>>We're back with Jerome West, product management security lead at for HCI at Dell Technologies Hyper-converged infrastructure. Jerome, welcome. >>Thank you, David. >>Hey, Jerome, In this series, A blueprint for trusted infrastructure, we've been digging into the different parts of the infrastructure stack, including storage, servers and networking, and now we want to cover hyperconverged infrastructure. So my first question is, what's unique about HCI that presents specific security challenges? What do we need to know? >>So what's unique about Hyperconverge infrastructure is the breadth of the security challenge. We can't simply focus on a single type of IT system, so like a server or a storage system or a virtualization piece of software. I mean, HCI is all of those things. So luckily we have excellent partners like VMware, Microsoft, and internal partners like the Dell Power Edge team, the Dell storage team, the Dell networking team, and on and on. These partnerships, in these collaborations are what make us successful from a security standpoint. So let me give you an example to illustrate. In the recent past, we're seeing growing scope and sophistication in supply chain attacks. This mean an attacker is going to attack your software supply chain upstream so that hopefully a piece of code, malicious code that wasn't identified early in the software supply chain is distributed like a large player, like a VMware or Microsoft or a Dell. So to confront this kind of sophisticated hard to defeat problem, we need short term solutions and we need long term solutions as well. >>So for the short term solution, the obvious thing to do is to patch the vulnerability. The complexity is for our HCI portfolio. We build our software on VMware, so we would have to consume a patch that VMware would produce and provide it to our customers in a timely manner. Luckily, VX Rail's engineering team has co engineered a release process with VMware that significantly shortens our development life cycle so that VMware will produce a patch and within 14 days we will integrate our own code. With the VMware release, we will have tested and validated the update and we will give an update to our customers within 14 days of that VMware release. That as a result of this kind of rapid development process, Vxl had over 40 releases of software updates last year for a longer term solution. We're partnering with VMware and others to develop a software bill of materials. We work with VMware to consume their software manifest, including their upstream vendors and their open source providers to have a comprehensive list of software components. Then we aren't caught off guard by an unforeseen vulnerability and we're more able to easily detect where the software problem lies so that we can quickly address it. So these are the kind of relationships and solutions that we can co engineer with effective collaborations with our, with our partners. >>Great, Thank you for that. That description. So if I had to define what cybersecurity resilience means to HCI or converged infrastructure, and to me my takeaway was you gotta have a short term instant patch solution and then you gotta do an integration in a very short time, you know, two weeks to then have that integration done. And then longer term you have to have a software bill of materials so that you can ensure the providence of all the components help us. Is that a right way to think about cybersecurity resilience? Do you have, you know, a additives to that definition? >>I do. I really think that site cybersecurity and resilience for hci, because like I said, it has sort of unprecedented breadth across our portfolio. It's not a single thing, it's a bit of everything. So really the strength or the secret sauce is to combine all the solutions that our partner develops while integrating them with our own layer. So let me, let me give you an example. So hci, it's a, basically taking a software abstraction of hardware functionality and implementing it into something called the virtualized layer. It's basically the virtual virtualizing hardware functionality, like say a storage controller, you could implement it in a hardware, but for hci, for example, in our VX rail portfolio, we, or our vxl product, we integrate it into a product called vsan, which is provided by our partner VMware. So that portfolio strength is still, you know, through our, through our partnerships. >>So what we do, we integrate these, these security functionality and features in into our product. So our partnership grows to our ecosystem through products like VMware, products like nsx, Verizon, Carbon Black and Bsphere. All of them integrate seamlessly with VMware. And we also leverage VMware's software, par software partnerships on top of that. So for example, VX supports multifactor authentication through bsphere integration with something called Active Directory Federation services for adfs. So there is a lot of providers that support adfs, including Microsoft Azure. So now we can support a wide array of identity providers such as Off Zero or I mentioned Azure or Active Directory through that partnership. So we can leverage all of our partners partnerships as well. So there's sort of a second layer. So being able to secure all of that, that provides a lot of options and flexibility for our customers. So basically to summarize my my answer, we consume all of the security advantages of our partners, but we also expand on that to make a product that is comprehensively secured at multiple layers from the hardware layer that's provided by Dell through Power Edge to the hyper-converged software that we build ourselves to the virtualization layer that we get through our partnerships with Microsoft and VMware. >>Great. I mean that's super helpful. You've mentioned nsx, Horizon, Carbon Black, all the, you know, the VMware component OTH zero, which the developers are gonna love. You got Azure identity, so it's really an ecosystem. So you may have actually answered my next question, but I'm gonna ask it anyway cuz you've got this software defined environment and you're managing servers and networking and storage with this software led approach, how do you ensure that the entire system is secure end to end? >>That's a really great question. So the, the answer is we do testing and validation as part of the engineering process. It's not just bolted on at the end. So when we do, for example, the xra is the market's only co engineered solution with VMware, other vendors sell VMware as a hyperconverged solution, but we actually include security as part of the co-engineering process with VMware. So it's considered when VMware builds their code and their process dovetails with ours because we have a secure development life cycle, which other products might talk about in their discussions with you that we integrate into our engineering life cycle. So because we follow the same