, well, I could spend 1.2 billion to build a new one if I wanted to. Or if you had a capability to truly optimize your compute to what you needed and free up 30% of your capacity of that data center. So you can deploy additional name spaces into your cluster. Like that's a huge opportunity. >>So either out of question, I mean, may, maybe it, it doesn't sound very intelligent at this point, but so is it an ongoing process or is it something that you do at the very beginning mean you start deploying this. Yeah. And maybe as a service. Yep. Once in a year I say, okay, let's do it again and see if something changes. Sure. So one spot 1, 1, 1 single, you know? >>Yeah. Um, would you recommend somebody performance tests just once a year? >>Like, so that's my thing is, uh, previous at previous roles I had, uh, my role was you performance test, every single release. And that was at a minimum once a week. And if your thing did not get faster, you had to have an executive exception to get it into production. And that's the space that we wanna live in as well as part of your C I C D process. Like this should be continuous verification every time you deploy, we wanna make sure that we're recommending the perfect configuration for your application in the name space that you're deploying >>Into. And I would be as bold as to say that we believe that we can be a part of adding, actually adding a step in the C I C D process that's connected to optimization and that no application should be released monitored and sort of, uh, analyzed on an ongoing basis without optimization being a part of that. And again, not just from a cost perspective, yeah. Cost end performance, >>Almost a couple of hundred vendors on this floor. You know, you mentioned some of the big ones, data, dog, et cetera. But what happens when one of the up and comings out of nowhere, completely new data structure, some imaginable way to click to elementry data. Yeah. How do, how do you react to that? >>Yeah. To us it's zeros and ones. Yeah. Uh, and you know, we're, we're, we're really, we really are data agnostic from the standpoint of, um, we're not, we we're fortunate enough to, from the design of our algorithm standpoint, it doesn't get caught up on data structure issues. Um, you know, as long as you can capture it and make it available, uh, through, you know, one of a series of inputs, what one, one would be load or performance tests, uh, could be telemetry, could be observability if we have access to it. Um, honestly the messier, the, the better from time to time, uh, from a machine learning standpoint, um, it, it, it's pretty powerful to see we've, we've never had a deployment where we, uh, where we saved less than 30% while also improving performance by at least 10%. But the typical results for us are 40 to 60% savings and, you know, 30 to 40% improvement in performance. >>And what happens if the application is, I, I mean, yes, Kubernetes is the best thing of the world, but sometimes we have to, you know, external data sources or, or, you know, we have to connect with external services anyway. Mm-hmm <affirmative> yeah. So can you, you know, uh, can you provide an indication also on, on, on this particular application, like, you know, where the problem could >>Be? Yeah, yeah. And that, that's absolutely one of the things that we look at too, cuz it's um, especially when you talk about resource consumption, it's never a flat line, right? Like depending on your application, depending on the workloads that you're running, um, it varies from sometimes minute to minute, day to day, or it could be week to week even. Um, and so especially with some of the products that we have coming out with what we want to do, you know, partnering with, uh, you know, integrating heavily with the HPA and being able to handle some of those bumps and not necessarily bumps, but bursts and being able to do it in a way that's intelligent so that we can make sure that, like I said, it's the perfect configuration for the application regardless of the time of day that you're operating in or what your traffic patterns look like. Um, or you know, what your disc looks like, right? Like cuz with our, our low environment testing, any metric you throw at us, we can, we can optimize for. >>So Madden Patrick, thank you for stopping by. Yeah. Yes. We can go all day. Because day two is I think the biggest challenge right now. Yeah. Not just in Kubernetes, but application replatforming and re and transformation. Very, very difficult. Most CTOs and S that I talked to, this is the challenge space from Valencia Spain. I'm Keith Townsend, along with my host en Rico senior. And you're watching the queue, the leader in high tech coverage.

>>Hello, and welcome back to the live cube coverage here in San Francisco, California, the cube live coverage. Two days, day two of a summit 2022, a summit New York city coming up in the summer. We'll be there as well. Events are back. I'm the host, John fur, the cube got great guest here, Johnny Dallas with Ze. Um, here's on the cube. We're gonna talk about his background. Uh, little trivia here. He was the youngest engineer ever worked at Amazon at the age. 17 had to get escorted into reinvent in Vegas cause he was underage <laugh> with security, all good stories. Now the CEO of gonna called Ze know DevOps kind of focus, managed service, a lot of cool stuff, John, welcome to the cube. >>Thanks John. Great. >>So tell a story. You were the youngest engineer at AWS. >>I was, yes. So I used to work at a company called Bebo. I got started very young. I started working when I was about 14, um, kind of as a software engineer. And when I, uh, was about 16, I graduated out of high school early. Um, worked at this company, Bebo running all of the DevOps at that company. Um, I went to reinvent in about 2018 to give a talk about some of the DevOps software I wrote at that company. Um, but you know, as many of those things are probably familiar with reinvent happens in a casino and I was 16, so I was not able to actually go into the casino on my own <laugh> um, so I'd have <inaudible> security as well as C security escort me in to give my talk. >>Did Andy jazzy, was he aware of this? >>Um, you know, that's a great question. I don't know. <laugh> >>I'll ask him great story. So obviously you started a young age. I mean, it's so cool to see you jump right in. I mean, I mean, you never grew up with the old school that I used to grew up in loading package software, loading it onto the server, deploying it, plugging the cables in, I mean you just rocking and rolling with DevOps as you look back now what's the big generational shift because now you got the Z generation coming in, millennials are in the workforce. It's changing. Like no one's putting package software on servers. >>Yeah, no, I mean the tools keep getting better, right? We, we keep creating more abstractions that make it easier and easier. When I, when I started doing DevOps, I could go straight into E two APIs. I had APIs from the get go and you know, my background was, I was a software engineer. I never went through like the CIS admin stack. I, I never had to, like you said, rack servers, myself. I was immediately able to scale to I, I was managing, I think 2,500 concurrent servers across every Ables region through software. It was a fundamental shift. >>Did you know what an SRE was at that time? Uh, you were kind of an SRE on >>Yeah, I was basically our first SRE, um, familiar with the, with the phrasing, but really thought of myself as a software engineer who knows cloud APIs, not a SRE. >>All right. So let's talk about what's what's going on now, as you look at the landscape today, what's the coolest thing that's going on in your mind and cloud? >>Yeah, I think the, I think the coolest thing is, you know, we're seeing the next layer of those abstraction tools exist and that's what we're doing with Ze is we've basically gone and we've, we're building an app platform that deploys onto your cloud. So if you're familiar with something like Carku, um, where you just click a GitHub repo, uh, we actually make it that easy. You click a GI hub repo and it'll deploy on a AWS using Al AWS tools. >>So, right. So this is Z. This is the company. Yes. How old's the company >>About a year and a half old now. >>Right. So explain what it does. >>Yeah. So we make it really easy for any software engineer to deploy on a AWS. Um, that's not SREs. These are the actual application engineers doing the business logic. Mm-hmm <affirmative> they don't really want to think about Yamo. They don't really want to configure everything super deeply. Um, they want to say, run this API on a AWS in the best way possible. We've encoded all the best practices into software and we set it up for you. >>Yeah. So I think the problem you're solving is, is that there's a lot of want to be DevOps engineers. And then they realize, oh shit, I don't wanna do this. Yeah. And the people want to do it. They loved under the hood. Right. People love that infrastructure, but the average developer needs to actually be as agile on scale. So that seems to be the problem you solve. Right? Yeah. >>We, we, we give way more productivity to each individual engineer, you know? >>All right. So let me ask you a question. So let me just say, I'm a developer. Cool. I built this new app. It's a streaming app or whatever. I'm making it up cube here, but let's just say I deploy it. I need your service. But what happens about when my customers say, Hey, what's your SLA? The CDN went down from this it's flaky. Does Amazon have? So how do you handle all that SLA reporting that Amazon provides? Cause they do a good job with sock reports all through the console. But as you start getting into DevOps and sell your app, mm-hmm <affirmative> you have customer issues. You, how do you view that? Yeah, >>Well, I, I think you make a great point of AWS has all this stuff already. AWS has SLAs. AWS has contract. Aw, has a lot of the tools that are expected. Um, so we don't have to reinvent the wheel here. What we do is we help people get to those SLAs more easily. So, Hey, this is a AWS SLA as a default. Um, Hey, we'll configure your services. This is what you can expect here. Um, but we can really leverage AWS reli ability of you don't have to trust us. You have to trust S and trust that the setup is good there. >>Do you handle all the recovery or mitigation between, uh, identification say downtime for instance, oh, the servers not 99% downtime, uh, went down for an hour, say something's going on? And is there a service dashboard? How does it get what's the remedy? Do you have, how does all that work? >>Yeah, so we have some built in remediation. You know, we, we basically say we're gonna do as much as we can to keep your endpoint up 24 7 mm-hmm <affirmative>. If it's something in our control, we'll do it. If it's a disc failure, that's on us. If you push bad code, we won't put out that new version until it's working. Um, so we do a lot to make sure that your endpoint stays up, um, and then alert you if there's a problem that we can't fix. So cool. Hey, S has some downtime, this thing's going on. You need to do this action. Um, we'll let you know. >>All right. So what do you do for fun? >>Yeah, so, uh, for, for fun, um, a lot of side projects. <laugh>, uh, >>What's your side hustle right now. You got going on >>The, uh, it's a lot of schools playing >>With serverless. >>Yeah. Playing with a lot of serverless stuff. Um, I think there's a lot of really cool Lam stuff as well, going on right now. Um, I love tools is, is the truest answer is I love building something that I can give to somebody else. And they're suddenly twice as productive because of it. Um, >>That's a good feeling, isn't it? Oh >>Yeah. There's nothing >>Like that. Tools versus platforms. Mm-hmm, <affirmative>, you know, the expression, too many tools in the tool, she becomes, you know, tools for all. And then ultimately tools become platforms. What's your view on that? Because if a good tool works and starts to get traction, you need to either add more tools or start building a platform platform versus tool. What's your, what's your view on our reaction to that kind of concept debate? >>Yeah, it's a good question. Uh, we we've basically started as like a, a platform. First of we've really focused on these, uh, developers who don't wanna get deep into the DevOps. And so we've done all of the piece of the stacks. We do C I C D management. We do container orchestration, we do monitoring. Um, and now we're, spliting those up into individual tools so they can be used awesome in conjunction more. >>Right. So what are some of the use cases that you see for your service? It's DevOps basically nano service DevOps for people on a DevOps team. Do clients have a DevOps person and then one person, two people what's the requirements to run >>Z? Yeah. So we we've got teams, um, from no DevOps is kind of when they start and then we've had teams grow up to about, uh, five, 10 man DevOps teams. Mm-hmm <affirmative> um, so, you know, as more structured people come in, because we're in your cloud, you're able to go in and configure it on top you're we can't block you. Uh, you wanna use some new AOL service. You're welcome to use that alongside the stack that we deploy for >>You. How many customers do you have now? >>So we've got about 40 companies that are using us for all of their infrastructure, um, kind of across the board, um, as well as >>What's the pricing model. >>Uh, so our pricing model is we, we charge basically similar to an engineer salary. So we charge, uh, a monthly rate. We have plans at 300 bucks a month, a thousand bucks a month, and then enterprise plan for based >>On the requirement scale. Yeah. You know, so back into the people cost, you must offer her discounts, not a fully loaded thing, is it? >>Yeah. There's a discounts kind of at scale, >>Then you pass through the Amazon bill. >>Yeah. So our customers actually pay for the Amazon bill themselves. Oh. So >>They have their own >>Account. There's no margin on top. You're linking your Aless account in, um, it, which is huge because we can, we are now able to help our customers get better deals with Amazon. Um, got it. We're incentivized on their team to drive your cost down. >>And what's your unit main unit of economics software scale. >>Yeah. Um, yeah, so we, we think of things as projects. How many services do you have to deploy as that scales up? Um, awesome. >>All right. You're 20 years old now you not even can't even drink legally. <laugh> what are you gonna do when you're 30? We're gonna be there. >>Well, we're, uh, we're making it better. And >>The better, the old guy on the cube here. >><laugh> I think, uh, I think we're seeing a big shift of, um, you know, we've got these major clouds. AWS is obviously the biggest cloud. Um, and it's constantly coming out with new services. Yeah. But we're starting to see other clouds have built many of the common services. So Kubernetes is a great example. It exists across all the clouds. Um, and we're starting to see new platforms come up on top that allow you to leverage tools from multiple clouds. At the same time. Many of our customers actually have AWS as their primary cloud and they'll have secondary clouds or they'll pull features from other clouds into AWS, um, through our software. I think that I'm very excited by that. And I, uh, expect to be working on that when I'm 30. Awesome. >>Well, you gonna have a good future. I gotta ask you this question cuz uh, you know, I've always, I was a computer science undergraduate in the, in the eighties and um, computer science back then was hardcore, mostly systems OS stuff, uh, database compiler. Um, now there's so much compi, right? So mm-hmm <affirmative> how do you look at the high school college curriculum experience slash folks who are nerding out on computer science? It's not one or two things much. You've got a lot of, a lot of things. I mean, look at Python, data engineering, merging as a huge skill. What's it? What's it like for college kids now and high school kids? What, what do you think they should be doing if you had to give advice to your 16 year old self back a few years ago now in college? Um, I mean Python's not a great language, but it's super effective for coding and the data's really relevant, but it's you got other language opportunities, you got tools to build. So you got a whole culture of young builders out there. What should, what should people gravitate to in your opinion stay away from yeah. Or >>Stay away from that's a good question. I, I think that first of all, you're very right of the, the amount of developers is increasing so quickly. Um, and so we see more specialization. That's why we also see, you know, these SREs that are different than typical application engineering. You get more specialization in job roles. Um, I think if, what I'd say to my 16 year old self is do projects, um, the, I learned most of my, what I've learned just on the job or online trying things, playing with different technologies, actually getting stuff out into the world, um, way more useful than what you'll learn in kind of a college classroom. I think classrooms great to, uh, get a basis, but you need to go out and experiment actually try things. >>You know? I think that's great advice. In fact, I would just say from my experience of doing all the hard stuff and cloud is so great for just saying, okay, I'm done, I'm abandoning the project. Move on. Yeah. Because you know, it's not gonna work in the old days. You have to build this data center. I bought all this certain, you know, people hang on to the old, you know, project and try to force it out there. >>You can launch a project, >>Can see gratification, it ain't working <laugh> or this is shut it down and then move on to something new. >>Yeah, exactly. Instantly you should be able to do that much more quickly. Right. >>So you're saying get those projects and don't be afraid to shut it down. Mm-hmm <affirmative> that? Do you agree with that? >>Yeah. I think it's ex experiment. Um, you're probably not gonna hit it rich on the first one. It's probably not gonna be that idea is DJing me this idea. So don't be afraid to get rid of things and just try over and over again. It's it's number of reps that a win. >>I was commenting online. Elon Musk was gonna buy Twitter, that whole Twitter thing. And, and, and someone said, Hey, you know, what's the, I go look at the product group at Twitter's been so messed up because they actually did get it right on the first time <laugh> and, and became such a great product. They could never change it because people would freak out and the utility of Twitter. I mean, they gotta add some things, the added button and we all know what they need to add, but the product, it was just like this internal dysfunction, the product team, what are we gonna work on? Don't change the product so that you kind of have there's opportunities out there where you might get the lucky strike, right. Outta the gate. Yeah. Right. You don't know, >>It's almost a curse too. It's you're not gonna Twitter. You're not gonna hit a rich second time too. So yeah. >><laugh> Johnny Dallas. Thanks for coming on the cube. Really appreciate it. Give a plug for your company. Um, take a minute to explain what you're working on, what you're looking for. You're hiring funding. Customers. Just give a plug, uh, last minute and have the last word. >>Yeah. So, um, John Dallas from Ze, if you, uh, need any help with your DevOps, if you're a early startup, you don't have DevOps team, um, or you're trying to deploy across clouds, check us out ze.com. Um, we are actively hiring. So if you are a software engineer excited about tools and cloud, or you're interested in helping getting this message out there, hit me up. Um, find a Z. >>Yeah. LinkedIn Twitter handle GitHub handle. >>Yeah. I'm the only Johnny on a LinkedIn and GitHub and underscore Johnny Dallas underscore on Twitter. Right? Um, >>Johnny Dallas, the youngest engineer working at Amazon. Um, now 20 we're on great new project here. The cube builders are all young. They're growing in to the business. They got cloud at their, at their back it's, uh, tailwind. I wish I was 20. Again, this is a cue. I'm John for your host. Thanks for watching. >>Thanks.

>>Hey everyone. Welcome to this cube conversation featuring next law. I'm your host, Lisa Martin. And today we are joined by Sukkot Sarab CEO and founder of next, next LA Sukkot. Great to have you on the program, >>Lisa, thank you so much for having me here really excited about this. >>Tell us a little bit about next level. What is it that you guys do? >>Yeah. Um, you know, we are in the world of data and one of the biggest challenges that we face, um, as an industry is there is so much data, so much variety. How do we really get it into the hands of people who use data? And, um, the users of data are all across. You know, they shouldn't have to be engineers. They are across the board in different functions. So next, last purpose and mission has always been ready to use data in the hands of the users. So, um, what misled us today is makes it possible for users across the board, whether those are data scientists, whether those are data analysts, whether those are people in various business functions to get the data that they need in the tools that they work with. So, um, we make that possible in a very, no code way, um, for users to get access to the data. Um, and very uniquely actually do that by automating a lot of the data engineering process. We'll talk more about that, but it's an exciting space to be in. >>It is an exciting space to be in. And of course that the volumes of data could just continue to explode and there's, that will not be slowing down anytime. Soon, as we know in businesses, one of the things we saw in the last two years was businesses pivoting so many times and really needing, going from survival mode to thriving mode, but the ability to harness the power and the insights and data is critical for businesses to be successful these days as consumers. We just expect that if it's our business life or personal life, whoever we're interacting with is going to know what we want, and we're going to be able to display that to us quickly. We think about data match. It's a relatively new concept, right? Talk to me about data mesh and what differentiates next left from your competition. >>Yeah. Um, so data mesh is essentially, I would say in the lineage of the concept of democratizing data, the idea has always been that data should get to the users. Now for a long time, these users were dependent on it and engineering to get the data to them. So what the data mesh is doing is it's bringing a framework by which users of data. We call them data, the domains, the different functions that use data. They can have the data to use themselves. They can manage things on their own. And I think that is allowing for a framework in which teams can truly scale. I mean, that bottleneck of depending on engineering to do everything for you is just not going work. And I think in the last two years, even more so we saw that as companies tried to move fast, it started to break down. And I think there is a lot of momentum around this concept of data mesh. For this reason, people are finding that this concept is what can help them scale >>And how does next SLA deliver that single tool so that you can really democratize data and give people with varying levels of technical fluency, the access that they need. I can imagine finance folks with ERP data marketing folks with CRM data. How do you do that with a single tool? >>Yeah. So, um, I think the key thing about getting data in the hands of users, as we think about data democratization has been that, how does it actually happen? How do you give people access to the data? You know, simply giving them passwords to systems is not enough right now the data mesh concept comes with the understanding that there should be an entity, which we call it a data, product data, you know, a data product becomes that sort of common entity that becomes something that people can get access to. They can use, they can collaborate on. Now, what is a data product becomes an important question, of course, and how do we get a data product? So our next step comes in in a very key way is we automatically generate these data products. So again, going back to the thinking that look there, there is not going to be enough engineers to write code for everything. What we are able to do is to say that we can actually, you know, connect to data systems, look to the data, understand it, and package it up as a product, as a data product. And that data product is a core element of the damage. I'm happy to share what a data product is, if it helps people understand and of, >>Yeah. Let's double click into that a little bit. I was noticing on your website about next sets and I wanted to know what that is and how does it reimagine data, product creation. >>Yeah. Um, so let me just break down a little bit about what is the data product in the first place, right. I mean, as consumers, we use products all the time, you know, I'm, I'm, my laptop is here on our desk and that is a product. It is a product made from raw materials, like wood and metal and screws, right. And somebody designed the product, somebody built it and I'm using it. So if we think of the same parallel in the world of data, then API APIs and files and database tables, those are the raw materials. Um, if somebody takes that and packages that up into something that other people can use easily, that is the concept of the data product. Now, what, how is it different from data? Well, you take the core data and you put things around it. Like, what is the distribution of data? >>What is the structure of it? You know, what are the validations that make it work, how to better manage that, who has access to it when you take that raw material and put all of those other structures, it that's when it sort of becomes a data product. And the next step concept in next door is essentially a manifestation of that. It is the concept that these data products do not need to be new copies of data, which is a huge pain by the way. But instead they can be these logical entities. So if I can take us back to the world of compute, where we understand the concept of containers, no, these containers are basically a logical NPP that gives us access to the computation resources. Think of next set as a very similar thing, a logical entity that gives us access to the data resources. And, um, this is something that, you know, we have been able to innovate and automate in such a way that today, when people think of the data mesh and they want to build that, they see us as a component in that whole framework. So data mesh is a much broader framework, but we are sort of the building block for that, through this concept >>Building block. Got it. Talk to me about where your customer conversations are happening. Are they within chief data officer chief information officer? Is it within the C-suite as data is every company these days has to be a data company. >>Oh yeah. Very much in the C-suite. Right. So again, this changes a little bit industry by industry because every industry is organized differently. Um, for example, you know, we have some amazing customer international services there. The conversation often is this the chief data officer or the chief analytics officer. And the key thing that the C suite is thinking is how does this work in the future? How do you know the scale of data challenges are, is the growth of that is so fast. How do we handle things to three years, five years from now? And that's where the strategic conversation is. And that's where things like data mesh become extremely important for companies where we talked to them about, you know, how our technology sort of enables that, right? Um, across other industries, the functions may vary. And one of the things which is very interesting with data, um, compared to other technologies is that it touches almost every aspect of business. It's not limited to engineering. It is your person in HR who is doing HR analytics, source candidates, and profiles are reviewing and all that stuff to finance, to operations, every aspect of business does touch data. So this has to be done in a language and a mechanism that's much more approachable. >>It's gotta be horizontal for all of those different types of users, right. To be able to understand so that ultimately not only did they get access to the data, but they can pull out those insights faster than their competition, whether it's to develop new revenue, streams, new products, new services, you know, the, the person on the other end or the companies on the other end are expecting that real-time interaction. >>Yeah. Yeah. But that's >>No longer a nice to have >>No longer likes to have. And to clarify, right. I mean, the use of data is in multiple ways, right? So analytics is a big use of data, which is how is my business doing and running. Um, we have customers like, um, you know, um, Marchex and Poshmark and bed bath beyond and so on. We'll use us heavily to bring data for the analytics use cases as our companies, for example, like a door dash or Instacart, but that data feeds operational purposes, operational purposes, meaning, understanding the availability of inventory or products across different stores. Not that data has functioned to say, well, if I know what products are available, then I can list them. Then I can go pick them up. And that's not a analytics use case alone. It has a, um, you know, it has an operational use case, right? Um, similarly we see that in audit tracking, we have customers, for example, like Narvar that use us to connect to different shipping tracking system. So the applications of data are in analytics. There are certainly also in operations, which is core business. And they're also, of course, in data science. There's no question that the extension of analytics from looking back on how business is doing to data science, which is, you know, what should we be doing and how should we be more intelligent? So it's across the board, >>Across the board, horizontal, all industries really need to do this, but one of the things that pop into my mind as you were walking through that example was the supply chain challenges that we're all experiencing right now. How can next help organizations mitigate some of the challenges that are going on? >>I think what happens is, you know, technologies like ours, which are the data layer are at a fundamental foundational level. One of the things about next slide is that we are able to bring a data into a much more real time usage. So where in companies, but traditionally moving data on a much more sort of periodic basis. We are our plumbing under the hood. We are completely in real time, which means that we are allowing companies to now get access to that data in a faster way where possible. So again, this is not something that can be fixed overnight, but the role that data can play in is better visibility. Um, and better visibility means those business decisions are being made earlier at the right time. It's more insight. And hopefully that eventually leads to sort of, um, much more efficient, actual on the ground, some movement of products and so on. >>Yeah. That visibility is absolutely critical regardless of the global climate. Right. Talk to me last question here, since we're almost out of time, give me a little bit about your AWS partnership and then talk to me about what's next for next time. >>Um, you know, as, as a technology provider, we ended up, um, running a lot of our own infrastructure on AWS as do many of our customers. And, uh, we have been an AWS partner for multiple years, but very decently, we actually made our product available on AWS marketplace, which means that the access to our technology has become so much easier for companies. Now, uh, next law has started its journey focusing on mid to large enterprises and some of the most complex use cases out there from some of the biggest banks to some of the biggest companies in marketing to some of the core companies in retail, logistics and so on. Now what is happening is that the powerful nature of our product and the ease of use that we have given that need is coming further and further earlier in the life cycle of companies, right? So today new companies are starting up, which said, which are saying that we need to make that sort of investment in data infrastructure earlier. You know, and that's why we have seen even some very small, early startups starting to use next level to come to us for our technology. So we are very much partnered up with AWS because AWS covers the whole gamut from companies that were started yesterday to extremely large enterprises, um, and bring our technology accessible to them. >>Excellent. Well, thank you so much for joining me. It sounds like a tremendous amount of momentum and opportunity at Nexa. We appreciate your insights and best of luck to you. We look forward to hearing more. >>Thank you, Lisa. It's a pleasure talking to it's an exciting space. So time flies, when we talked about that, >>Doesn't it, it really does for sockets sound room. I'm Lisa Martin. You're watching the queue, leave it here for more coverage and a leader in live tech hybrid events.

(soft music) >> There are many constants in the storage business, relentlessly declining cost per bit, innovations that perpetually battled the laws of physics, a seemingly endless flow of venture capital, despite the intense competition. And there's one other constant in the storage business, Eric Hertzog, and he joins us today in this CUBE video exclusive to talk about IBM's recent storage announcements. Eric, welcome back to theCUBE. >> Great, Dave, thanks very much, we love being on theCUBE and you guys do a great job of informing the industry about what's going on in storage and IT in general. >> Well, thank you for that. >> Great job. >> We're going to cover a lot of ground today. IBM Storage, made a number of announcements the past month around data resilience, a new as-a-service model, which a lot of folks are doing in the industry, you've made performance enhancements. Can you give us the top line summary of the hard news, Eric? >> Sure, the top line summary is of course cyber security is on top of mind for everybody in the recent Fortune 500 list that came out, you probably saw, there was a survey of CEOs of Fortune 500 companies, they named cybersecurity as their number one concern, not war, not pandemic, but cybersecurity. So we've got an announcement around data resilience and cyber resiliency built on our FlashSystem family with our new offering, Safeguarded Copy. And the second thing is the move to a new method of storage consumption. Storage-as-a-Service, a pay-as-you-go model, cloud-like the way people buy cloud storage, that's what you can do now from IBM Storage with our Storage-as-a-Service. Those are the key, two takeaways, Dave. >> Yeah and I want to stay on the trends that we're seeing in cyber for a moment, the work from home pivot in the hybrid work approach has really created a new exposures, people aren't as secure outside of the walled garden of the offices and we've seen a dramatic escalation in the adversaries capabilities and techniques, another least of which is island hopping, in other words, putting code fragments in the digital supply chain, they reform once they're inside the company and it's almost like this organic creepy thing that occurs. They're also living as you know, stealthily for many, many months, sometimes years, exfiltrating data, and then just waiting and then when companies respond, the incidents response trigger a ransomware incident. So they escalate the cyber crime and it's just a really, really bad situation for victims. What are you seeing in that regard and the trends? >> Well, one of the key things we see as everyone is very concerned about cybersecurity. The Biden administration has issued (indistinct) not only to the government sector, but to the private sector, cyber security is a big issue. Other governments across the world have done the same thing. So at IBM Storage, what we see is taking a comprehensive view. Many people think that cybersecurity is moat with the alligators, the castle wall and then of course the sheriff of Nottingham to catch the bad guys. And we know the sheriff of Nottingham doesn't do a good job of catching Robin Hood. So it takes a while as you just pointed out, sitting there for months or even longer. So one of the key things you need to do in an overall cybersecurity strategy is don't forget storage. Now our announcement around Safeguarded Copy is very much about rapid recovery after an attack for malware or ransomware. We have a much broader set of cyber security technology inside of IBM Storage. For example, with our FlashSystem family, we can encrypt data at rest with no performance penalty. So if someone steals that data, guess what? It's encrypted. We can do anomalous pattern detection with our backup product, Spectrum Protect Plus, why would you care? Well, if theCUBE's backup was taking two hours on particular datasets and all of a sudden it was taking four hours, Hmm maybe someone is encrypting those backup data sets. And so we notify. So what we believe at IBM is that an overarching cybersecurity strategy has to keep the bad guys out, threat detection, anomalous pattern behavior on the network, on the servers, on the storage and all of that, chasing the bad guy down once they breach the wall, 'cause that does happen, but if you don't have cyber and data resilience built into your storage technology, you are leaving a gap that the bad guys can explain, whether that be the malware ransomware guys oh by the way, Dave, there still is internal IT theft that there was a case about 10 years ago now where 10 IT guys stole $175 million. I kid you not, $175 million from a bunch of large banks across the country, and that was an internal IT theft. So between the internal IT issues that could approach you malware and ransomware, a comprehensive cybersecurity strategy, must include storage. >> So I want to ask you about come back to Safeguarded Copy and you mentioned some features and capabilities, encrypting data at rest, your anomalous pattern recognition inferring, you're taking a holistic approach, but of course you've got a storage centricity, what's different about your cyber solution? What's your unique value probability to your (indistinct) . >> Well, when you look at Safeguarded Copy, what it does is it creates immutable copies that are logically air-gapped, but logically air-gapped locally. So what that means is if you have a malware or ransomware attack and you need to do a recovery, whether it be a surgical recovery or a full-on recovery, because they attacked everything, then we can do recovery in a couple hours versus a couple of days or a couple of weeks. Now, in addition to the logical local air-gapping with Safeguarded Copy, you also could do remote logical air-gapping by snapping out to the cloud, which we also have on our FlashSystem products and you also of course, could take our FlashSystem products and back up to tape, giving you a physical air gap. In short, we give our customers three different ways to help with malware and ransomware. >> Let me ask you- >> Are air-gapped locally. >> Yeah, please continue, I'm sorry. >> So our air-gapping locally for rapid recovery, air-gapping remotely, which again, then puts it on the cloud provider network, so hopefully they can't breach that. And then clearly a physical air gap going out to tape all three and on the mainframe, we have Safeguarded Copy already, Dave and several of our mainframe customers actually do two of those things, they'll do Safeguarded Copy or rapid recovery locally, but they'll also take that Safeguarded Copy and either put it out to tape or put it out to a cloud provider with a remote logical air-gap using a snapshot. >> I want to ask you a question about management 'cause when you ask CSOs, what's your number one challenge, they'll say lack of talent, We've got all these tools and all this lack of skills to really do all this stuff. Can't hire people fast enough and they don't have the skills. So when you think about it, and so what you do is you bring a lot of automation into the orchestration and management. My question is this, when you set up air gaps, do you recommend, or what do you see in terms of not, of logically and physically not only physically separating the data, but also the management and orchestration and automation does that have to be logically air-gapped as well or can you use the same management system? What's best practice there? >> Ah, so what we do is we work with our copy management software, which will manage regular copies as well, but Safeguarded Copies are immutable. You can't write to them, you can't get rid of them and they're logically air-gapped from the local hosts. So the hosts, for the Safeguarded Copies that immutable copy, you just made, the hosts don't even know that it's there. So you manage that with our copy management software, which by the way, we'll manage regular snapshots and replicas as well, but what that allows you to do is allows you to automate, for example, you can automate recovery across multiple FlashSystem arrays, the copy services manager will allow you to set different parameters for different Safeguarded Copies. So a certain Safeguarded Copy, you could say, make me a copy every four hours. And then on another volume on a different data set, you could say, make me a copy every 12 hours. Once you set all that stuff update, it's completely automated, completely automated. >> So, I want to come back to something you mentioned about anomalous pattern recognition and how you help with threat detection. So a couple of a couple of quick multi-part question here. First of all, the backup corpus is an obvious target. So that's an area that you have to protect. And so can, and you're saying, you've used the example if your backups taking too long, but so how do you do that? What's the technology behind that? And then can you go beyond, should you go beyond just the backup corpus, with primary data or copies on-prem, et cetera? Two part questions. >> So when we look at it, the anomalous pattern detection is part of our backup software, say Spectrum Protect and what it does it uses AI-based technology, it recognizes a pattern. So it knows that the backup dataset for the queue takes two hours and it recognizes that, and it sees that as the normal state of events. So if all of a sudden that backup that theCUBE was doing used to take two hours and starts taking four, what it does is that's an anomalous pattern, it's not a normal pattern. It'll send a note to the backup admin, the storage admin, whoever you designate it to and say the backup data set for theCUBE that used to take two hours, it's taken four hours, you probably ought to check that. So when we view cyber resiliency from a storage perspective, it's broad. We just talked about anomalous pattern detection in Spectrum Protect. We were talking most of the conversation about our Safeguarded Copy, which is available on the mainframe for several years and is now available on FlashSystems, making immutable local air-gap copies, that can be rapidly recovered and are immutable and can help you recover for a malware or ransomware attack. Our data at rest encryption happens to be with no performance penalty. So when you look at it, you need to create an overarching strategy for cybersecurity and then when you look at your storage estate, you need to look at your secondary storage, backup, replicas, snaps, archive, and have a strategy there to protect that and then you need a strategy to protect your primary storage, which would be things like Safeguarded Copy and encryption. So then you put it all together and in fact, Dave, one of the things we offer is a free cyber resilience assessment. It's not only for IBM Storage, but it happens to be a cyber resilience assessment that conforms to the NIST Framework and it's heterogeneous. So if you're a big company, you've got IBM EMC and HP Storage, guess what? It's all about the data sets not about the storage. So we say, you said these 10 data sets are critical, why are you not encrypting them? These data sets are XYZ, why are you not air-gapping them? So we come up based on the NIST Framework, a set of recommendations that are not IBM specific, but they are storage specific. Here's how you make your storage more resilient, both your secondary storage and your primary storage. That's how we see the big thing and Safeguarded Copy of course fits in on the primary storage side, A on the mainframe, which we've had for several years now and B in the Linux world, the Unix world and the Windows Server world on our FlashSystem portfolio with the announcement we did on July 20th. >> Great, thank you for painting that picture. Eric, are you seeing any use case patterns emerge in this space? >> Well, we see a couple of things. First of all, is A most resellers and most end-users, don't see storage an overarching part of the cybersecurity strategy, and that's starting to change. Second thing we're seeing is more and more storage companies are trying to get into this bailiwick of offering cyber and data resilience. The value IBM brings of course is much longer experience to that and we even integrate with other products. So for example, IBM offers a product called QRadar from the security divisions not a storage product, a security product, and it helps you with early data breach recognition. So it looks at servers, network access, it looks at the storage and it actually integrates now with our Safeguarded Copy. So, part of the value that we bring is this overarching strategy of a comprehensive data and cyber resilience across our whole portfolio, including Safeguarded Copy our July 20th announcement. But also integration beyond storage now with our QRadar product from IBM security division. And there will be future announcements coming in both Q4 and Q1 of additional integration with other security technologies, so you can see how storage can be a vital COD in the corporate cybersecurity strategy. >> Got it, thank you. Let's pivot to the, as-a-service it's, cloud obviously is brought in that as-a-service. Now, it seems like everybody has one now. You guys have announced obviously HPE, Dell, Lenovo, Cisco, Pure, everybody's gotten out there as-a-service model, what do we need to know about your as-a-service solution and why is it different from the others? >> Sure. Well, one of the big differences is we actually go on actual storage, not effective. So when you look at effective storage, which most of them do that includes creating the (indistinct) data sets and other things, so you're basically paying for that. Second thing we do is we have a bigger margin. So for example, if theCUBE says we want SLA-3 and we sell it by the SLA, Dave, SLA-1, two and three. So let's say theCUBE needs SLA-3 and the minimum capacity is a 100 terabytes, but let's say you think you need 300 terabytes. No problem. You also have a variable. One of the key differences is unlike many of our competitors, the rate for the base and the rate for the variable are identical. Several of our competitors, when you're in the base, you pay a certain amount, when you go into the variable, they charge you a premium. The other key differentiator is around data reduction. Some of our competitors and all storage companies have data reduction technology. Block-level D do thin provisioning, compression, we all offer those features. The difference is with IBM's pay-as-you-grow, Storage-as-a-Service model, if you have certain data sets that are not very deducible, not very compressible, we absorbed that with our competitors, most of them, if the dataset is not easily deducible, compressible, and they don't see the value, they actually charge you a premium for that. So that is a huge difference. And then the last big difference is our a 100% availability guarantee. We have that on our FlashSystem product line, we're the only one offering 100% availability guarantee. We also against many of the competitors offer a better base nines, as you know, availability characteristics. We offer six nines of availability, which is five minutes and 26 seconds of downtime and a 100% availability of offering. Some of our competitors only offer four nines of availability and if you want five or six, they charge you extra. We give you six nines base in which has only five minutes and change of downtime in a year. So those are the key difference between us and the other as-a-service models out there. >> So, the basic concept I think, is if you commit to more and buy more, you pay less per. I mean, that's the basic philosophy of these things, right? So, if- >> Yes. >> I commit to you X, let's say, I want to just sort of start small and I commit to you to X and great. I'm in now in, maybe I sign up for a multi-year term, I commit this much, whatever, a 100 terabytes or whatever the minimum is. And then I can say, Hey, you know what? This is working for me. The CFO likes it and the IT guys can provision more seamlessly, we got our chargeback or showback model goes, I want to now make a bigger commitment and I can, and I want to sort of, can I break my three-year term and come back and then renegotiate, kind of like reserved instances, maybe bigger and pay less? How do you approach that? >> Well, what you do is we do a couple of things. First of all, you could always add additional capacity, and you just call up. We assign a technical account manager to every account. So in addition to what you get from the regular sales team and what you get from our value business partners, by the way, we did factor in the business partners, Dave, into this, so business partners will have a great pay-as-you-go Storage-as-a-Service solution, that includes partners and their ability to leverage. In fact, several of our partners that do have both MSP and MHP businesses are working right now to leverage our Storage-as-a-Service, and then add on their own value with their own MSP and MHP capability. >> And they can white label that? Is that right or? >> Well, you'd still have Storage-as-a-Service from IBM. They would resell that to theCUBE and then