framework, all of the, all of the codes should interoperate from a security standpoint. And so when we do our final validation testing when we do a software release, we're already halfway there in ensuring that all these features will give the customers what we promised. >>That's great. All right, let's, let's close pitch me, what would you say is the strong suit summarize the, the strengths of the Dell hyperconverged infrastructure and converged infrastructure portfolio specifically from a security perspective? Jerome? >>So I talked about how hyper hyper-converged infrastructure simplifies security management because basically you're gonna take all of these features that are abstracted in in hardware, they're now abstracted in the virtualization layer. Now you can manage them from a single point of view, whether it would be, say, you know, in for VX rail would be b be center, for example. So by abstracting all this, you make it very easy to manage security and highly flexible because now you don't have limitations around a single vendor. You have a multiple array of choices and partnerships to select. So I would say that is the, the key to making it to hci. Now, what makes Dell the market leader in HCI is not only do we have that functionality, but we also make it exceptionally useful to you because it's co engineered, it's not bolted on. So I gave the example of, I gave the example of how we, we modify our software release process with VMware to make it very responsive. >>A couple of other features that we have specific just to HCI are digitally signed LCM updates. This is an example of a feature that we have that's only exclusive to Dell that's not done through a partnership. So we digitally sign our software updates so you, the user can be sure that the, the update that they're installing into their system is an authentic and unmodified product. So we give it a Dell signature that's invalidated prior to installation. So not only do we consume the features that others develop in a seamless and fully validated way, but we also bolt on our own specific HCI security features that work with all the other partnerships and give the user an exceptional security experience. So for, for example, the benefit to the customer is you don't have to create a complicated security framework that's hard for your users to use and it's hard for your system administrators to manage. It all comes in a package. So it, it can be all managed through vCenter, for example, or, and then the specific hyper, hyper-converged functions can be managed through VxRail manager or through STDC manager. So there's very few pains of glass that the, the administrator or user ever has to worry about. It's all self contained and manageable. >>That makes a lot of sense. So you got your own infrastructure, you're applying your best practices to that, like the digital signatures, you've got your ecosystem, you're doing co-engineering with the ecosystems, delivering security in a package, minimizing the complexity at the infrastructure level. The reason Jerome, this is so important is because SecOps teams, you know, they gotta deal with cloud security, they gotta deal with multiple clouds. Now they have their shared responsibility model going across multiple, They got all this other stuff that they have to worry, they gotta secure containers and the run time and, and, and, and, and the platform and so forth. So they're being asked to do other things. If they have to worry about all the things that you just mentioned, they'll never get, you know, the, the securities is gonna get worse. So what my takeaway is, you're removing that infrastructure piece and saying, Okay guys, you now can focus on those other things that is not necessarily Dell's, you know, domain, but you, you know, you can work with other partners to, and your own teams to really nail that. Is that a fair summary? >>I think that is a fair summary because absolutely the worst thing you can do from a security perspective is provide a feature that's so unusable that the administrator disables it or other key security features. So when I work with my partners to define, to define and develop a new security feature, the thing I keep foremost in mind is, will this be something our users want to use in our administrators want to administer? Because if it's not, if it's something that's too difficult or onerous or complex, then I try to find ways to make it more user friendly and practical. And this is a challenge sometimes because we are, our products operate in highly regulated environments and sometimes they have to have certain rules and certain configurations that aren't the most user friendly or management friendly. So I, I put a lot of effort into thinking about how can we make this feature useful while still complying with all the regulations that we have to comply with. And by the way, we're very successful in a highly regulated space. We sell a lot of VxRail, for example, into the Department of Defense and banks and, and other highly regulated environments, and we're very successful >>There. Excellent. Okay, Jerome, thanks. We're gonna leave it there for now. I'd love to have you back to talk about the progress that you're making down the road. Things always, you know, advance in the tech industry and so would appreciate that. >>I would look forward to it. Thank you very much, Dave. >>You're really welcome. In a moment I'll be back to summarize the program and offer some resources that can help you on your journey to secure your enterprise infrastructure. I wanna thank our guests for their contributions and helping us understand how investments by a company like Dell can both reduce the need for dev sec up teams to worry about some of the more fundamental security issues around infrastructure and have greater confidence in the quality providence and data protection designed in to core infrastructure like servers, storage, networking, and hyper-converged systems. You know, at the end of the day, whether your workloads are in the cloud, OnPrem or at the edge, you are responsible for your own security. But vendor r and d and vendor process must play an important role in easing the burden faced by security devs and operation teams. And on behalf of the cube production content and social teams as well as Dell Technologies, we want to thank you for watching a blueprint for trusted infrastructure. Remember part one of this series as well as all the videos associated with this program, and of course, today's program are available on demand@thecube.net with additional coverage@siliconangle.com. And you can go to dell.com/security solutions dell.com/security solutions to learn more about Dell's approach to securing infrastructure. And there's tons of additional resources that can help you on your journey. This is Dave Valante for the Cube, your leader in enterprise and emerging tech coverage. We'll see you next time.