they'd add in their own MHP or MSP. >> Got it. >> That said partners interested in doing a white label, we would certainly entertain that capability. >> Got it. I interrupted you, carry on please. >> Yeah, you can go ahead and add more capacity, not a problem. You also can change the SLA. So theCUBE, one of the leading an industry analyst firms, you bought every analyst firm in the world, and you're using IBM Storage-as-a-Service, pay-as-you-go cloud-like model. So what you do is you call up the technical account manager and say, Eric, we bought all these other companies they're using on-prem storage, we'd like to move to Storage-as-a-Service for all the companies we acquire. We can do that, so that would up your capacity. And then you could say, now we've been at SLA-2, but because we're adding all these new applications of workloads from our acquired companies, we want some of it to be at SLA-1. So we can have some of your workloads on SLA-2, others on SLA-1, you could switch everything to SLA-1, and you just call your technical account manager and they'll make that happen for you or your business partner, obviously, if you bought through the channel. >> I get it, the hard question is what if all those other companies theCUBE acquired are also IBM Storage-as-a-Service customers? Can I, what's that discussion like? Hey, can I consolidate those and get a better deal? >> Yeah, there are all Storage-as-a-Service customers and Dave I love that thought, we would just figure out a way to consolidate the agreement. The agreements are one through five years. What I think also that's very unique is let's say for whatever reason, and we all love finance people. Let's say the IT guys have called the finance and say, we did a one-year contract, we now like to do a three-year contract. The one year is coming up and guess what? Finance's delayed for whatever reason, the PO doesn't go through. So the ITI calls up the technical account manager, we love your service, it's delayed in finance. We will let them stay on their Storage-as-a-Service, even though they don't have a contract. Now, of course they've told us they want to do one, but if they exceed the contract by a quarter or two, because they can't get the finance guys are messing with the IT guys, that's fine. What the key differentiators? Exactly the same price. Several of our competitors will also extend without a contract, but until you do a contract, they charge you a premium, we do not, whatever, if you're an SLA-3, you're SLA-3, we'll extend you and no big deal. And then you do your contract, when the finance guys get their act together and you're ready to go. So that is something we can do and we'll do on a continual basis. >> Last question. Let's go way out. So, we're not doing any time, near-term forecasts, I'm trying to understand how popular you think as-a-service is going to be. I mean, if you think about the end of the decade, let's think industry total, IBM specific, how popular do you think as-a-service models will be? Do you think it will be the majority of the transacted business or it's kind of more of a, just one of many? >> So I think there will be many, some people will still have bare metal on-premises. Some people will still do virtualization on-premises or in a hybrid cloud configuration. What I do think though is Storage-as-a-Service will be over 50% by the end. Remember, we're sitting at 2021. So we're talking now 2029. >> Right. >> So I think Storage-as-a-Service will be over 50%. I think most of that Storage-as-a-Service will be in a hybrid cloud model. I think the days of a 100% cloud, which is the way it started. I think a lot of people realize that a 100% cloud actually is more expensive than a hybrid cloud or fully on-prem. I was at a major university in New York, they are in the healthcare space and I know their CIO from one of my past lives. I was talking to him, they did a full on analysis of all the cloud providers going a 100% cloud. And their analysis showed that a 100% cloud, particularly for highly transactional workloads was 50% more expensive than buying it, paying the maintenance and paying their employees. So we did an all in view. So what I think it's going to be is Storage-as-a-Service will be over 50%. I think most of that Storage-as-a-Service will be in a hybrid cloud configuration with storage on-prem or in a colo, like what our IBM pay-as-you-go service will do and then it will be accessed and available through a hybrid cloud configuration with IBM Cloud, Google, Amazon as or whoever the cloud provider is. So I do think that you're looking at over 50% of the storage being as-a-service, but I do think the bulk of that as-a-service will be as-a-service through someone like IBM or our competitors and then part of it will be from the cloud providers. But I do think you're going to see a mix because right now the expense of going a 100% cloud cloud storage is dramatically understated and when someone does an analysis like that major university in New York did, they had a guy from finance, help them do the analysis and it was 50% more expensive than doing on-premise either on-prem or on-prem as-a-service, both were way cheaper. >> But you own the asset, right? >> Yes. >> As-a-service model. >> We, right, we own the asset. >> And I would bet, >> I would bet that over the lifetime value of the spend and it as-a-service model, just like the cloud, if you do this with IBM or any of your competitors, I would bet that overall you're going to spend more just like you've seen in the cloud, but you get the benefit is the flexibility that you get. >> Yeah, yeah. If you compare it to the, so obviously the number one model would be to buy. That's probably going to be the least expensive. >> Right. >> But it's also the least flexible. Then you also have leasing, more flexibility, but leasing usually is more expensive. Just like when you lease your car, if you add up all the lease payments and then you, at the end, pay that balloon payment to buy, it's cheaper to buy the car up front than it is to lease a car. Same thing with any IT asset, now storage network servers, all are available on leasing, the net is at the bottom line, that's more than buying it upfront. And then Storage-as-a-Service will also be more expensive than buying it, my friend, but ultimate capability, altering SLAs, adding new capacity, being able to handle an app very quickly. We can provision the storage, as you mentioned, the IT guys can easily provision. We provision, the storage in 10 minutes, if you bought from IBM Storage or any competitor you bought and you need more storage, A you got to put a PO through your system and if you're not theCUBE, but you're a giant global Fortune 500, sometimes it takes weeks to get the PO done. Then the PO has to go to the business partner, the business partner has got to give a PO to the distributor and a PO to IBM. So it can take you weeks to actually get the additional storage that you need. With Storage-as-a-Service from IBM with our pay-as-you-go, cloud-like model, all you have to do is provision and you're done. And by the way, we provide a 50% overage for free. So if they end up needing more storage, that 50% is actually sitting on-prem already and if they get to 75% utilization of the total amount of storage, we then call them up, the technical account manager would call them up and their business partner and say, Dave, do you know that you guys are at 75% full? We'd like to come add some additional storage to get you back down to a 50% margin. And by the way, most of our competitors only do a 25% margin. So again, another differentiator for IBM Storage-as-a-Service. >> What about, I said, last question, but I have another question. What about day one? Like how long does it take, if I want to start fresh with as-a-service? >> Get it. >> How long does it take to get up and running? >> Basically you put the PO through, whatever it takes on your side or through your business partner, we then we'll sign the technical account manager, will call you up because you need to tell us, do you want to, in a colo facility that you're working with or do you want to put it on on-prem? And then once we do that, we just schedule a time for your IT guys do the install. So, probably two weeks. >> Yeah. >> It all depends because you've got to call back and say, Eric, we'd like it at our colo partner, our colo partners, ABC, we got to call ABC and then get back to you or on-prem , we're going to have guys in the office, a good day when it's not going to be too busy. Could you come two weeks from Thursday? Which now would be three weeks for sake of argument. But that would be, we interface with the customer, with the technical account manager to do it on your schedule on your time, whether you do it in your own facility or use a colo provider. >> Yeah, but once you tell, once I tell you, once we get through all that stuff, it's two weeks from when that's all agreed. >> Yeah. >> It's like the Xerox copier salesman, (Dave chuckles) Where are you going to put it? Once you decide where you're going to put it, then it's a couple of weeks. It's not a month or two months or yeah. >> Yeah, it's not. And we need additional capacity, remember there's a 50% margin sitting there. So if you need to go into the variable and use it, and when we hit a 75%, we actually track it with our storage insights pro. So we'll call you up and say, Dave, you're at 76%. We'd like to add more storage to give you better margin of extra storage and you would say, great, when can we do it? So, yeah, we're proactive about that to make sure that you stay at that 50% margin. Again, our competitors, all do only have 25% margin. So we're giving you that better margin, a larger margin in case you really have a high capacity demand for that quarter and we proactively will call you up, if we think you need more based on monitoring your storage usage. >> Great. Eric got to go, thank you so much for taking us through that great detail, I really appreciate it. Always good to see you. >> Great, thanks Dave, really appreciate it. >> Alright, thank you for watching this CUBE conversation, this is Dave Vellante and we'll see you next time. (soft music)

(upbeat music) >> The Cloud essentially turned the data center into an API and ushered in the era of programmable infrastructure, no longer do we think about deploying infrastructure in rigid silos with a hardened, outer shell, rather infrastructure has to facilitate digital business strategies. And what this means is putting data at the core of your organization, irrespective of its physical location. It also means infrastructure generally and storage specifically must be accessed as sets of services that can be discovered, deployed, managed, secured, and governed in a DevOps model or OpsDev, if you prefer. Now, this has specific implications as to how vendor product strategies will evolve and how they'll meet modern data requirements. Welcome to this Cube conversation, everybody. This is Dave Vellante. And with me to discuss these sea changes is Ajay Singh, the Chief Product Officer of Pure Storage, Ajay welcome. >> Thank you, David, gald to be on. >> Yeah, great to have you, so let's talk about your role at Pure. I think you're the first CPO, what's the vision there? >> That's right, I just joined up Pure about eight months ago from VMware as the chief product officer and you're right, I'm the first our chief product officer at Pure. And at VMware I ran the Cloud management business unit, which was a lot about automation and infrastructure as code. And it's just great to join Pure, which has a phenomenal all flash product set. I kind of call it the iPhone or flash story super easy to use. And how do we take that same ease of use, which is a heart of a Cloud operating principle, and how do we actually take it up to really deliver a modern data experience, which includes infrastructure and storage as code, but then even more beyond that and how do you do modern operations and then modern data services. So super excited to be at Pure. And the vision, if you may, at the end of the day, is to provide, leveraging this moderate experience, a connected and effortless experience data experience, which allows customers to ultimately focus on what matters for them, their business, and by really leveraging and managing and winning with their data, because ultimately data is the new oil, if you may, and if you can mine it, get insights from it and really drive a competitive edge in the digital transformation in your head, and that's what be intended to help our customers to. >> So you joined earlier this year kind of, I guess, middle of the pandemic really I'm interested in kind of your first 100 days, what that was like, what key milestones you set and now you're into your second a 100 plus days. How's that all going? What can you share with us in and that's interesting timing because the effects of the pandemic you came in in a kind of post that, so you had experience from VMware and then you had to apply that to the product organization. So tell us about that sort of first a 100 days and the sort of mission now. >> Absolutely, so as we talked about the vision, around the modern data experience, kind of have three components to it, modernizing the infrastructure and really it's kudos to the team out of the work we've been doing, a ton of work in modernizing the infrastructure, I'll briefly talk to that, then modernizing the data, much more than modernizing the operations. I'll talk to that as well. And then of course, down the pike, modernizing data services. So if you think about it from modernizing the infrastructure, if you think about Pure for a minute, Pure is the first company that took flash to mainstream, essentially bringing what we call consumer simplicity to enterprise storage. The manual for the products with the front and back of a business card, that's it, you plug it in, boom, it's up and running, and then you get proactive AI driven support, right? So that was kind of the heart of Pure. Now you think about Pure again, what's unique about Pure has been a lot of our competition, has dealt with flash at the SSD level, hey, because guess what? All this software was built for hard drive. And so if I can treat NAND as a solid state drive SSD, then my software would easily work on it. But with Pure, because we started with flash, we released went straight to the NAND level, and as opposed to kind of the SSD layer, and what that does is it gives you greater efficiency, greater reliability and create a performance compared to an SSD, because you can optimize at the chip level as opposed to at the SSD module level. That's one big advantage that Pure has going for itself. And if you look at the physics, in the industry for a minute, there's recent data put out by Wikibon early this year, effectively showing that by the year 2026, flash on a dollar per terabyte basis, just the economics of the semiconductor versus the hard disk is going to be cheaper than hard disk. So this big inflection point is slowly but surely coming that's going to disrupt the hardest industry, already the high end has been taken over by flash, but hybrid is next and then even the long tail is coming up over there. And so to end to that extent our lead, if you may, the introduction of QLC NAND, QLC NAND powerful competition is barely introducing, we've been at it for a while. We just recently this year in my first a 100 days, we introduced the flasher AC, C40 and C60 drives, which really start to open up our ability to go after the hybrid story market in a big way. It opens up a big new market for us. So great work there by the team,. Also at the heart of it. If you think about it in the NAND side, we have our flash array, which is a scale-up latency centric architecture and FlashBlade which is a scale-out throughput architecture, all operating with NAND. And what that does is it allows us to cover both structured data, unstructured data, tier one apps and tier two apps. So pretty broad data coverage in that journey to the all flash data center, slowly but surely we're heading over there to the all flash data center based on demand economics that we just talked about, and we've done a bunch of releases. And then the team has done a bunch of things around introducing and NVME or fabric, the kind of thing that you expect them to do. A lot of recognition in the industry for the team or from the likes of TrustRadius, Gartner, named FlashRay, the Carton Peer Insights, the customer choice award and primary storage in the MQ. We were the leader. So a lot of kudos and recognition coming to the team as a result, Flash Blade just hit a billion dollars in cumulative revenue, kind of a leader by far in kind of the unstructured data, fast file an object marketplace. And then of course, all the work we're doing around what we say, ESG, environmental, social and governance, around reducing carbon footprint, reducing waste, our whole notion of evergreen and non-disruptive upgrades. We also kind of did a lot of work in that where we actually announced that over 2,700 customers have actually done non-disruptive upgrades over the technology. >> Yeah a lot to unpack there. And a lot of this sometimes you people say, oh, it's the plumbing, but the plumbing is actually very important too. 'Cause we're in a major inflection point, when we went from spinning disk to NAND. And it's all about volumes, you're seeing this all over the industry now, you see your old boss, Pat Gelsinger, is dealing with this at Intel. And it's all about consumer volumes in my view anyway, because thanks to Steve Jobs, NAND volumes are enormous and what two hard disk drive makers left in the planet. I don't know, maybe there's two and a half, but so those volumes drive costs down. And so you're on that curve and you can debate as to when it's going to happen, but it's not an if it's a when. Let me, shift gears a little bit. Because Cloud, as I was saying, it's ushered in this API economy, this as a service model, a lot of infrastructure companies have responded. How are you thinking at Pure about the as a service model for your customers? What's the strategy? How is it evolving and how does it differentiate from the competition? >> Absolutely, a great question. It's kind of segues into the second part of the moderate experience, which is how do you modernize the operations? And that's where automation as a service, because ultimately, the Cloud has validated and the address of this model, right? People are looking for outcomes. They care less about how you get there. They just want the outcome. And the as a service model actually delivers these outcomes. And this whole notion of infrastructure as code is kind of the start of it. Imagine if my infrastructure for a developer is just a line of code, in a Git repository in a program that goes through a CICD process and automatically kind of is configured and set up, fits in with the Terraform, the Ansibles, all that different automation frameworks. And so what we've done is we've gone down the path of really building out what I think is modern operations with this ability to have storage as code, disability, in addition modern operations is not just storage scored, but also we've got recently introduced some comprehensive ransomware protection, that's part of modern operations. There's all the threat you hear in the news or ransomware. We introduced what we call safe mode snapshots that allow you to recover in literally seconds. When you have a ransomware attack, we also have in the modern operations Pure one, which is maybe the leader in AI driven support to prevent downtime. We actually call you 80% of the time and fix the problems without you knowing about it. That's what modern operations is all about. And then also Martin operations says, okay, you've got flash on your on-prem side, but even maybe using flash in the public Cloud, how can I have seamless multi-Cloud experience in our Cloud block store we've introduced around Amazon, AWS and Azure allows one to do that. And then finally, for modern applications, if you think about it, this whole notion of infrastructure's code, as a service, software driven storage, the Kubernetes infrastructure enables one to really deliver a great automation framework that enables to reduce the labor required to manage the storage infrastructure and deliver it as code. And we have, kudos to Charlie and the Pure storage team before my time with the acquisition of Portworx, Portworx today is truly delivers true storage as code orchestrated entirely through Kubernetes and in a multi-Cloud hybrid situation. So it can run on EKS, GKE, OpenShift rancher, Tansu, recently announced as the leader by giggle home for enterprise Kubernetes storage. We were really proud about that asset. And then finally, the last piece are Pure as a service. That's also all outcome oriented, SLS. What matters is you sign up for SLS, and then you get those SLS, very different from our competition, right? Our competition tends to be a lot more around financial engineering, hey, you can buy it OPEX versus CapEx. And, but you get the same thing with a lot of professional services, we've really got, I'd say a couple of years and lead on, actually delivering and managing with SRE engineers for the SLA. So a lot of great work there. We recently also introduced Cisco FlashStack, again, flash stack as a service, again, as a service, a validation of that. And then finally, we also recently did a announcement with Aquaponics, with their bare metal as a service where we are a key part of their bare metal as a service offering, again, pushing the kind of the added service strategy. So yes, big for us, that's where the buck is skating, half the enterprises, even on prem, wanting to consume things in the Cloud operating model. And so that's where we're putting it lot. >> I see, so your contention is, it's not just this CapEx to OPEX, that's kind of the, during the economic downturn of 2007, 2008, the economic crisis, that was the big thing for CFOs. So that's kind of yesterday's news. What you're saying is you're creating a Cloud, like operating model, as I was saying upfront, irrespective of physical location. And I see that as your challenge, the industry's challenge, be, if I'm going to effect the digital transformation, I don't want to deal with the Cloud primitives. I want you to hide the underlying complexity of that Cloud. I want to deal with higher level problems, but so that brings me to digital transformation, which is kind of the now initiative, or I even sometimes call it the mandate. There's not a one size fits all for digital transformation, but I'm interested in your thoughts on the must take steps, universal steps that everybody needs to think about in a digital transformation journey. >> Yeah, so ultimately the digital transformation is all about how companies are gain a competitive edge in this new digital world or that the company are, and the competition are changing the game on, right? So you want to make sure that you can rapidly try new things, fail fast, innovate and invest, but speed is of the essence, agility and the Cloud operating model enables that agility. And so what we're also doing is not only are we driving agility in a multicloud kind of data, infrastructure, data operation fashion, but we also taking it a step further. We were also on the journey to deliver modern data services. Imagine on a Pure on-prem infrastructure, along with your different public Clouds that you're working on with the Kubernetes infrastructures, you could, with a few clicks run Kakfa as a service, TensorFlow as a service, Mongo as a service. So me as a technology team can truly become a service provider and not just an on-prem service provider, but a multi-Cloud service provider. Such that these services can be used to analyze the data that you have, not only your data, your partner data, third party public data, and how you can marry those different data sets, analyze it to deliver new insights that ultimately give you a competitive edge in the digital transformation. So you can see data plays a big role there. The data is what generates those insights. Your ability to match that data with partner data, public data, your data, the analysis on it services ready to go, as you get the digital, as you can do the insights. You can really start to separate yourself from your competition and get on the leaderboard a decade from now when this digital transformation settles down. >> All right, so bring us home, Ajay, summarize what does a modern data strategy look like and how does it fit into a digital business or a digital organization? >> So look, at the end of the day, data and analysis, both of them play a big role in the digital transformation. And it really comes down to how do I leverage this data, my data, partner data, public data, to really get that edge. And that links back to a vision. How do we provide that connected and effortless, modern data experience that allows our customers to focus on their business? How do I get the edge in the digital transformation? But easily leveraging, managing and winning with their data. And that's the heart of where Pure is headed. >> Ajay Singh, thanks so much for coming inside theCube and sharing your vision. >> Thank you, Dave, it was a real pleasure. >> And thank you for watching this Cube conversation. This is Dave Vellante and we'll see you next time. (upbeat music)

>> From around the globe, it's theCUBE, covering Data Citizens '21. Brought to you by Collibra. >> For the past decade organizations have been effecting very deliberate data strategies and investing quite heavily in people, processes and technology, specifically designed to gain insights from data, better serve customers, drive new revenue streams we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing. Data is increasingly distributed making collaboration and governance more challenging, especially where operational use cases are a priority. Hello, everyone. My name is Dave Vellante and you're watching theCUBE coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz who's the vice president and principal analyst at Forrester Research. Hello, Michele. Welcome to theCUBE. >> Hi, Dave. Thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, analyst, the analyst, et cetera, and many data-related functions. And my first question is what are they thinking about today? What's on their minds, these data experts? >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it's hyper contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right. The data and I sort of refer to this as well is sort of out there, it's distributed at the edge. But generally our data organizations are actually quite centralized and as well you talk about the need to trust the data obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business, we're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typical enterprise architects for data, a data engineering team that's managing warehouses as in data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data, and analytics, and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge the goal is that you're actually driving an experience and an application. And so in that case we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that? And there's a team over there to help. So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that. You took a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and my inference is that's changing and I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation, whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary. And you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development. And still incorporating the business expertise from those stakeholders, so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well. Hubris out context in, I mean, that's kind of what's been lacking. And I'm glad you said you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this. I come back to organization because I think a lot of organizations look the most cost effective way for us to serve the business is to have a single data team with hyper specialized roles. That'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business, which as you pointed out has the context is frustrated. They can't get to data. So there's this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. There the use cases where a lot of the manufacturing industrial device. Any sort of IOT based use case really recognized that without applying data and intelligence to whatever process was going to be executed. It was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IOT or edge component to though. We live on our smart phones, we live on our smart watches, we've got our laptops. All of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics but how do you feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate but ensure that there sort of an activation and alignment where the value is going to come out, and still being trusted and governed. >> I got kind of a weird question, but I'm going. I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, hey, we're going to pay up for the price of a vaccine. The cost per dose out, 28 bucks or whatever it was. And he pointed out that the EU haggled big time and they don't want to pay $19. And as a result they're not as far along. Israel understood that the real value was opening up the economy. And so there's an analogy here which I want to come back to my organization and it relates to the DataOps. Is if the real metric is, hey, I have an idea for a data product. How long does it take to go from idea to monetization? That seems to me to be a better KPI than how much storage I have, or how much geometry petabytes I'm managing. So my question is, and it relates to DataOps. Can that DataOps, should that DataOps individual maybe live, and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that and maybe talk a little bit more about this DataOps role. Is it. >> Yeah. >> Fungible. >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data, and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping. A lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter, really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset this DataOps team aligned to solution development, aligned to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately scope the data products that they're delivering to incremental value-based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michelle. I want to come back to this idea of collaboration because over the last decade we've seen attempts, I've seen software come out to try to help the various roles collaborate and some of it's been okay, but you have these hyper specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process. That's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations for the data science teams as well or even if there's machine learning engineers incorporated. And then certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So processes is number one. And number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science-oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage, and almost force you to collaborate and manage through sharing. >> Thank you for that. I want to ask you about the role data governance. You've mentioned trust and that's data quality, and you've got teams that are focused on and specialists focused on data quality. There's the data catalog. Here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is are a lot of value, I would say most is modeling. And in the future, you mentioned edge it's going to be a lot of influencing in real time. And people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate. We talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor intensive. What are you seeing the trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective and governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point, it was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum. You really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules, and business logic, and policies into services that are composable components and bring those into the solutions that you're defining. And in that way what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally driven on executing on governance rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge and context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and we synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging, and glossaries, and governance of data than we had before. And that goes back into what we talked about earlier of like, this is the new wave of DataOps. This is how you bring data products to fruition now. Everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team they know when the data is crap within my CRM, but our data systems are context agnostic generally. And you obviously understand that problem well. But so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run is as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain-centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this, because as you're working within your CRM systems, like a Salesforce, for example you're using an iPaaS MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources. And what's happening there is that the data is being modeled and personalized to whatever view insight our task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift, both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying centralizing data and creating an over-weighted business application and an over, both in terms of the data foundation but also in terms of the types of business services, and status updates, and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michelle, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share, that you're super pumped about? >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation. Really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value and tying it very specifically to processes and metrics, which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights. And as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed. And doing so not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things together is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCUBE. Really awesome perspectives. You're in an exciting space, so appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCUBE. My name is Dave Vellante. (upbeat music)

>>Hey, welcome to the live panel. My name is Brett. I am your host, and indeed we are live. In fact, if you're curious about that, if you don't believe us, um, let's just show a little bit of the browser real quick to see. Yup. There you go. We're live. So, all right. So how this is going to work is I'm going to bring in some guests and, uh, in one second, and we're going to basically take your questions on the topic designer of the day, that continuous integration testing. Uh, thank you so much to my guests welcoming into the panel. I've got Carlos, Nico and Mandy. Hello everyone. >>Hello? All right, >>Let's go. Let's go around the room and all pretend we don't know each other and that the internet didn't read below the video who we are. Uh, hi, my name is Brett. I am a Docker captain, which means I'm supposed to know something about Docker. I'm coming from Virginia Beach. I'm streaming here from Virginia Beach, Virginia, and, uh, I make videos on the internet and courses on you to me, Carlos. Hey, >>Hey, what's up? I'm Carlos Nunez. I am a solutions architect, VMware. I do solution things with computers. It's fun. I live in Dallas when I'm moving to Houston in a month, which is where I'm currently streaming. I've been all over the Northeast this whole week. So, um, it's been fun and I'm excited to meet with all of you and talk about CIA and Docker. Sure. >>Yeah. Hey everyone. Uh, Nico, Khobar here. I'm a solution engineer at HashiCorp. Uh, I am streaming to you from, uh, the beautiful Austin, Texas. Uh, ignore, ignore the golden gate bridge here. This is from my old apartment in San Francisco. Uh, just, uh, you know, keeping that, to remember all the good days, um, that that lived at. But, uh, anyway, I work at Patrick Corp and I work on all things, automation, um, and cloud and dev ops. Um, and I'm excited to be here and Mandy, >>Hi. Yeah, Mandy Hubbard. I am streaming from Austin, Texas. I am, uh, currently a DX engineer at ship engine. Um, I've worked in QA and that's kind of where I got my, uh, my Docker experience and, um, uh, moving into DX to try and help developers better understand and use our products and be an advocate for them. >>Nice. Well, thank you all for joining me. Uh, I really appreciate you taking the time out of your busy schedule to be here. And so for those of you in chat, the reason we're doing this live, because it's always harder to do things live. The reason we're here is to answer a question. So we didn't come with a bunch of slides and demos or anything like that. We're here to talk amongst ourselves about ideas and really here for you. So we've, we obviously, this is about easy CII, so we're, we're going to try to keep the conversation around testing and continuous integration and all the things that that entails with containers. But we may, we may go down rabbit holes. We may go veer off and start talking about other things, and that's totally fine if it's in the realm of dev ops and containers and developer and ops workflows, like, Hey, it's, it's kinda game. >>And, uh, these people have a wide variety of expertise. They haven't done just testing, right? We, we live in a world where you all kind of have to wear many hats. So feel free to, um, ask what you think is on the top of your mind. And we'll do our best to answer. It may, might not be the best answer or the correct answer, but we're going to do our best. Um, well, let's get it start off. Uh, let's, let's get a couple of topics to start off with. Uh, th the, the easy CGI was my, one of my three ideas. Cause he's the, one of the things that I'm most excited about is the innovation we're seeing around easier testing, faster testing, automated testing, uh, because as much as we've all been doing this stuff for, you know, 15 years, since 20 years since the sort of Jenkins early days, um, it it's, it seems like it's still really hard and it's still a lot of work. >>So, um, let's go around the room real quick, and everybody can just kind of talk for a minute about like your experience with testing and maybe some of your pain points, like what you don't like about our testing world. Um, and we can talk about some pains, cause I think that will lead us to kind of talk about what, what are the things we're seeing now that might be better, uh, ideas about how to do this. I know for me, uh, testing, obviously there's the code part, but just getting it automated, but mostly getting it in the hands of developers so that they can control their own testing. And don't have to go talk to a person to run that test again, or the mysterious Jenkins platform somewhere. I keep mentioning Jenkins cause it's, it is still the dominant player out there. Um, so for me, I'm, I'm, I, I don't like it when I'm walking into a room and there's, there's only one or two people that know how the testing works or know how to make the new tests go into the testing platform and stuff like that. So I'm always trying to free those things so that any of the developers are enabled and empowered to do that stuff. So someone else, Carlos, anybody, um, >>Oh, I have a lot of opinions on that. Having been a QA engineer for most of my career. Um, the shift that we're saying is everyone is dev ops and everyone is QA. Th the issue I see is no one asked developers if they wanted to be QA. Um, and so being the former QA on the team, when there's a problem, even though I'm a developer and we're all running QA, they always tend to come to the one of the former QA engineers. And they're not really owning that responsibility and, um, and digging in. So that's kind of what I'm saying is that we're all expected to test now. And some people, well, some people don't know how it's, uh, for me it was kind of an intuitive skill. It just kind of fit with my personality, but not knowing what to look for, not knowing what to automate, not even understanding how your API end points are used by your front end to know what to test when a change is made. It's really overwhelming for developers. And, um, we're going to need to streamline that and, and hold their hands a little bit until they get their feet wet with also being QA. >>Right. Right. So, um, uh, Carlos, >>Yeah, uh, testing is like, Tesla is one of my favorite subjects to talk about when I'm baring with developers. And a lot of it is because of what Mandy said, right? Like a lot of developers now who used to write a test and say, Hey, QA, go. Um, I wrote my unit tests. Now write the rest of the test. Essentially. Now developers are expected to be able to understand how testing, uh, testing methodologies work, um, in their local environments, right? Like they're supposed to understand how to write an integration tasks federate into and tasks, a component test. And of course, how to write unit tests that aren't just, you know, assert true is true, right? Like more comprehensive, more comprehensive, um, more high touch unit tests, which include things like mocking and stubbing and spine and all that stuff. And, you know, it's not so much getting those tests. Well, I've had a lot of challenges with developers getting those tests to run in Docker because of usually because of dependency hell, but, um, getting developers to understand how to write tests that matter and mean something. Um, it's, it's, it can be difficult, but it's also where I find a lot of the enjoyment of my work comes into play. So yeah. I mean, that's the difficulty I've seen around testing. Um, big subject though. Lots to talk about there. >>Yeah. We've got, we've already got so many questions coming in. You already got an hour's worth of stuff. So, uh, Nico 81st thoughts on that? >>Yeah, I think I definitely agree with, with other folks here on the panel, I think from a, um, the shift from a skillset perspective that's needed to adopt the new technologies, but I think from even from, uh, aside from the organizational, um, and kind of key responsibilities that, that the new developers have to kinda adapt to and, and kind of inherit now, um, there's also from a technical perspective as there's, you know, um, more developers are owning the full stack, including the infrastructure piece. So that adds a lot more to the plate in Tim's oaf, also testing that component that they were not even, uh, responsible for before. Um, and, um, also the second challenge that, you know, I'm seeing is that on, you know, the long list of added, um, uh, tooling and, you know, there's new tool every other day. Um, and, um, that kind of requires more customization to the testing, uh, that each individual team, um, any individual developer Y by extension has to learn. Uh, so the customization, uh, as well as the, kind of the scope that had, uh, you know, now in conferences, the infrastructure piece, um, uh, both of act to the, to the challenges that we're seeing right now for, um, for CGI and overall testing, um, uh, the developers are saying, uh, in, in the market today. >>Yeah. We've got a lot of questions, um, about all the, all the different parts of this. So, uh, let me just go straight to them. Cause that's why we're here is for the people, uh, a lot of people asking about your favorite tools and in one of this is one of the challenges with integration, right? Is, um, there is no, there are dominant players, but there, there is such a variety. I mean, every one of my customers seems like they're using a different workflow and a different set of tools. So, and Hey, we're all here to just talk about what we're, what we're using, uh, you know, whether your favorite tools. So like a lot of the repeated questions are, what are your favorite tools? Like if you could create it from scratch, uh, what would you use? Pierre's asking, you know, GitHub actions sounds like they're a fan of GitHub actions, uh, w you know, mentioning, pushing the ECR and Docker hub and, uh, using vs code pipeline, I guess there may be talking about Azure pipelines. Um, what, what's your preferred way? So, does anyone have any, uh, thoughts on that anyone want to throw out there? Their preferred pipeline of tooling? >>Well, I have to throw out mine. I might as Jenkins, um, like kind of a honorary cloud be at this point, having spoken a couple of times there, um, all of the plugins just make the functionality. I don't love the UI, but I love that it's been around so long. It has so much community support, and there are so many plugins so that if you want to do something, you don't have to write the code it's already been tested. Um, unfortunately I haven't been able to use Jenkins in, uh, since I joined ship engine, we, most of our, um, our, our monolithic core application is, is team city. It's a dotnet application and TeamCity plays really well with.net. Um, didn't love it, uh, Ms. Jenkins. And I'm just, we're just starting some new initiatives that are using GitHub actions, and I'm really excited to learn, to learn those. I think they have a lot of the same functionality that you're looking for, but, um, much more simplified in is right there and get hubs. So, um, the integration is a lot more seamless, but I do have to go on record that my favorite CICT tools Jenkins. >>All right. You heard it here first people. All right. Anyone else? You're muted? I'm muted. Carlin says muted. Oh, Carla says, guest has muted themselves to Carlos. You got to unmute. >>Yes. I did mute myself because I was typing a lot, trying to, you know, try to answer stuff in the chat. And there's a lot of really dark stuff in there. That's okay. Two more times today. So yeah, it's fine. Yeah, no problem. So totally. And it's the best way to start a play more. So I'm just going to go ahead and light it up. Um, for enterprise environments, I actually am a huge fan of Jenkins. Um, it's a tool that people really understand. Um, it has stood the test of time, right? I mean, people were using Hudson, but 15 years ago, maybe longer. And, you know, the way it works, hasn't really changed very much. I mean, Jenkins X is a little different, but, um, the UI and the way it works internally is pretty familiar to a lot of enterprise environments, which is great. >>And also in me, the plugin ecosystem is amazing. There's so many plugins for everything, and you can make your own if you know, Java groovy. I'm sure there's a perfect Kotlin in there, but I haven't tried myself, but it's really great. It's also really easy to write, um, CIS code, which is something I'm a big fan of. So Jenkins files have been, have worked really well for me. I, I know that I can get a little bit more complex as you start to build your own models and such, but, you know, for enterprise enterprise CIO CD, if you want, especially if you want to roll your own or own it yourself, um, Jenkins is the bellwether and for very good reason now for my personal projects. And I see a lot on the chat here, I think y'all, y'all been agreed with me get hub actions 100%, my favorite tool right now. >>Um, I love GitHub actions. It's, it's customizable, it's modular. There's a lot of plugins already. I started using getting that back maybe a week after when GA and there was no documentation or anything. And I still, it was still my favorite CIA tool even then. Um, and you know, the API is really great. There's a lot to love about GitHub actions and, um, and I, and I use it as much as I can from my personal project. So I still have a soft spot for Travis CAI. Um, you know, they got acquired and they're a little different now trying to see, I, I can't, I can't let it go. I just love it. But, um, yeah, I mean, when it comes to Seattle, those are my tools. So light me up in the comments I will respond. Yeah. >>I mean, I, I feel with you on the Travis, the, I think, cause I think that was my first time experiencing, you know, early days get hub open source and like a free CIA tool that I could describe. I think it was the ammo back then. I don't actually remember, but yeah, it was kind of an exciting time from my experience. There was like, oh, this is, this is just there as a service. And I could just use it. It doesn't, it's like get hub it's free from my open source stuff. And so it does have a soft spot in my heart too. So yeah. >>All right. We've got questions around, um, cam, so I'm going to ask some questions. We don't have to have these answers because sometimes they're going to be specific, but I want to call them out because people in chat may have missed that question. And there's probably, you know, that we have smart people in chat too. So there's probably someone that knows the answer to these things. If, if it's not us, um, they're asking about building Docker images in Kubernetes, which to me is always a sore spot because it's Kubernetes does not build images by default. It's not meant for that out of the gate. And, uh, what is the best way to do this without having to use privileged containers, which privileged containers just implying that yeah, you, you, it probably has more privileges than by default as a container in Kubernetes. And that is a hard thing because, uh, I don't, I think Docker doesn't lie to do that out of the gate. So I don't know if anyone has an immediate answer to that. That's a pretty technical one, but if you, if you know the answer to that in chat, call it out. >>Um, >>I had done this, uh, but I'm pretty sure I had to use a privileged, um, container and install the Docker Damon on the Kubernetes cluster. And I CA I can't give you a better solution. Um, I've done the same. So, >>Yeah, uh, Chavonne asks, um, back to the Jenkins thing, what's the easiest way to integrate Docker into a Jenkins CICB pipeline. And that's one of the challenges I find with Jenkins because I don't claim to be the expert on Jenkins. Is there are so many plugins because of this, of this such a huge ecosystem. Um, when you go searching for Docker, there's a lot that comes back, right. So I, I don't actually have a preferred way because every team I find uses it differently. Um, I don't know, is there a, do you know if there's a Jenkins preferred, a default plugin? I don't even know for Docker. Oh, go ahead. Yeah. Sorry for Docker. And jacon sorry, Docker plugins for Jenkins. Uh, as someone's asking like the preferred or easy way to do that. Um, and I don't, I don't know the back into Jenkins that well, so, >>Well, th the new, the new way that they're doing, uh, Docker builds with the pipeline, which is more declarative versus the groovy. It's really simple, and their documentation is really good. They, um, they make it really easy to say, run this in this image. So you can pull down, you know, public images and add your own layers. Um, so I don't know the name of that plugin, uh, but I can certainly take a minute after this session and going and get that. Um, but if you really are overwhelmed by the plugins, you can just write your, you know, your shell command in Jenkins. You could just by, you know, doing everything in bash, calling the Docker, um, Damon directly, and then getting it working just to see that end to end, and then start browsing for plugins to see if you even want to use those. >>The plugins will allow more integration from end to end. Some of the things that you input might be available later on in the process for having to manage that yourself. But, you know, you don't have to use any of the plugins. You can literally just, you know, do a block where you write your shell command and get it working, and then decide if, for plugins for you. Um, I think it's always under important to understand what is going on under the hood before you, before you adopt the magic of a plugin, because, um, once you have a problem, if you're, if it's all a lockbox to you, it's going to be more difficult to troubleshoot. It's kind of like learning, get command line versus like get cracking or something. Once, once you get in a bind, if you don't understand the underlying steps, it's really hard to get yourself out of a bind, versus if you understand what the plugin or the app is doing, then, um, you can get out of situations a lot easier. That's a good place. That's, that's where I'd start. >>Yeah. Thank you. Um, Camden asks better to build test environment images, every commit in CII. So this is like one of those opinions of we're all gonna have some different, uh, or build on build images on every commit, leveraging the cash, or build them once outside the test pile pipeline. Um, what say you people? >>Uh, well, I I've seen both and generally speaking, my preference is, um, I guess the ant, the it's a consultant answer, right? I think it depends on what you're trying to do, right. So if you have a lot of small changes that are being made and you're creating images for each of those commits, you're going to have a lot of images in your, in your registry, right? And on top of that, if you're building those images, uh, through CAI frequently, if you're using Docker hub or something like that, you might run into rate limiting issues because of Docker's new rate, limiting, uh, rate limits that they put in place. Um, but that might be beneficial if the, if being able to roll back between those small changes while you're testing is important to you. Uh, however, if all you care about is being able to use Docker images, um, or being able to correlate versions to your Docker images, or if you're the type of team that doesn't even use him, uh, does he even use, uh, virgins in your image tags? Then I would think that that might be a little, much you might want to just have in your CIO. You might want to have a stage that builds your Docker images and Docker image and pushes it into your registry, being done first particular branches instead of having to be done on every commit regardless of branch. But again, it really depends on the team. It really depends on what you're building. It really depends on your workflow. It can depend on a number of things like a curse sometimes too. Yeah. Yeah. >>Once had two points here, you know, I've seen, you know, the pattern has been at every, with every, uh, uh, commit, assuming that you have the right set of tests that would kind of, uh, you would benefit from actually seeing, um, the, the, the, the testing workflow go through and can detect any issue within, within the build or whatever you're trying to test against. But if you're just a building without the appropriate set of tests, then you're just basically consuming almond, adding time, as well as all the, the image, uh, stories associated with it without treaty reaping the benefit of, of, of this pattern. Uh, and the second point is, again, I think if you're, if you're going to end up doing a per commit, uh, definitely recommend having some type of, uh, uh, image purging, um, uh, and, and, and garbage collection process to ensure that you're not just wasting, um, all the stories needed and also, um, uh, optimizing your, your bill process, because that will end up being the most time-consuming, um, um, you know, within, within your pipeline. So this is my 2 cents on this. >>Yeah, that's good stuff. I mean, those are both of those are conversations that could lead us into the rabbit hole for the rest of the day on storage management, uh, you know, CP CPU minutes for, uh, you know, your build stuff. I mean, if you're in any size team, more than one or two people, you immediately run into headaches with cost of CIA, because we have now the problem of tools, right? We have so many tools. We can have the CIS system burning CPU cycles all day, every day, if we really wanted to. And so you re very quickly, I think, especially if you're on every commit on every branch, like that gets you into a world of cost mitigation, and you probably are going to have to settle somewhere in the middle on, uh, between the budget, people that are saying you're spending way too much money on the CII platform, uh, because of all these CPU cycles, and then the developers who would love to have everything now, you know, as fast as possible and the biggest, biggest CPU's, and the biggest servers, and have the bills, because the bills can never go fast enough, right. >>There's no end to optimizing your build workflow. Um, we have another question on that. This is another topic that we'll all probably have different takes on is, uh, basically, uh, version tags, right? So on images, we, we have a very established workflow in get for how we make commits. We have commit shots. We have, uh, you know, we know get tags and there's all these things there. And then we go into images and it's just this whole new world that's opened up. Like there's no real consensus. Um, so what, what are your thoughts on the strategy for teams in their image tag? Again, another, another culture thing. Um, commander, >>I mean, I'm a fan of silver when we have no other option. Um, it's just clean and I like the timestamp, you know, exactly when it was built. Um, I don't really see any reason to use another, uh, there's just normal, incremental, um, you know, numbering, but I love the fact that you can pull any tag and know exactly when it was created. So I'm a big fan of bar, if you can make that work for your organization. >>Yep. People are mentioned that in chat, >>So I like as well. Uh, I'm a big fan of it. I think it's easy to be able to just be as easy to be able to signify what a major changes versus a minor change versus just a hot fix or, you know, some or some kind of a bad fix. The problem that I've found with having teams adopt San Bernardo becomes answering these questions and being able to really define what is a major change, what is a minor change? What is a patch, right? And this becomes a bit of an overhead or not so much of an overhead, but, uh, uh, uh, a large concern for teams who have never done versioning before, or they never been responsible for their own versioning. Um, in fact, you know, I'm running into that right now, uh, with, with a client that I'm working with, where a lot, I'm working with a lot of teams, helping them move their applications from a legacy production environment into a new one. >>And in doing so, uh, versioning comes up because Docker images, uh, have tags and usually the tax correlate to versions, but some teams over there, some teams that I'm working with are only maintaining a script and others are maintaining a fully fledged JAK, three tier application, you know, with lots of dependencies. So telling the script, telling the team that maintains a script, Hey, you know, you should use somber and you should start thinking about, you know, what's major, what's my number what's patch. That might be a lot for them. And for someone or a team like that, I might just suggest using commit shots as your versions until you figure that out, or maybe using, um, dates as your version, but for the more for the team, with the larger application, they probably already know the answers to those questions. In which case they're either already using Sember or they, um, or they may be using some other version of the strategy and might be in December, might suit them better. So, um, you're going to hear me say, it depends a lot, and I'm just going to say here, it depends. Cause it really does. Carlos. >>I think you hit on something interesting beyond just how to version, but, um, when to consider it a major release and who makes those decisions, and if you leave it to engineers to version, you're kind of pushing business decisions down the pipe. Um, I think when it's a minor or a major should be a business decision and someone else needs to make that call someone closer to the business should be making that call as to when we want to call it major. >>That's a really good point. And I add some, I actually agree. Um, I absolutely agree with that. And again, it really depends on the team that on the team and the scope of it, it depends on the scope that they're maintaining, right? And so it's a business application. Of course, you're going to have a product manager and you're going to have, you're going to have a product manager who's going to want to make that call because that version is going to be out in marketing. People are going to use it. They're going to refer to and support calls. They're going to need to make those decisions. Sember again, works really, really well for that. Um, but for a team that's maintaining the scripts, you know, I don't know, having them say, okay, you must tell me what a major version is. It's >>A lot, but >>If they want it to use some birds great too, which is why I think going back to what you originally said, Sember in the absence of other options. I think that's a good strategy. >>Yeah. There's a, there's a, um, catching up on chat. I'm not sure if I'm ever going to catch up, but there's a lot of people commenting on their favorite CII systems and it's, and it, it just goes to show for the, the testing and deployment community. Like how many tools there are out there, how many tools there are to support the tools that you're using. Like, uh, it can be a crazy wilderness. And I think that's, that's part of the art of it, uh, is that these things are allowing us to build our workflows to the team's culture. Um, and, uh, but I do think that, you know, getting into like maybe what we hope to be at what's next is I do hope that we get to, to try to figure out some of these harder problems of consistency. Uh, one of the things that led me to Docker at the beginning to begin with was the fact that it wa it created a consistent packaging solution for me to get my code, you know, off of, off of my site of my local system, really, and into the server. >>And that whole workflow would at least the thing that I was making at each step was going to be the same thing used. Right. And that, that was huge. Uh, it was also, it also took us a long time to get there. Right. We all had to, like Docker was one of those ones that decade kind of ideas of let's solidify the, enter, get the consensus of the community around this idea. And we, and it's not perfect. Uh, you know, the Docker Docker file is not the most perfect way to describe how to make your app, but it is there and we're all using it. And now I'm looking for that next piece, right. Then hopefully the next step in that, um, that where we can all arrive at a consensus so that once you hop teams, you know, okay. We all knew Docker. We now, now we're all starting to get to know the manifests, but then there's this big gap in the middle where it's like, it might be one of a dozen things. Um, you know, so >>Yeah, yeah. To that, to that, Brett, um, you know, uh, just maybe more of a shameless plug here and wanting to kind of talk about one of the things that I'm on. So excited, but I work, I work at Tasha Corp. I don't know anyone, or I don't know if many people have heard of, um, you know, we tend to focus a lot on workflows versus technologies, right. Because, you know, as you can see, even just looking at the chat, there's, you know, ton of opinions on the different tooling, right. And, uh, imagine having, you know, I'm working with clients that have 10,000 developers. So imagine taking the folks in the chat and being partnered with one organization or one company and having to make decisions on how to build software. Um, but there's no way you can conversion one or, or one way or one tool, uh, and that's where we're facing in the industry. >>So one of the things that, uh, I'm pretty excited about, and I don't know if it's getting as much traction as you know, we've been focused on it. This is way point, which is a project, an open source project. I believe we got at least, uh, last year, um, which is, it's more of, uh, it's, it is aim to address that really, uh, uh, Brad set on, you know, to come to tool to, uh, make it extremely easy and simple. And, you know, to describe how you want to build, uh, deploy or release your application, uh, in, in a consistent way, regardless of the tools. So similar to how you can think of Terraform and having that pluggability to say Terraform apply or plan against any cloud infrastructure, uh, without really having to know exactly the details of how to do it, uh, this is what wave one is doing. Um, and it can be applied with, you know, for the CIA, uh, framework. So, you know, task plugability into, uh, you know, circle CEI tests to Docker helm, uh, Kubernetes. So that's the, you know, it's, it's a hard problem to solve, but, um, I'm hopeful that that's the path that we're, you know, we'll, we'll eventually get to. So, um, hope, you know, you can, you can, uh, see some of the, you know, information, data on it, on, on HashiCorp site, but I mean, I'm personally excited about it. >>Yeah. Uh I'm to gonna have to check that out. And, um, I told you on my live show, man, we'll talk about it, but talk about it for a whole hour. Uh, so there's another question here around, uh, this, this is actually a little bit more detailed, but it is one that I think a lot of people deal with and I deal with a lot too, is essentially the question is from Cameron, uh, D essentially, do you use compose in your CIO or not Docker compose? Uh, because yes I do. Yeah. Cause it, it, it, it solves so many problems am and not every CGI can, I don't know, there's some problems with a CIO is trying to do it for me. So there are pros and cons and I feel like I'm still on the fence about it because I use it all the time, but also it's not perfect. It's not always meant for CIA. And CIA sometimes tries to do things for you, like starting things up before you start other parts and having that whole order, uh, ordering problem of things anyway. W thoughts and when have thoughts. >>Yes. I love compose. It's one of my favorite tools of all time. Um, and the reason why it's, because what I often find I'm working with teams trying to actually let me walk that back, because Jack on the chat asked a really interesting question about what, what, what the hardest thing about CIS for a lot of teams. And in my experience, the hardest thing is getting teams to build an app that is the same app as what's built in production. A lot of CGI does things that are totally different than what you would do in your local, in your local dev. And as a result of that, you get, you got this application that either doesn't work locally, or it does work, but it's a completely different animal than what you would get in production. Right? So what I've found in trying to get teams to bridge that gap by basically taking their CGI, shifting the CII left, I hate the shift left turn, but I'll use it. >>I'm shifting the CIO left to your local development is trying to say, okay, how do we build an app? How do we, how do we build mot dependencies of that app so that we can build so that we can test our app? How do we run tests, right? How do we build, how do we get test data? And what I found is that trying to get teams to do all this in Docker, which is normally a first for a lot of teams that I'm working with, trying to get them all to do all of this. And Docker means you're running Docker, build a lot running Docker, run a lot. You're running Docker, RM a lot. You ran a lot of Docker, disparate Docker commands. And then on top of that, trying to bridge all of those containers together into a single network can be challenging without compose. >>So I like using a, to be able to really easily categorize and compartmentalize a lot of the things that are going to be done in CII, like building a Docker image, running tests, which is you're, you're going to do it in CII anyway. So running tests, building the image, pushing it to the registry. Well, I wouldn't say pushing it to the registry, but doing all the things that you would do in local dev, but in the same network that you might have a mock database or a mock S3 instance or some of something else. Um, so it's just easy to take all those Docker compose commands and move them into your Yammel file using the hub actions or your dankest Bob using Jenkins, or what have you. Right. It's really, it's really portable that way, but it doesn't work for every team. You know, for example, if you're just a team that, you know, going back to my script example, if it's a really simple script that does one thing on a somewhat routine basis, then that might be a lot of overhead. Um, in that case, you know, you can get away with just Docker commands. It's not a big deal, but the way I looked at it is if I'm, if I'm building, if I build something that's similar to a make bile or rate file, or what have you, then I'm probably gonna want to use Docker compose. If I'm working with Docker, that's, that's a philosophy of values, right? >>So I'm also a fan of Docker compose. And, um, you know, to your point, Carlos, the whole, I mean, I'm also a fan of shifting CEI lift and testing lift, but if you put all that logic in your CTI, um, it changes the L the local development experience from the CGI experience. Versus if you put everything in a compose file so that what you build locally is the same as what you build in CGI. Um, you're going to have a better experience because you're going to be testing something more, that's closer to what you're going to be releasing. And it's also very easy to look at a compose file and kind of, um, understand what the dependencies are and what's happening is very readable. And once you move that stuff to CGI, I think a lot of developers, you know, they're going to be intimidated by the CGI, um, whatever the scripting language is, it's going to be something they're going to have to wrap their head around. >>Um, but they're not gonna be able to use it locally. You're going to have to have another local solution. So I love the idea of a composed file use locally, um, especially if he can Mount the local workspace so that they can do real time development and see their changes in the exact same way as it's going to be built and tested in CGI. It gives developers a high level of confidence. And then, you know, you're less likely to have issues because of discrepancies between how it was built in your local test environment versus how it's built in NCI. And so Docker compose really lets you do all of that in a way that makes your solution more portable, portable between local dev and CGI and reduces the number of CGI cycles to get, you know, the test, the test data that you need. So that's why I like it for really, for local dev. >>It'll be interesting. Um, I don't know if you all were able to see the keynote, but there was a, there was a little bit, not a whole lot, but a little bit talk of the Docker, compose V two, which has now built into the Docker command line. And so now we're shifting from the Python built compose, which was a separate package. You could that one of the challenges was getting it into your CA solution because if you don't have PIP and you got down on the binary and the binary wasn't available for every platform and, uh, it was a PI installer. It gets a little nerdy into how that works, but, uh, and the team is now getting, be able to get unified with it. Now that it's in Golang and it's, and it's plugged right into the Docker command line, it hopefully will be easier to distribute, easier to, to use. >>And you won't have to necessarily have dependencies inside of where you're running it because there'll be a statically compiled binary. Um, so I've been playing with that, uh, this year. And so like training myself to do Docker going from Docker dash compose to Docker space, compose. It is a thing I I'm almost to the point of having to write a shell replacement. Yeah. Alias that thing. Um, but, um, I'm excited to see what that's going, cause there's already new features in it. And it, these built kit by default, like there's all these things. And I, I love build kit. We could make a whole session on build kit. Um, in fact there's actually, um, maybe going on right now, or right around this time, there is a session on, uh, from Solomon hikes, the seat, uh, co-founder of Docker, former CTO, uh, on build kit using, uh, using some other tool on top of build kit or whatever. >>So that, that would be interesting for those of you that are not watching that one. Cause you're here, uh, to do a check that one out later. Um, all right. So another good question was caching. So another one, another area where there is no wrong answers probably, and everyone has a different story. So the question is, what are your thoughts on CII build caching? There's often a debate between security. This is from Quentin. Thank you for this great question. There's often a debate between security reproducibility and build speeds. I haven't found a good answer so far. I will just throw my hat in the ring and say that the more times you want to build, like if you're trying to build every commit or every commit, if you're building many times a day, the more caching you need. So like the more times you're building, the more caching you're gonna likely want. And in most cases caching doesn't bite you in the butt, but that could be, yeah, we, can we get the bit about that? So, yeah. Yeah. >>I'm going to quote Carlos again and say, it depends on, on, you know, how you're talking, you know, what you're trying to build and I'm quoting your colors. Um, yeah, it's, it's got, it's gonna depend because, you know, there are some instances where you definitely want to use, you know, depends on the frequency that you're building and how you're building. Um, it's you would want to actually take advantage of cashing functionalities, um, for the build, uh, itself. Um, but if, um, you know, as you mentioned, there could be some instances where you would want to disable, um, any caching because you actually want to either pull a new packages or, um, you know, there could be some security, um, uh, disadvantages related to security aspects that would, you know, you know, using a cache version of, uh, image layer, for example, could be a problem. And you, you know, if you have a fleet of build, uh, engines, you don't have a good grasp of where they're being cashed. We would have to, um, disable caching in that, in that, um, in those instances. So it, it would depend. >>Yeah, it's, it's funny you have that problem on both sides of cashing. Like there are things that, especially in Docker world, they will cash automatically. And, and then, and then you maybe don't realize that some of that caching could be bad. It's, it's actually using old, uh, old assets, old artifacts, and then there's times where you would expect it to cash, that it doesn't cash. And then you have to do something extra to enable that caching, especially when you're dealing with that cluster of, of CIS servers. Right. And the cloud, the whole clustering problem with caching is even more complex, but yeah, >>But that's, that's when, >>Uh, you know, ever since I asked you to start using build kits and able to build kit, you know, between it's it's it's reader of Boston in, in detecting word, you know, where in, in the bill process needs to cash, as well as, uh, the, the, um, you know, the process. I don't think I've seen any other, uh, approach there that comes close to how efficient, uh, that process can become how much time it can actually save. Uh, but again, I think, I think that's, for me that had been my default approach, unless I actually need something that I would intentionally to disable caching for that purpose, but the benefits, at least for me, the benefits of, um, how bill kit actually been processing my bills, um, from the builds as well as, you know, using the cash up until, you know, how it detects the, the difference in, in, in the assets within the Docker file had been, um, you know, uh, pretty, you know, outweigh the disadvantages that it brings in. So it, you know, take it each case by case. And based on that, determine if you want to use it, but definitely recommend those enabling >>In the absence of a reason not to, um, I definitely think that it's a good approach in terms of speed. Um, yeah, I say you cash until you have a good reason not to personally >>Catch by default. There you go. I think you catch by default. Yeah. Yeah. And, uh, the trick is, well, one, it's not always enabled by default, especially when you're talking about cross server. So that's a, that's a complexity for your SIS admins, or if you're on the cloud, you know, it's usually just an option. Um, I think it also is this, this veers into a little bit of, uh, the more you cash the in a lot of cases with Docker, like the, from like, if you're from images and checked every single time, if you're not pinning every single thing, if you're not painting your app version, you're at your MPN versions to the exact lock file definition. Like there's a lot of these things where I'm I get, I get sort of, I get very grouchy with teams that sort of let it, just let it all be like, yeah, we'll just build two images and they're totally going to have different dependencies because someone happened to update that thing and after whatever or MPM or, or, and so I get grouchy about that, cause I want to lock it all down, but I also know that that's going to create administrative burden. >>Like the team is now going to have to manage versions in a very much more granular way. Like, do we need to version two? Do we need to care about curl? You know, all that stuff. Um, so that's, that's kind of tricky, but when you get to, when you get to certain version problems, uh, sorry, uh, cashing problems, you, you, you don't want those set those caches to happen because it, if you're from image changes and you're not constantly checking for a new image, and if you're not pinning that V that version, then now you, you don't know whether you're getting the latest version of Davion or whatever. Um, so I think that there's, there's an art form to the more you pen, the less you have, the less, you have to be worried about things changing, but the more you pen, the, uh, all your versions of everything all the way down the stack, the more administrative stuff, because you're gonna have to manually change every one of those. >>So I think it's a balancing act for teams. And as you mature, I to find teams, they tend to pin more until they get to a point of being more comfortable with their testing. So the other side of this argument is if you trust your testing, then you, and you have better testing to me, the less likely to the subtle little differences in versions have to be penned because you can get away with those minor or patch level version changes. If you're thoroughly testing your app, because you're trusting your testing. And this gets us into a whole nother rant, but, uh, yeah, but talking >>About penny versions, if you've got a lot of dependencies isn't that when you would want to use the cash the most and not have to rebuild all those layers. Yeah. >>But if you're not, but if you're not painting to the exact patch version and you are caching, then you're not technically getting the latest versions because it's not checking for all the time. It's a weird, there's a lot of this subtle nuance that people don't realize until it's a problem. And that's part of the, the tricky part of allow this stuff, is it, sometimes the Docker can be almost so much magic out of the box that you, you, you get this all and it all works. And then day two happens and you built it a second time and you've got a new version of open SSL in there and suddenly it doesn't work. Um, so anyway, uh, that was a great question. I've done the question on this, on, uh, from heavy. What do you put, where do you put testing in your pipeline? Like, so testing the code cause there's lots of types of testing, uh, because this pipeline gets longer and longer and Docker building images as part of it. And so he says, um, before staging or after staging, but before production, where do you put it? >>Oh man. Okay. So, um, my, my main thought on this is, and of course this is kind of religious flame bait, so sure. You know, people are going to go into the compensation wrong. Carlos, the boy is how I like to think about it. So pretty much in every stage or every environment that you're going to be deploying your app into, or that your application is going to touch. My idea is that there should be a build of a Docker image that has all your applications coded in, along with its dependencies, there's testing that tests your application, and then there's a deployment that happens into whatever infrastructure there is. Right. So the testing, they can get tricky though. And the type of testing you do, I think depends on the environment that you're in. So if you're, let's say for example, your team and you have, you have a main branch and then you have feature branches that merged into the main branch. >>You don't have like a pre-production branch or anything like that. So in those feature branches, whenever I'm doing CGI that way, I know when I freak, when I cut my poll request, that I'm going to merge into main and everything's going to work in my feature branches, I'm going to want to probably just run unit tests and maybe some component tests, which really, which are just, you know, testing that your app can talk to another component or another part, another dependency, like maybe a database doing tests like that, that don't take a lot of time that are fascinating and right. A lot of would be done at the beach branch level and in my opinion, but when you're going to merge that beach branch into main, as part of a release in that activity, you're going to want to be able to do an integration tasks, to make sure that your app can actually talk to all the other dependencies that it talked to. >>You're going to want to do an end to end test or a smoke test, just to make sure that, you know, someone that actually touches the application, if it's like a website can actually use the website as intended and it meets the business cases and all that, and you might even have testing like performance testing, low performance load testing, or security testing, compliance testing that would want to happen in my opinion, when you're about to go into production with a release, because those are gonna take a long time. Those are very expensive. You're going to have to cut new infrastructure, run those tests, and it can become quite arduous. And you're not going to want to run those all the time. You'll have the resources, uh, builds will be slower. Uh, release will be slower. It will just become a mess. So I would want to save those for when I'm about to go into production. Instead of doing those every time I make a commit or every time I'm merging a feature ranch into a non main branch, that's the way I look at it, but everything does a different, um, there's other philosophies around it. Yeah. >>Well, I don't disagree with your build test deploy. I think if you're going to deploy the code, it needs to be tested. Um, at some level, I mean less the same. You've got, I hate the term smoke tests, cause it gives a false sense of security, but you have some mental minimum minimal amount of tests. And I would expect the developer on the feature branch to add new tests that tested that feature. And that would be part of the PR why those tests would need to pass before you can merge it, merge it to master. So I agree that there are tests that you, you want to run at different stages, but the earlier you can run the test before going to production. Um, the fewer issues you have, the easier it is to troubleshoot it. And I kind of agree with what you said, Carlos, about the longer running tests like performance tests and things like that, waiting to the end. >>The only problem is when you wait until the end to run those performance tests, you kind of end up deploying with whatever performance you have. It's, it's almost just an information gathering. So if you don't run your performance test early on, um, and I don't want to go down a rabbit hole, but performance tests can be really useless if you don't have a goal where it's just information gap, uh, this is, this is the performance. Well, what did you expect it to be? Is it good? Is it bad? They can get really nebulous. So if performance is really important, um, you you're gonna need to come up with some expectations, preferably, you know, set up the business level, like what our SLA is, what our response times and have something to shoot for. And then before you're getting to production. If you have targets, you can test before staging and you can tweak the code before staging and move that performance initiative. Sorry, Carlos, a little to the left. Um, but if you don't have a performance targets, then it's just a check box. So those are my thoughts. I like to test before every deployment. Right? >>Yeah. And you know what, I'm glad that you, I'm glad that you brought, I'm glad that you brought up Escalades and performance because, and you know, the definition of performance says to me, because one of the things that I've seen when I work with teams is that oftentimes another team runs a P and L tests and they ended, and the development team doesn't really have too much insight into what's going on there. And usually when I go to the performance team and say, Hey, how do you run your performance test? It's usually just a generic solution for every single application that they support, which may or may not be applicable to the application team that I'm working with specifically. So I think it's a good, I'm not going to dig into it. I'm not going to dig into the rabbit hole SRE, but it is a good bridge into SRE when you start trying to define what does reliability mean, right? >>Because the reason why you test performance, it's test reliability to make sure that when you cut that release, that customers would go to your site or use your application. Aren't going to see regressions in performance and are not going to either go to another website or, you know, lodge in SLA violation or something like that. Um, it does, it does bridge really well with defining reliability and what SRE means. And when you have, when you start talking about that, that's when you started talking about how often do I run? How often do I test my reliability, the reliability of my application, right? Like, do I have nightly tasks in CGI that ensure that my main branch or, you know, some important branch I does not mean is meeting SLA is meeting SLR. So service level objectives, um, or, you know, do I run tasks that ensure that my SLA is being met in production? >>Like whenever, like do I use, do I do things like game days where I test, Hey, if I turn something off or, you know, if I deploy this small broken code to production and like what happens to my performance? What happens to my security and compliance? Um, you can, that you can go really deep into and take creating, um, into creating really robust tests that cover a lot of different domains. But I liked just using build test deploy is the overall answer to that because I find that you're going to have to build your application first. You're going to have to test it out there and build it, and then you're going to want to deploy it after you test it. And that order generally ensures that you're releasing software. That works. >>Right. Right. Um, I was going to ask one last question. Um, it's going to have to be like a sentence answer though, for each one of you. Uh, this is, uh, do you lint? And if you lint, do you lent all the things, if you do, do you fail the linters during your testing? Yes or no? I think it's going to depend on the culture. I really do. Sorry about it. If we >>Have a, you know, a hook, uh, you know, on the get commit, then theoretically the developer can't get code there without running Melinta anyway, >>So, right, right. True. Anyone else? Anyone thoughts on that? Linting >>Nice. I saw an additional question online thing. And in the chat, if you would introduce it in a multi-stage build, um, you know, I was wondering also what others think about that, like typically I've seen, you know, with multi-stage it's the most common use case is just to produce the final, like to minimize the, the, the, the, the, the image size and produce a final, you know, thin, uh, layout or thin, uh, image. Uh, so if it's not for that, like, I, I don't, I haven't seen a lot of, you know, um, teams or individuals who are actually within a multi-stage build. There's nothing really against that, but they think the number one purpose of doing multi-stage had been just producing the minimalist image. Um, so just wanted to kind of combine those two answers in one, uh, for sure. >>Yeah, yeah, sure. Um, and with that, um, thank you all for the great questions. We are going to have to wrap this up and we could go for another hour if we all had the time. And if Dr. Khan was a 24 hour long event and it didn't sadly, it's not. So we've got to make room for the next live panel, which will be Peter coming on and talking about security with some developer ex security experts. And I wanted to thank again, thank you all three of you for being here real quick, go around the room. Um, uh, where can people reach out to you? I am, uh, at Bret Fisher on Twitter. You can find me there. Carlos. >>I'm at dev Mandy with a Y D E N D Y that's me, um, >>Easiest name ever on Twitter, Carlos and DFW on LinkedIn. And I also have a LinkedIn learning course. So if you check me out on my LinkedIn learning, >>Yeah. I'm at Nicola Quebec. Um, one word, I'll put it in the chat as well on, on LinkedIn, as well as, uh, uh, as well as Twitter. Thanks for having us, Brett. Yeah. Thanks for being here. >>Um, and, and you all stay around. So if you're in the room with us chatting, you're gonna, you're gonna, if you want to go to see the next live panel, I've got to go back to the beginning and do that whole thing, uh, and find the next, because this one will end, but we'll still be in chat for a few minutes. I think the chat keeps going. I don't actually know. I haven't tried it yet. So we'll find out here in a minute. Um, but thanks you all for being here, I will be back a little bit later, but, uh, coming up next on the live stuff is Peter Wood security. Ciao. Bye.

(upbeat music) >> From theCube studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a Cube Conversation. >> Hi, Lisa Martin here with Caitlin Gordon, the VP of product marketing for Dell Technologies. Caitlin, welcome back to theCube, we are excited to see you again. >> I'm very excited to be here again. So data protection in the news, what's going on? Yeah, it's been a busy year, we had our, obviously our PowerProtect DD appliance launched last year. And then this year, we have announcements on the software side, we had announcements at vMworld, some more at Dell Technologies world. And now today we're announcing even more, which is the new PowerProtect DP series appliances, the new integrated appliances, it's really exciting. So we now have our PowerProtect DP, the next generation of Data Domain. And we have our PowerProtect DP appliances, integrated appliances. And that's all about combining both protection storage, protection software in a single converged all in one offering. It's really popular with our customers today, because of the simplicity, the ability to really modernize your data protection in a very simple way, get up, really up and running quickly. And in fact, it's really the fastest growing part of the backup appliance market. >> I have read that the integrated appliances, our market is growing twice as fast as the target market. So give us a picture of what customers can expect from the new DP series. >> Yeah, it's not that just similar to actually our DD series from last year, which is there's four models in the new DP series. And it's really all about getting better performance, better efficiency, we've got new hardware assisted compression, denser drives, and all that gives us the ability to get faster backups, faster recovery. In fact, you get 38% faster backups, 45% faster recovery, more logical capacity, 30% more logical capacity 65 to one deduplication, which is just incredible. And 60,000 IOPS for instant access, so really ups the game both in performance and in efficiency. >> Those are big numbers. You mentioned a DD launched last year, contrast it with what you're announcing now, what's the significance of the DP series. >> And this is exciting for us because it does a couple things, it expands our PowerProtect appliance family, with the new DP series of integrated appliances. But at the same time, we're also announcing other important PowerProtect enhancements. on the software side, PowerProtect Data Manager, which we've been enhancing and continuing to talk about all year, also has some new improvements, the ability to deploy it in Azure, in AWS Govcloud for in-cloud protection, the enhancements that we've done with VMware that we talked about not that long ago at VMworld, about being able to integrate with storage based policy management, really automating and simplifying VMware protection. And it's really all about Kubernetes, right? And the ability to support Kubernetes as well. So not only is this an exciting appliance launch for us, but it's also the marking of yet even more enhancements on the PowerProtect Data Manager side. And all that together means that with PowerProtect, you really have a one stop shop for all of your data protection needs, no matter where the data lives. No matter what SLA, whether it's a physical virtual appliance, whether it's target or integrated. You've all got them in the PowerProtect family now. >> Excellent, all right, last question for you Caitlin. We know Dell Technologies is focused on three big waves, its cloud, VMware, and cyber recovery. Anything else you want to add here? >> cyber resiliency, cyber recovery, ransomware has really risen to the top of the list unfortunately for many organizations, and PowerProtect cyber recovery is really an important enhancement that we also have with this announcement today. We've had this offering and market for a couple years, but the exciting new enhancement here. So it is the first cyber recovery solution endorsed by Sheltered Harbor. And if you're not familiar with PowerProtect cyber recovery, it provides an automated air gapped solution for data isolation and then cyber sense provides the analytics and the forensics for discovering, diagnosing and remediating those attacks. So it's really all about ransomware protecting from or covering from those attacks, which unfortunately have become all too common for our customers today. >> Excellent news Caitlin, thanks for sharing what's new. Congratulations to you and the Dell team. >> Thank you so much Lisa. >> Okay Gordon. I'm Lisa Martin. You're watching theCube (upbeat music)

>> Announcer: From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world. This is "theCUBE' conversation. >> Hi, Lisa Martin here with Caitlin Gordon, the VP of Product Marketing for Dell Technologies. Caitlin, welcome back to 'theCUBE' I'm excited to see you again. >> I'm very excited to be here again. >> So data protection in the news, what's going on? >> Yeah you know, it's been a busy year. We had obviously our power protect DD appliance launched last year and then this year, we've have announcements on the software side. We had announcements at the VMworld some more at Dell Technologies world. And now today we're announcing even more, which is the new PowerProtect PP series appliances, the new integrated appliances. And it's really exciting. So we now have our PowerProtect DD,xx the next generation of data domain, and we have our PowerProtect DP appliances, integrated appliances. And that's all about combining both protection storage, protecting software in a single converge, all in one offering. It's really popular with our customers today because of the simplicity, the ability to really modernize your data protection in a very simple way, get up really up and running quickly. And in fact, it's really the fastest growing part of the back of appliance market. >> I have read that the integrated appliances, our market is growing twice as fast as the targeted market. So give us a picture of what customers can expect from the new DP series. >> Yeah, and it's not that dissimilar to actually our DD series from last year which is there's in four models in the new DP series. There's a 4,400 which is actually now taking the PowerProtect brand and putting that on the existing DP 4,400 and then three new appliances: the 5,900, the 8,400 and then the 8,900. And it's really all about getting better performance, better efficiency. We've got new hardware, assisted compression, denser drives, and all that gives us the ability to get faster backups, faster recovery, in fact you get 38% faster backups, 45% faster recovery, more logical capacity, 30% more logical capacity, 65 to one theater application, which is just incredible and 60,000 IOPS for instant access. So really ups the game, both in performance and an efficiency. >> Those are big numbers, you mentioned the DD launch last year, contrast it with what you're announcing now. What's the significance of the DP series? >> And that this is exciting for us because it does a couple things. It expands our PowerProtect appliance family with the new DP series of integrated appliances. But at the same time, we're also announcing other important PowerProtect enhancements. On the software side, PowerProtect data manager, which we've enhancing and continuing to talk about all year also has some new improvements. The ability deploy it in Azure, in AWS GovCloud for in-cloud protection. The enhancements that we've done with VMware that we talked about, not that long ago at VMworld about being able to integrate with storage based policy management, really automating and simplifying VMware protection. And it's really all about Kubernetes right And the ability to support Kubernetes as well. So not only is this an exciting appliance launch for us, but it's also the marketing of yet even more enhancements on the PowerProtect data manager side. And all that together means that with PowerProtect, you really have a one-stop shop for all of your data protection needs no matter where the data lives, no matter what SLA, whether it's a physical, virtual appliance, whether it's target or integrated, you've all bought them in the PowerProtect family now. >> Excellent. All right. Last question for you, Caitlin we know Dell Technologies is focused on three big waves, it's cloud VMware and Cyber Recovery. Anything else you want to add here? Yeah, I'll pick up, especially on that last one, we talke%d a little bit about the enhancements we've done with cloud in cloud data protection, longterm recovery, disaster recovery, as well as what we've done on the VMware front, really important that we continue to have that automation at simplicity with VM-ware but cyber resiliency, cyber recovery ransomware has really risen to the top of the list. Unfortunately for many organizations and PowerProtect cyber recovery is really an important enhancement that we also have with this announcement today. We've had this offer in market for a couple of years, with the exciting new enhancement here. It is the first cyber recovery solution and endorsed by Sheltered Harbor. So it is the first Cyber Recovery solution endorsed by Sheltered Harbor. And if you're not familiar with PowerProtect data, PowerProtect, if you're not familiar with PowerProtect cyber recovery, it provides an automated air gapped solution for data isolation and then cyber sense provides the analytics and the forensics for discovering, diagnosing and remediating those attacks. So it's really all about ransomware protecting from protecting from or covering from those attacks, which unfortunately have become all too common for our customers today. >> Excellent news, Caitlin. Thanks for sharing what's new congratulations to you and the Dell team. >> Thank you so much, Lisa. >> For Cait%lin Gordon I'm Lisa Martin. You're watch%ing 'theCUBE'. (calm music)

our Studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hey welcome back they're ready Jeff Rick here with the cube we're in our Palo Alto studios today to have a conversation with a really exciting company they've actually been around for a while but they've raised a ton of money and they're doing some really important work in the world in which we live today which is a lot different than the world was when they started in 2010 so we're excited to welcome to the studio he's been here on before Mohit ladee is the CEO and co-founder of Thousand Eyes mode great to see you great to see you as well as pretty to be here yeah welcome back but for people that didn't see the last video or not that familiar with Thousand Eyes tell them a little bit kind of would a thousand eyes all about absolutely so in today's world the cloud is your new data center the Internet is your new network and SAS is your new application stack and thousand eyes is built to be the the only thing that can really help you see across all three of these like it's your own private environment I love that I love that kind of setup and framing because those are the big three things and as you said all those things have moved from inside your control to outside of your control so in 2010 is that was that division I mean when you guys started the company UCLA I guess a while ago now what was that the trend what did you see what yes what kind of started it so it's really interesting right so our background as a founding company with two founders we did our PhD at UCLA in computer science and focused on internet and we were fascinated by the internet because it was just this complex system that nobody understood but we knew even then that it would meaningfully change our lives not just as consumers but even as enterprise companies so we had this belief that it's going to be the backbone of the modern enterprise and nobody quite understood how it worked because everyone was focused on your own data center your own network and so our entire vision at that point was we want people to feel the power of seeing the internet like your network that's sort of where we started and then as we started to expand on that vision it was clear to us that the Internet is what brings companies together what brings the cloud closer to the enterprise what brings the SAS applications closer to the enterprise right so we expanded into into cloud and SAS as well so when you had that vision you know people had remote offices and they would set up they would you know set up tunnels and peer-to-peer and all kinds of stuff why did you think that it was gonna go to that next step in terms of the internet you know just kind of the public Internet being that core infrastructure yes we were at the at the very early stages of this journey to cloud right and at the same time you had companies like Salesforce you had office 365 they were starting to just make it so much easier for companies to deploy a CRM you don't have to stand up these massive servers anymore its cloud-based so it was clear to us that that was gonna be the new stack and we knew that you had to build a fundamentally different technology to be able to operate in that stack and it's not just about visibility it's about making use of collective information as well because you're going from a private environment with your own data center your own private network your own application stack to something that's sitting in the cloud which is a shared environment going over the Internet which is the same network that carries cat videos that your kids watch it's carrying production traffic now for your core applications and so you need a different technology stack and you need to really sort of benefit from this notion of collective intelligence of knowing what everybody sees together as one view so I'm here I think I think Salesforce was such an important company in terms of getting enterprises to trust a SAS application for really core function which just sales right I think that was a significant moment in moving the dial was there a killer app for you guys that was you know for your customers the one where they finally said wait you know we need a different level of his ability to something that we rely on that's coming to us through an outside service so it's interesting right when we started the company we had a lot of advisors that said hey your position should be you're gonna help enterprises enforce SLA with Salesforce and we actually took a different position because what we realized was Salesforce did all the right stuff on their data centers but the internet could mess things up or enterprise companies that were not ready to move to cloud didn't have the right architectures would have some bottlenecks in their own environment because they are backhauling traffic from their London office to New York and then exiting from New York they're going back to London so all this stuff right so we took the position of really presenting thousand eyes as a way to get transparency into this ecosystem and we we believe that if we take this position if we want to help both sides not just the enterprise companies we want to help sales force we want to have enterprise companies and just really present it as a means of finding a common truth of what is actually going on it works so much better right so there wasn't really sort of one killer application but we found that anything that was real-time so if you think about video based applications or any sort of real-time communications based so the web access of the world they were just very sensitive to network conditions and internet conditions same with things that are moving a lot of data back and forth so these applications like Salesforce office 365 WebEx they just are demanding applications on the infrastructure and even if they're done great if the infrastructure doesn't it doesn't give you a great experience right and and and you guys made a really interesting insight too it's an it's an all your literature it's it's a really a core piece of what you're about and you know when you owned it you could diagnose it and hopefully you could fix it or call somebody else to fix it but when you don't own it it's a very different game and as you guys talked about it's really about finding the evidence or everyone's not pointing fingers back in and forth a to validate where the actual problem is and then to also help those people fix the problem that you don't have direct control of so it's a very different you know kind of requirement to get things fixed when they have to get fixed yeah and the first aspect of that is visibility so as an example right you generally don't have a problem going from one part of your house to another part of your house because you own the whole place you know exactly what sits between the two rooms that you're trying to get to you don't you don't have run into surprises but when you're going from let's say Palo Alto to San Francisco and you have two options you can take the 101 or 280 you need to know what you expect to see before you get on one of those options right and so the Internet is very similar you have these environments that you have no idea what to expect and if you don't see that with the right level of granularity that you would in your own environments you would make decisions that you have you know you have no control over right the visibility is really important but it's giving that lens like making it feel like a google maps of the internet that gives you the power to look at these environments like it's your private network that's the hard part right and then so what you guys have done as I understand is you've deployed sensors basically all over the Internet all at an important pops yeah an important public clouds and important enterprises etc so that you now have a view of what's going on it I can have that view inside my enterprise by leveraging your infrastructure is that accurate correct and so this is where the notion of being able to set up this sort of data collection environment is really difficult and so we have created all of this over years so enterprise companies consumer companies they can leverage this infrastructure to get instant results so there's zero implementation what right but the key to that is also understanding the internet itself and so this is where a research background comes in play because we studied we did years of research on actually modeling the internet so we know what strategic locations to put these probes that to give good coverage we know how to fill the gaps and so it's not just a numbers game it's how you deploy them where you deploy them and knowing that connectivity we've created this massive infrastructure now that can give you eyes on the internet and we leverage all of their data together so if let's say hypothetically you know AT&T has an issue that same issue is impacting multiple customers through all our different measurements so it's like ways if you're using ways to get from point A to point B if Waze was just used by your family members and nobody else it would give you completely useless information values in that collective insight right and then now you also will start to be able to until every jamel and AI and you know having all that data and apply just more machine learning to it to even better get out in front of problems I imagine as much as as is to be able to identify it so that's a really interesting point right so the first thing we have to tackle is making a complex data set really accessible and so we have a lot of focus into essentially getting insights out of it using techniques that are smarter than the brute-force techniques to get insights out and then present it in manners that it's accessible and digestible and then as we look into the next stages we're going to bring more and more things like learning and so on to take it even further right it's funny the accessible and digestible piece I've just had a presentation the other day and there was a woman from a CSO at a big bank and she talked about you know the problem of false positives and in in early days I mean their biggest issues was just too much data coming in from too many sensors and and too many false positives to basically bury people so I didn't have time to actually service the things that are a priority so you know a nice presentation of a whole lot of data that's a big difference to make it actual it is absolutely true and now that the example I'll give you is oftentimes when you think about companies that operate with a strong network core like we do they are in the weeds right which is important but what is really important is tying that intelligence to business impact and so the entire product portfolio we've built it's all about business impact user experience and then going into connecting the dots or the network side so we've seen some really interesting events and as much as we know the internet every day I wake up and I see something that surprises me right we've had customers that have done migrations to cloud that have gone horribly wrong right so we the latest when I was troubleshooting with the customer was where we saw they migrated from there on from data center to Amazon and the user experience was 10x worse than what it was on their own data the app once they moved to Amazon okay and what had happened there was the whole migration to Amazon included the smart sort of CDN where they were fronting your traffic at local sites but the traffic was going all over the place so from if a user was in London instead of going to the London instance of Amazon they were going to Atlanta they were going to Los Angeles and so the whole migration created a worse user experience and you don't have that lens because you don't see that in a net portion of that right that's what we like we caught it instantly and we were able to showcase that hey this is actually a really bad migration and it's not that Amazon is bad it's just it's been implemented incorrectly right so ya fix these things and those are all configurations all Connecticut which is so very easy all the issues you hear about with with Amazon often go back to miss configuration miss settings suboptimal leaving something open so to have that visibility makes a huge impact and it's more challenging because you're trying to configure different components of this environment right so you have a cloud component you have the internet component your own network you have your own firewalls and you used to have this closed environment now it's hybrid it involves multiple parties multiple skill sets so a lot of things can really go wrong yeah I think I think you guys you guys crystallize very cleanly is kind of the inside out and outside in approach both you know a as as a service consumer yep right I'm using Salesforce I'm using maybe s3 I'm using these things that I need and I want to focus on that and I want to have a good experience I want my people to be able to get on their Salesforce account and book business but but don't forget the other way right because as people are experiencing my service that might be connecting through and aggregating many other services along the way you know I got to make sure my customer experience is big and you guys kind of separate those two things out and really make sure people are focusing on both of them correct and it's the same technology but you can use that for your production services which are revenue generating or you can use that for your employee productivity the the visibility that you provide is is across a common stack but on the production side for example because of the way the internet works right your job is not just to ensure a great performance in user experience your job is also to make sure that people are actually reaching your site and so we've seen several instances where because of the way internet works somebody else could announce that their google.com and they could suck a bunch of traffic from the Internet and this happens quite routinely in the notion of what is now known as DP hijacks or sometimes DNS hijacks and the the one that I remember very well is when there was the small ISP in Nigeria that announced the identity of the address block for Google and that was picked up by China Telecom which was picked up by a Russian telco and now you have Russia China and Nigeria in the path for traffic to Google which is actually not even going to Google's right those kinds of things are very possible because of the way the internet how fast those things kind of rise up and then get identified and then get shut off is this hours days weeks in this kind of example so it really depends because if you are let's say you were Google in this situation right you're not seeing a denial of service attack T or data centers in fact you're just not seeing traffic running it because somebody else is taking it away right it's like identity theft right like I somebody takes your identity you wouldn't get a mail in your inbox saying hey your identity has been taken back so I see you have to find it some other way and usually it's the signal by the time you realize that your identity has been stolen you have a nightmare ahead of you all right so you've got some specific news a great great conversation you know it's super insightful to talk people that are in the weeds of how all the stuff works but today you have a new a new announcement some new and new offering so tell us about what's going on so we have a couple of announcements today and coming back to this notion of the cloud being a new data center the internet your new network right two things were announcing today is one we're announcing our second version of the cloud then benchmark performance comparison and what this is about is really helping people understand the nuances the performance difference is the architecture differences between Amazon Google ad your IBM cloud and Alibaba cloud so as you make decisions you actually understand what is the right solution for me from a performance architecture standpoint so that's one it's a fascinating report we found some really interesting findings that surprised us as well and so we're releasing that we're also touching on the internet component by releasing a new product which we call as Internet insights and that is giving you the power to actually look at the internet more holistically like you own the entire internet so that is really something we're all excited about because it's the first time that somebody can actually see the Internet see all these connections see what is going on between major service providers and feel like you completely owned the environment so are people using information like that to dynamically you know kind of reroute the way that they handle their traffic or is it more just kind of a general health you know kind of health overview you know how much of it do I have control over how much should I have control over and how much of I just need to know what's going on so yeah so in just me great question so the the best way I can answer that is what I heard CIO say in a CIO forum we were presenting it where they were a customer it's a large financial services customer and somebody asked the CIO what was the value of thousand I wasn't the way he explained it which was really fascinating was phase one of thousand eyes when we started using it was getting rid of technical debt because we would keep identifying issues which we could fix but we could fix the underlying root cause so it doesn't happen again and that just cleared the technical debt that we had made our environment much better and then we started to optimize the environments to just get better get more proactive so that's a good way to think about it when you think about our customers most of the times they're trying to just not have their hair on fire right that's the first step right once we can help them with that then they go on to tuning optimising and so on but knowing what is going on is really important for example if you're providing a.com service like cube the cube comm right it's its life and you're providing it from your data center here you have two up streams like AT&T and Verizon and Verizon is having issues you can turn off that connection and read all your customers back live having a full experience if you know that's the issues right right the remediation is actually quite quite a few times it's very straight forward if you know what you are trying to solve right so do you think on the internet insights this is going to be used just more for better remediation or do you think it's it's kind of a step forward and getting a little bit more proactive and a little bit more prescriptive and getting out ahead of the issues or or can you because these things are kind of ephemeral and come and go so I think it's all of the about right so one the things that the internet insights will help you is with planning because as you expand into new geo so if you're a company that's launching a service in a new market right that immediately gives you a landscape of who do you connect with where do you host right now you can actually visualize the entire network how do you reach your customer base the best right so that's the planning aspect and if you plan right you would actually reduce a lot of the trouble that you see so we had this customer of ours that was deploying Estevan software-defined man in there a she offices and they used thousand eyes to evaluate two different ISPs that they were looking at one of them had this massive time-of-day congestion so every time every day at nine o'clock the latency would get doubled because of congestion it's common in Asia the other did not have time of day congestion and with that view they could implement the entire Estevan on the ice pea that actually worked well for them so planning is important part of this and then the other aspect of this is the thing that folks often don't realize is internet is not static it's constantly changing so you know AT&T may connect to where I is in this way it connects it differently it connects to somebody else and so having that live map as you're troubleshooting customer experience issues so let's say you have customers from China that are having a ton of issues all of a sudden or you see a drop of traffic from China now you can relate that information of where these customers are coming from with our view of the health of the Chinese internet and which specific ISPs are having issues so that's the kind of information merger that simply doesn't happen today right promote is a fascinating discussion and we could go on and on and on but unfortunately do not have all day but I really like what you guys are doing the other thing I just want to close on which which I thought was really interesting is you know a lot of talked about digital transformation we always talk about digital transformation everybody wants a digital transfer eyes it but you really boiled it down into really three create three critical places that you guys play the digital experience in terms of what what the customers experience you know getting to cloud everybody wants to get to cloud so one can argue how much and what percentage but everybody's going to cloud and then as you said in this last example the modern when as you connect all these remote sites and you guys have a play in all of those places so whatever you thought about in 2010 that worked out pretty well thank you and we had a really strong vision but kudos to the team that we have in place that has stretched it and really made the most out of that so excited good job and thanks for for stopping by sharing the story thank you for hosting always fun to be here absolutely all right well he's mo and I'm Jeff you're watching the cube when our Palo Alto studio is having a cube conversation thanks for watching we'll see you next time [Music]

hello everybody and thank you for joining us today at the virtual Vertica BBC 2020 today's breakout session is entitled the road to autonomous database management how Domo is delivering SLA for less my name is su LeClair I'm the director of marketing at Vertica and I'll be your host for this webinar joining me is Ben white senior database engineer at Domo but before we begin I want to 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&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 aren't able to address or drew our best to answer them offline alternatively you can visit vertical forums to post your questions there after the session our engineering team is planning to join the forum to keep the conversation going also as a reminder you can maximize your screen by clicking the double arrow button in the lower right corner of the slide and yes this virtual session is being recorded and will be available to view on demand this week we'll send you notification as soon as it's ready now let's get started then over to you greetings everyone and welcome to our virtual Vertica Big Data conference 2020 had we been in Boston the song you would have heard playing in the intro would have been Boogie Nights by heatwaves if you've never heard of it it's a great song to fully appreciate that song the way I do you have to believe that I am a genuine database whisperer then you have to picture me at 3 a.m. on my laptop tailing a vertical log getting myself all psyched up now as cool as they may sound 3 a.m. boogie nights are not sustainable they don't scale in fact today's discussion is really all about how Domo engineers the end of 3 a.m. boogie nights again well I am Ben white senior database engineer at Domo and as we heard the topic today the road to autonomous database management how Domo is delivering SLA for less the title is a mouthful in retrospect I probably could have come up with something snazzy er but it is I think honest for me the most honest word in that title is Road when I hear that word it evokes for me thoughts of the journey and how important it is to just enjoy it when you truly embrace the journey often you look up and wonder how did we get here where are we and of course what's next right now I don't intend to come across this too deep so I'll submit there's nothing particularly prescient and simply noticing the elephant in the room when it comes to database economy my opinion is then merely and perhaps more accurately my observation the office context imagine a place where thousands and thousands of users submit millions of ad-hoc queries every hour now imagine someone promised all these users that we could deliver bi leverage at cloud scale in record time I know what many of you should be thinking who in the world would do such a thing of course that news was well received and after the cheers from executives and business analysts everywhere and chance of Keep Calm and query on finally started to subside someone that turns an ass that's possible we can do that right except this is no imaginary place this is a very real challenge we face the demo through imaginative engineering demo continues to redefine what's possible the beautiful minds at Domo truly embrace the database engineering paradigm that one size does not fit all that little philosophical nugget is one I would pick up while reading the white papers and books of some guy named stone breaker so to understand how I and by extension Domo came to truly value analytic database administration look no further than that philosophy and what embracing it would mean it meant really that while others were engineering skyscrapers we would endeavor to build Datta neighborhoods with a diverse kapala G of database configuration this is where our journey at Domo really gets under way without any purposeful intent to define our destination not necessarily thinking about database as a service or anything like that we had planned this ecosystem of clusters capable of efficiently performing varied workloads we achieve this with custom configurations for node count resource pool configuration parameters etc but it also meant concerning ourselves with the unattended consequences of our ambition the impact of increased DDL activities on the catalog system overhead in general what would be the management requirements of an ever-evolving infrastructure we would be introducing multiple points of failure what are the advantages the disadvantages those types of discussions and considerations really help to define what would be the basic characteristics of our system the database itself needed to be trivial redundant potentially ephemeral customizable and above all scalable and we'll get more into that later with this knowledge of what we were getting into automation would have to be an integral part of development one might even say automation will become the first point of interest on our journey now using popular DevOps tools like saltstack terraform ServiceNow everything would be automated I mean it discluded everything from larger multi-step tasks like database designs database cluster creation and reboots to smaller routine tasks like license updates move-out and projection refreshes all of this cool automation certainly made it easier for us to respond to problems within the ecosystem these methods alone still if our database administration reactionary and reacting to an unpredictable stream of slow query complaints is not a good way to manage a database in fact that's exactly how three a.m. Boogie Nights happen and again I understand there was a certain appeal to them but ultimately managing that level of instability is not sustainable earlier I mentioned an elephant in the room which brings us to the second point of interest on our road to autonomy analytics more specifically analytic database administration why our analytics so important not just in this case but generally speaking I mean we have a whole conference set up to discuss it domo itself is self-service analytics the answer is curiosity analytics is the method in which we feed the insatiable human curiosity and that really is the impetus for analytic database administration analytics is also the part of the road I like to think of as a bridge the bridge if you will from automation to autonomy and with that in mind I say to you my fellow engineers developers administrators that as conductors of the symphony of data we call analytics we have proven to be capable producers of analytic capacity you take pride in that and rightfully so the challenge now is to become more conscientious consumers in some way shape or form many of you already employ some level of analytics to inform your decisions far too often we are using data that would be categorized as nagging perhaps you're monitoring slow queries in the management console better still maybe you consult the workflows analyzing how about a logging and alerting system like sumo logic if you're lucky you do have demo where you monitor and alert on query metrics like this all examples of analytics that help inform our decisions being a Domo the incorporation of analytics into database administration is very organic in other words pretty much company mandated as a company that provides BI leverage a cloud scale it makes sense that we would want to use our own product could be better at the business of doma adoption of stretches across the entire company and everyone uses demo to deliver insights into the hands of the people that need it when they need it most so it should come as no surprise that we have from the very beginning use our own product to make informed decisions as it relates to the application back engine in engineering we call it our internal system demo for Domo Domo for Domo in its current iteration uses a rules-based engine with elements through machine learning to identify and eliminate conditions that cause slow query performance pulling data from a number of sources including our own we could identify all sorts of issues like global query performance actual query count success rate for instance as a function of query count and of course environment timeout errors this was a foundation right this recognition that we should be using analytics to be better conductors of curiosity these types of real-time alerts were a legitimate step in the right direction for the engineering team though we saw ourselves in an interesting position as far as demo for demo we started exploring the dynamics of using the platform to not only monitor an alert of course but to also triage and remediate just how much economy could we give the application what were the pros and cons of that Trust is a big part of that equation trust in the decision-making process trust that we can mitigate any negative impacts and Trust in the very data itself still much of the data comes from systems that interacted directly and in some cases in directly with the database by its very nature much of the data was past tense and limited you know things that had already happened without any reference or correlation to the condition the mayor to those events fortunately the vertical platform holds a tremendous amount of information about the transaction it had performed its configurations the characteristics of its objects like tables projections containers resource pools etc this treasure trove of metadata is collected in the vertical system tables and the appropriately named data collector tables as a version 9 3 there are over 190 tables that define the system tables while the data collector is the collection of 215 components a rich collection can be found in the vertical system tables these tables provide a robust stable set of views that let you monitor information about your system resources background processes workload and performance allowing you to more efficiently profile diagnose and correlate historical data such as low streams query profiles to pool mover operations and more here you see a simple query to retrieve the names and descriptions of the system tables and an example of some of the tables you'll find the system tables are divided into two schemas the catalog schema contains information about persistent objects and the monitor schema tracks transient system States most of the tables you find there can be grouped into the following areas system information system resources background processes and workload and performance the Vertica data collector extends system table functionality by gathering and retaining aggregating information about your database collecting the data collector mixes information available in system table a moment ago I show you how you get a list of the system tables in their description but here we see how to get that information for the data collector tables with data from the data collecting tables in the system tables we now have enough data to analyze that we would describe as conditional or leading data that will allow us to be proactive in our system management this is a big deal for Domo and particularly Domo for demo because from here we took the critical next step where we analyze this data for conditions we know or suspect lead to poor performance and then we can suggest the recommended remediation really for the first time we were using conditional data to be proactive in a database management in record time we track many of the same conditions the Vertica support analyzes via scrutinize like tables with too many production or non partition fact tables which can negatively affect query performance and life in vertical in viral suggests if the table has a data a time step column you recommend the partitioning by the month we also can track catalog sizes percentage of total memory and alert thresholds and trigger remediations requests per hour is a very important metric in determining when a trigger are scaling solution tracking memory usage over time allows us to adjust resource pool parameters to achieve the optimal performance for the workload of course the workload analyzer is a great example of analytic database administration I mean from here one can easily see the logical next step where we were able to execute these recommendations manually or automatically be of some configuration parameter now when I started preparing for this discussion this slide made a lot of sense as far as the logical next iteration for the workload analyzing now I left it in because together with the next slide it really illustrates how firmly Vertica has its finger on the pulse of the database engineering community in 10 that OS management console tada we have the updated work lies will load analyzer we've added a column to show tuning commands the management console allows the user to select to run certain recommendations currently tuning commands that are louder and alive statistics but you can see where this is going for us using Domo with our vertical connector we were able to then pull the metadata from all of our clusters we constantly analyze that data for any number of known conditions we build these recommendations into script that we can then execute immediately the actions or we can save it to a later time for manual execution and as you would expect those actions are triggered by thresholds that we can set from the moment nyan mode was released to beta our team began working on a serviceable auto-scaling solution the elastic nature of AI mode separated store that compute clearly lent itself to our ecosystems requirement for scalability in building our system we worked hard to overcome many of the obstacles they came with the more rigid architecture of enterprise mode but with the introduction is CRM mode we now have a practical way of giving our ecosystem at Domo the architectural elasticity our model requires using analytics we can now scale our environment to match demand what we've built is a system that scales without adding management overhead or our necessary cost all the while maintaining optimal performance well we're really this is just our journey up to now and which begs the question what's next for us we expand the use of Domo for Domo within our own application stack maybe more importantly we continue to build logic into the tools we have by bringing machine learning and artificial intelligence to our analysis and decision making really do to further illustrate those priorities we announced the support for Amazon sage maker autopilot at our demo collusive conference just a couple of weeks ago for vertical the future must include in database economy the enhanced capabilities in the new management console to me are clear nod to that future in fact with a streamline and lightweight database design process all the pieces should be in place versions deliver economists database management itself we'll see well I would like to thank you for listening and now of course we will have a Q&A session hopefully very robust thank you [Applause]

>> From the SiliconAngle media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to theCUBE's Boston-area studio. Welcome back to the program, CUBE alum, Ashok Ramu, Vice President and General Manager of Cloud at Actifio, great to see you. >> Happy New Year, Stu, happy to be here. >> 2020, hard to believe it said, it feels like we're in the future here. And talking about future, we've watched Actifio for many years, we remember when copy data management, the category, was created, and really, Actifio, we were talking a lot before Cloud was the topic that we spent so much talking about, but Actifio has been on this journey with its customers in Cloud for many years, and of course, that is your role is working, building the product, the team working all over it, so give us a little bit of a history, if you would, and give us the path that led to 10C announcement. >> Sure thing. We started the Cloud journey early on, in 2014 or 2013-ish, when Amazon was the only Cloud that really worked. We built our architecture, in fact, we took our enterprise architecture and put it on the Cloud and realized, "Oh my god," you know, it's a world of difference. The economics don't work, the security model is different, the scale is different. So, I think with the 8.0 version that came out in 2017, we really kind of figured out the architecture that worked for large enterprises, particularly enterprises that have diverse data sets and have requirements around, you know, marrying different applications to data sets anywhere they want, so we came up with efficient use of object, we came up with the capability of migrating workloads, taking VMware VMs, bringing up on Azure, bringing up on DCP, et cetera. So that was the first foray into Actifio's Cloud, and since then, we've been just building strength after strength, you know. It's been a building block, understanding our customers, and thank you to the customers and the hyperscalers that actually led us to the 10C release. So this, I believe, we've taken it up a notch wherein, we understand the Cloud, we understand the infrastructure, the software auto-tunes itself to know where it's running on, taking the guessing game out of the equation. So 10C really represents what we see as a launchpad for the rest of the Cloud journey that Actifio's going to embark upon. We have enabled a number of new use cases like AI and ML, data transformation is key, we tackled really complicated workloads like HANA and Sybase and MySQL, et cetera, and in addition to that, we also adopt different native Cloud technologies, like Cloud snapshots, like recovery orchestration of the Cloud, et cetera. >> Yeah, I think it's worth reminding our audience there that Actifio's always been software. And when you talk about, you know, I think back to 2013, 2014, it was the public Cloud versus the data center, and we have seen the public Cloud in many ways looks more and more like what the enterprise has been used to. >> Absolutely. >> And the data centers have been trying to Cloud-ify for a number of years, and things like containerization and Kubernetes is blurring the line, and of course, every hyperscaler out there now has something that reaches their public Cloud into the data center and of course, technologies like VMware are also extending into the public Cloud, or, SAP now, of course is all of the Cloud environment. So with hybrid Cloud and multi-Cloud as kind of the waves of driving, help us understand that Actifio lives in all of these environments, and they're all a little bit different, so how does Actifio make sure that it can provide the functionality and experience that users want, regardless of where it is? >> Absolutely, you said it right. Actifio has always been a software company. And it is our customers that showed us, by Cloudifying their data centers, that we had to operate in the Cloud. So we had on premises VMware Clouds, not before we had Amazon and Azure and Google. So that evolution started much early on. And so, from what, you know, Actifio's a very customer-driven company, be it, you know, all segments of the company are driven by the customers, and in 2019, and even before, when you see a strong trend to migrate workloads, to move workloads, we realized, there is a significant opportunity, because the hardest thing to migrate is the volume of data because it's ever-changing, and it is ever-growing. So, the key element of neutrality was the application itself. Microsoft SQL's a SQL no matter how you run it. It could be on a big Windows machine in your data center or a NGCP, it makes no difference. So Actifio's approach to start application down basically gave us the freedom to say, we're going to create SQL to SQL. I don't know if you're running in Azure, Google, DOP data center, or AliCloud, it makes no difference to me. I understand SQL, I understand SQL's availability groups, I understand logs, I can capture it and give it back to you, so when we took that approach, it kind of automatically gave us infrastructure neutrality, really didn't care. So when we have a conversation with a customer, it basically goes around lines of, "Okay, Mr. Customer, how much data do you have? And what are your key applications? Can you categorize them in terms of priority?" It usually comes out to be databases are the crown jewels, so they're the number one priority in terms of data management, migration, test Av, et cetera. And then, we basically drill down into the ecosystem the databases live into. So, because we walk application down, the conversation is the same whether the customer is in the data center, or in the Cloud. So that is how we've evolved, and that's how we're thinking from a product standpoint, from a support standpoint, and then the overall company is built that way. So it makes it easy for us to adapt a new platform that comes in. So, when you talked about, you know, how does, each Cloud is different, you're absolutely right, the security concepts are different, right? Microsoft is built on active directory, Google is built on something very different. So how do you utilize and how do you make this work? We do have an infrastructure layer that basically provides Cloud-specific capabilities for various Cloud platforms. And that has gotten to a point where it understands and tunes itself from a security standpoint and a performance standpoint. Once that's taken care of, the rest of the application stack, which is over 90% of our software, stays the same, there's no change. And so that is how we kind of tackle this. Because the ecosystem we live in, we have to keep up with two people. We have to keep up with the infrastructure people who are making it bigger, faster, and we also have to keep up with the application people who are making it fancier and more complicated. So that's unfortunately the ecosystem we live in, and taking this approach has given us a mechanism to insulate us from a lot of the complexities of these two environments. >> Yeah, that's great, 'cause when you talk to customers and you say, "What's going on in your environment," change is difficult. So, how many different pieces of what I'm doing do I need to move to be able to take advantage of the modern economics. On the one hand, you know, if I have an application and I like it, well, maybe I could just lift and shift it, but if I'm just lifting, shifting, I'm not necessarily taking advantage of the full Cloud native environments, but I need to make sure that my data is protected, backup, you mentioned security, are of course the top concerns, so. It sounds like, in many ways, you're talking, helping customers work through some of those initiatives, being able to take advantage of new environments, but not need to completely change everything. Maybe, I'd love to hear a little bit, when you talk about the developers and DevOps initiatives that are happening inside customers, where does that impact, where does that connect with what Actifio's doing? >> Well, that's a great question. So, let me start with a real customer example. We have this customer, SEI Investments, who basically, their business model is to grow by acquisition, so they're adding on tens, hundreds of developers every quarter. So it's impossible to keep up with infrastructure needs when you grow at that pace. They decided to adopt a Cloud platform. And with each Cloud platform comes some platform-specific piece that all these developers now have to re-tool themselves. So, I'm a developer, I used to come in the morning, open up my machine and start working away on the application, now I have to do something different, and if there is 300 of me, and the cost of moving to the Cloud was a lot less than training the developers. It was much harder to train the developers because it has been ongoing process. So we were presented the challenge of how do you avoid it? So, when we are able to separate the application layer from the data layer, because of the way we operate, what we present as a solution was to say, just move your, what is the heaviest layer you have? That's the database, okay. And what are the copies you're creating? I'm creating hundreds of copies of my Oracle database, okay. Let's just move that to the Cloud. All of the front-end application doesn't see a change, thanks to the great infrastructure work the Cloud providers do, you add 10 Gigabyte to everywhere. So network is not a problem, computer's not a problem, it's just available on an API call, so you provision that. All they did was a data movement, moved it from Point A to Point B, gives you the flexibility to spend up any number of copies you want in the Cloud, now, your developer tool sets haven't changed, so there's no training required for developers, but from an operations standpoint, you've completely eased the burden of creating a hundred more copies every month, because Cloud is built for that. So you take the elasticity of the Cloud, advantage of that, and provide the data in the last mile to the Cloud, thereby, developers, they will access the application with the same level of ease. So, that is the paradigm we're seeing, we're seeing, you know, in some of our customers, there is faster and better storage provision for Actifio because there are 190 developers working off Actifio, where there's only about a handful of people running production. So, it's a paradigm shift is where we see it. And the pace at which we bring up the application wherein we're able to bring up 150 terabyte article database in three hours. Before Actifio, it used to be, maybe, 30 days, if you were lucky. So it's not just an order of magnitude, it's what you can do with that data, is where we're seeing the shift going to. >> Yeah, it's interesting, when you go back and look at some of the changes that have happened in the Cloud, Cloud storage was one of the earliest discussed use cases there, and backup to the Cloud was one of the earlier pieces of the Cloud storage discussion. Yet, we've seen changes and maturation into what can actually be done, explain a little bit how Actifio enables even greater functionality when you're talking about backup to the Cloud. >> Absolutely. You know, the object storage technology, it's probably the most scalable and stable piece of storage known to mankind, because nobody can build that level of scale that Amazon, Azure, and Google have put into it. From a security standpoint, performance standpoint, and scale standpoint. So I'm able to drop my data in Boston and pick it up in Tokyo seamlessly, right? That's unheard of before. And the biggest impediment to that, was a lot of legacy application data didn't know how to consume this object storage. So what Actifio came up with on onboard technology was to light up the object storage for everybody, and basically make it a performance neutral platform, wherein you take the guessing game out of the customer. The customer doesn't need to go research S3 or Google Nearline or Google Persistent Disk and say I want ten copies there versus five copies there, Actifio figures it out for you. You give us your SLA, you give us your RTOs and RPOs, and we tell you, okay, this is the most cost effective way to store your data. You get the multi-year retention for free, you get the GDPR, appchafe and protection for free, you get the geo-redundancy for free. All this is built into the platform. In addition, you also can run DevOps off the object store. You can run DR off the object store. So we enabled a lot of the legacy use cases using this new technology, so that is kind of where we see the cusp, wherein, in the Cloud, there's always a question and a debate, does D-doop make sense? D-doop consumes a lot of compute, takes a lot of memory, you need to have that memory and compute whether you want it or not. We're seeing a lot more adoption of encryption, where the data is encrypted at source. When you encrypt data, D-doop is just a big compute-churning platform, it doesn't do much for you. So we went through this debate actively, I think four or five years ago, and we figured out, object store's the way to go. You cannot get storage, I mean, it's a buck a terabyte in Google, and dropping. How can you get storage that's reliable, scalable, at a lower cost? All we had to do was actuate the use of that storage, which is what we did. >> Yeah. I'm just laughing a little bit because, you know, gosh, I think back a dozen years ago, the industry knew that the future of storage would be object, yet it's taken a long time to really be able to leverage it and use it, and the Cloud, the hyperscalers of course, have been a huge enabler on that, but we don't want customers to have to think about that it's object underneath, and that's the bridging the gap that I think we've been looking for. There, what else. We talk about really being able to extract the value out of Cloud, you know, data protection, disaster recovery, migrations are all things that are top of mind. >> Yeah, absolutely. All those use cases, and we're seeing some of the top rating CIOs talk about AI and ML. We've had a couple of customers who want to basically take their manufacturing data from remote sites and pump it into Google bit query. Now we all know manufacturing happens in Taiwan and Singapore and all those locations, now how do you take data from all those applications, normalize it, and pump it into Google bit query and get your predictable results on a quarterly basis, it's a challenge. Because the data volumes are large. So with our Cloud technology and our onboard capability, we're able to funnel data directly into Google Nearline, and on a quarterly basis, on a scheduled basis, transform it, push it into bit query, and bring out the results for the end user. So that journey is pretty transformated, from a customer standpoint. What they used to have five people do maybe once a year, now with a push of a button happens every quarter. So it's a change in how the AI and ML analytics evolve. The other element is also you know, our partnership with IBM, we're working very closely with their Cloud bag for data. Cloud bag for data is an awesome platform built to analyze any kind of data that you might have. With Actifio's normalization platform, you basically can feed any data into Actifio and it presents a unified interface into the slow pack, so you can build your analytics workloads very quickly and easily. >> So we've talked a lot about Cloud, one of the other C's of course in 10C is containers, if we look at containerization, when it first started, it was stateless applications, most applications that are running in containers are running for very short period of time, so help us understand where Actifio fits there, what's the problem statement that you are solving? >> Oh, absolutely. So containers are coming up, up and coming and out of reality, and as we see more applications flow into containers, you see the data lives outside the container. Because containers are short-lived, they're microservices, they come up and they go down, and the state is maintained in a storage platform outside the container, so Actifio tackles containers by taking the data protection strategy we have for the storage platform already, Bell defined, but enhancing the data presentation into the container as it comes up. So a container can be brought up in seconds, maybe less. But the container is only brought to life when it can lead to data and start working again, so that's the bridge Actifio actuates. So we understand, you know, the architecture of how a container is put together, how the container system is put together, and basically, we marry the storage and the application consistent in the storage into the container so that the container's databases, or applications, come to life. >> And that could be in a customer's data center, in a public Cloud, Kubernetes enabled, all of that? >> Absolutely, it can be anywhere, and with 10C, what we have done is we've also integrated with Cloud Native Snapshot, so if you talk about net neutrality for the container platform, if it's on premises, we have all kinds of access to the storage, the infrastructure, and the platforms so our processing is very different. If you take it to the Cloud, let's say Google, Google Kubernetes platform is fairly, it's a black box. You get some storage, and you get containers. And you have an API access to the storage. So in Google, we automatically autotune and start taking the Google snapshots to take the storage perfection, so that's the other way we've kind of neutralized the platform. >> Yeah, you've got a, thinking about it just from a customer's standpoint, one of the big challenges there is they've got everything from their big monoliths, they're big databases, through these microservice Cloud native architectures there, and it sounds like you know, is that just one of the fundamental architectural designs to make sure that you can span across those environments and give customers a common look and feel between those environments? >> Absolutely. The single pane of glass is a big askt and a big focus for us, not just across infrastructure, it's across geos and across all platforms. So you could have workloads running AIX6, VMware, in the Cloud, all the way through containers, and manage it all to a single console, to know when was the last good backup, how many copies of the database am I running, and each of these databases could have their own security constructs. So we normalize all of those elements and put them in a single console. >> Okay, 10C, shipping today? >> 10C shipping today, we have early access to a few customers, the general availability releases possibly in the February timeframe. >> Okay, and if I'm an existing Actifio customer, what's the path for me to get to 10C? >> Our support will reach out and do a simple software upgrade, it's available on all Cloud platforms, it's available everywhere, so you will see that on all the marketplaces and the regular upgrade process will get you that. >> Okay, and if I'm not an Actifio customer today, how easy is it for me to try this out? >> Oh, it is very easy, with our Actifio go SAS platform, it's a one-click download, you can download and try it out, try all the capabilities of the platform, it's also available on all the Cloud marketplaces for you to go and access that. >> All right, well, Ashok, a whole lot of pieces inside of 10C, congratulations to you and the team for building that, and definitely look forward to hearing more about the customer deployments. >> Thank you, we have exciting times ahead. >> All right. Lots more coverage from theCUBE throughout 2020, be sure to check out theCUBE.net, I'm Stu Miniman, thanks for watching theCUBE. (techno music)

>>Live from Denver, Colorado. It's the cube covering comm vault. Go 2019 brought to you by. >>Hey, welcome back to the cube Lisa Martin with Steven and Amanda. We are covering combo go 19 in Colorado day two of our coverage and we're excited to welcome a successful comm vault customer to the cube. We have from the main stage this morning, Matthew mag meet data center, director of Sonic healthcare. Matthew, welcome. Thank you for having me. This is so exciting. Oh good. We're excited to have you. So you got to, you are, you're, as your pen says, a combo customer champion. >>I am a customer champion a, I've kind of prided myself on that for the last few years. Uh, I like to get involved in the community and kind of help the other newcomers to come volt as well. As better my understanding and try to give the guys on the other end of the support line and break. >>So before we dig into Sonic and what you guys are doing and how you're working with combo, give our audience an overview of Sonic healthcare, what you guys do, where you're based, all that good background stuff. Okay. >>So I worked for a Sonic healthcare USA, so that's obviously in the United States. Uh, we are an anatomical and clinical pathology laboratory company. Um, we are based, uh, West coast central and East coast of the United States and we work with hospitals, doctor's office to provide, you know, quick and reliable laboratory results. >>So this is patient data. Yes. We think of, we think of data as I'm sure you do as well. It's the lifeblood. It's the new oil. It's all the things, right? That you hear the new bacon. It's the new bacon is that was like your quote? I saw that combo last year. >>Yeah, they had, they had teachers last year with that data. Yeah. Data is the new bacon. >>Well it's, it's critical, you know, regardless of if you're for Kim comparing it to bacon, I do like that. But it's also, there's the proliferation of it is hard to manage. Tell us a little bit about the it environment at Sonic. You guys have been using combo for about four years, but give us an overview of what you were working with before and how, what may be some of the compelling events were. >>So coming on board with Sonic, uh, the combo rollout was relatively new. We didn't, I didn't really come into a preexisting environment. It was like, okay, this is, this is what we're going to use. I need you to learn it and run with it, make sure that it works. Right. And um, you know, coming from other companies that had different software applications, I was always in charge of the disaster recovery. That's always been kind of like a, a beating heart for me. >>You're the Dr. Guy. It is apparently, >>it's really hard to find someone who's excited about backups. So I've put, it's like, yes, please take it. So I'm coming in and being able to mold this application to kind of how I wanted it was a little like touch and go at first because we had people out of our, um, overseas office that were, uh, handling already and is, they kind of set the stage of how they wanted it to go. But, you know, things change. We've got to kind of move things as we go, but I kind of owe a lot to them to kind of really introducing me to combo. >> So Matthew, one of the things that we've really enjoyed talking about at this show is everybody's ready. They're born ready, they know what they're doing, what it's preparing for when things do fail. So you talked a little bit on stage about some of those times when things fail and how today you're able to be here and you're, the other person in the D R group is here and you don't have to worry about walking away from the office and you know, having, you know, I guess not a Pedro anymore, but getting that call. >>Yeah, they need to be there. So my cell phone. But yeah, so bring us through some of those, you know, failure scenarios. We are always trying different things. You know, combo does offer a wide array of different solutions they have for plans and one of them is their active directory plan. And I'm leaning towards this cause this is my most recent failure is, you know, we were just, I've always had issues with active directory testing. The fail over and my first attempt at it was a failure. But I learned so much off the bat that I'm actually comfortable now that there might be a few tweaks that we have to do. By worst case scenario, we'd definitely be able to get it back online without any issue. But if we would've gone into it without testing, without that failure, who knows what could have happened. It could've been just a resume generating event, you know? >>Well, so you, you Stu alluded to it and what you mentioned in the keynote was, Hey, my other only other Dr. Guy is here in the audience. So I actually, I have >>team a data center team and we're all in charge. It's eight, eight people and we're, we're in charge of the disaster recovery. But, uh, the old gentleman who's with me is the only other one who's, uh, uh, done a lot of the combo training. He comes to Kai, he's been to all three combos, goes with me and uh, he's, he's probably the, if I'm not around, he's the next in line to take that. So if there's a major issue it would be one of us that they would contact by. We're both here >> and you're both here. Well that actually speaks volumes. It does. And we're comfortable and you know, we've been checking email for things but you know, everything's smooth sailing so far. >>I think I saw a quote from you, I think it was in a video where you said before it was like having a newborn. >>Absolutely, absolutely. I used to check like sign in. It's like 10 o'clock every single night for the first year that I worked for sign cause I was petrified cause you know, I knew that I was backing stuff up but I don't know, was it still running with it still being backed up? Did it pause? Was it causing performance issues on the other end? There were so many what ifs and I just, I was, I was a mess. I was a nervous wreck constantly, you know, working till one or two in the morning and then go to bed and then eagerly get up and start checking stuff even before I left the house, you know? And I'm like, Oh, okay, that's finished. But now it's like, yeah, I know I finished not worried about Matthew. I think back to early in my career it was the dreaded backup window is, you know, when am I going to be able to get that in there? >>Can I finish the backup in the window that I have? And we've mostly gotten beyond that. But you know, there's so many new now we were just talking with Sandy Hamilton who was on stage before you about some of that automation. Really great automation sounds good, but there's gotta be a little bit of fear. It's like shit, you know, talking about like texting, I said like we've all texted the wrong thing or the wrong person or you had the wrong person. So tell us your thoughts about how automation is impacting your world and how calm voltage. >> I actually have very little automation workflow running through comm vault right now. A lot of the stuff that we do automation wise lies on the VMware side. Um, so that's, that's been good. I haven't really implemented a lot just because I personally am not comfortable with it yet. >>I'm not against it. It's just something that I haven't really trained myself enough to say I'm going to leave and let this run by itself. I'm still like, Oh no, this could be better. This could be better. This can be better. So until I'm 100% comfortable with that, I think we'll just leave it at a semiautomated task of just, sorry, you said something down the road that you're absolutely even even sitting in keynote yesterday and listening about the Alexa automation and SMS tax, I like writing in a piece of paper to test that because it's something that I've always wanted and ever since combo go last year when they were using Alexa to check SLA and RPO and RTO, I'm like, I want to be able to do that. So that's definitely down the road, but it's on the back burner right now. >>So give us a landscape view distributed organization. You talked about your base in the U S but all of the different clinics and organizations that you work with, are you living in this multi-cloud world? >>So, uh, we are pretty much zero cloud initiative company. Yeah. I'm actually trying to work on a slogan, Oh no, cloud zero cloud and proud or something like that. But I'm not 100% sure. It's definitely not out of the question. But with FedRAMP co compliancy and HIPAA, there's just a lot of regulation between the data that we have for the U S that transmits back and forth, let's say Australia or Ireland or something like that. There's certain regulations that we have to deal with and uh, in the cloud there's, there's very few options of where you can actually have those servers. So it's right now, you know, on prem is kind of, it's kind of our jam. >>So as a lot of organizations are going through FedRAMP certification, I was just at one of Dell's events the other week. They're going through it. I know some other like e-signature companies are doing, a lot of companies are, are you paying attention to that? Is that something that you think in the future might provide more confidence? >>Completely transparent. It's something I should be paying more attention to that I'm, I've just, I really haven't really done as much research as I should have and you know, I take full responsibility for that. But at the same time, you know, there's, there's a lot of other things going on in the U S that until we implement something of that nature, I don't really think that I'm really too concerned about it. So Matthew, you've been to a few of these events. Last one, last year there was a lot of talk about the coming change in this year. Lot of new faces, new Hedvig metallic. Yeah. So what we'll want to get your impression on the executive changes, some of the, you know, are you seeing any indications of organizational changes and the products? What I'm seeing is I'm seeing new life to a product that I've always been told is a dinosaur, which I kind of laugh cause I'm like, well if this dinosaur is doing things that, you know, the greatest and latest and greatest things aren't or aren't really doing. So to see this new life, the new rebranding of the logo, the new leadership, the new acquisitions and everything is just like feeding fuel to the fire. That is combo. And, and I'm, I'm pretty excited. I will say that I'm a little bit more excited about the new additions to like orchestrate and activate since stuff like metallic. I won't really be implementing just because of our business practices. But yeah. >>Let's talk about in our last few minutes here, cause they actually talked about some of the new technologies with orchestrate activate yesterday and today, but in terms of support we just had as to mention, we just interviewed Sandy Hamilton and she's come on board in the last, I think she said four and a half months. Owning professional services systems, engineering support, customer success throughout the entire life cycle. Tell us a little bit in, in our closing minute or so about the support and training that you've gotten from combat that give you the confidence for you and one of your other guys to be here and not tied to your phone. >>I don't think I'd still be with combo if it wasn't for their support. I, I owe so much to their support. They've brought me through some pretty dark times with deployment, with troubleshooting, with failures where I thought that I had things right and it just didn't work. I've called in at one in the morning, got great support, I've caught any 10 in the morning and got great support, phenomenal follow up. Um, their, their community impact, like their forums and their customer champion. So much. Just additional information that helps you not have to call in and not make you feel like that, Oh, that failure. So I owe a lot to their support and their training because without a, like I wouldn't be, I wouldn't be on stage. I'm, I'm wonder if you could put a point on that, the, the forums in your participation as a customer champion, you're spending your own time, you're working with your PBM. >>Why is that so important and how is this the vibrancy of this community, you know, it belongs to the worlds, you know, naming the things that you learn. Somebody taught me, so why shouldn't I teach somebody else? And if that makes someone else be able to go out and ride mountain bikes or cook with their daughter or do anything like that, then I'm all for it because it got me, it got me through all that. So I mean I have 10 15 minutes on the customer forum to answer you. Oh yeah, I know that. I've seen that. I had a gentleman the first morning at breakfast, like I've had a ticket open for two weeks and they can't figure it out. And we worked together and actually got his problem solved, you know? And it was like the only reason is because I've seen that and I worked with combat and they showed me how to fix it and I retain that knowledge. >>That's awesome. DOE takes paying it forward to a whole new level. And it also volumes about how you followed Jimmy chin this morning and nailed it. I tried. It was very difficult, you know, I'm sure that you know why he was filming that solo climber. He was sweaty palms. I was definitely sweaty phone calls. It was, well, Matthew, what a pleasure to have you on the program. So much fun. Thank you. Congratulations on your success and we look forward to hearing it. Many more great things out of Sonic. Thank you. All right. First to a minimum I and Lisa Martin, you're watching the cube from combo go 19.