>>The cybersecurity landscape continues to be one characterized by a series of point tools designed to do a very specific job, often pretty well, but the mosaic of tooling is grown over the years causing complexity in driving up costs and increasing exposures. So the game of Whackamole continues. Moreover, the way organizations approach security is changing quite dramatically. The cloud, while offering so many advantages, has also created new complexities. The shared responsibility model redefines what the cloud provider secures, for example, the S three bucket and what the customer is responsible for eg properly configuring the bucket. You know, this is all well and good, but because virtually no organization of any size can go all in on a single cloud, that shared responsibility model now spans multiple clouds and with different protocols. Now that of course includes on-prem and edge deployments, making things even more complex. Moreover, the DevOps team is being asked to be the point of execution to implement many aspects of an organization's security strategy. >>This extends to securing the runtime, the platform, and even now containers which can end up anywhere. There's a real need for consolidation in the security industry, and that's part of the answer. We've seen this both in terms of mergers and acquisitions as well as platform plays that cover more and more ground. But the diversity of alternatives and infrastructure implementations continues to boggle the mind with more and more entry points for the attackers. This includes sophisticated supply chain attacks that make it even more difficult to understand how to secure components of a system and how secure those components actually are. The number one challenge CISOs face in today's complex world is lack of talent to address these challenges. And I'm not saying that SecOps pros are not talented, They are. There just aren't enough of them to go around and the adversary is also talented and very creative, and there are more and more of them every day. >>Now, one of the very important roles that a technology vendor can play is to take mundane infrastructure security tasks off the plates of SEC off teams. Specifically we're talking about shifting much of the heavy lifting around securing servers, storage, networking, and other infrastructure and their components onto the technology vendor via r and d and other best practices like supply chain management. And that's what we're here to talk about. Welcome to the second part in our series, A Blueprint for Trusted Infrastructure Made Possible by Dell Technologies and produced by the Cube. My name is Dave Ante and I'm your host now. Previously we looked at what trusted infrastructure means and the role that storage and data protection play in the equation. In this part two of the series, we explore the changing nature of technology infrastructure, how the industry generally in Dell specifically, are adapting to these changes and what is being done to proactively address threats that are increasingly stressing security teams. >>Now today, we continue the discussion and look more deeply into servers networking and hyper-converged infrastructure to better understand the critical aspects of how one company Dell is securing these elements so that dev sec op teams can focus on the myriad new attack vectors and challenges that they faced. First up is Deepak rang Garage Power Edge security product manager at Dell Technologies. And after that we're gonna bring on Mahesh Nagar oim, who was consultant in the networking product management area at Dell. And finally, we're close with Jerome West, who is the product management security lead for HCI hyperconverged infrastructure and converged infrastructure at Dell. Thanks for joining us today. We're thrilled to have you here and hope you enjoy the program. Deepak Arage shoes powered security product manager at Dell Technologies. Deepak, great to have you on the program. Thank you. >>Thank you for having me. >>So we're going through the infrastructure stack and in part one of this series we looked at the landscape overall and how cyber has changed and specifically how Dell thinks about data protection in, in security in a manner that both secures infrastructure and minimizes organizational friction. We also hit on the storage part of the portfolio. So now we want to dig into servers. So my first question is, what are the critical aspects of securing server infrastructure that our audience should be aware of? >>Sure. So if you look at compute in general, right, it has rapidly evolved over the past couple of years, especially with trends toward software defined data centers and with also organizations having to deal with hybrid environments where they have private clouds, public cloud locations, remote offices, and also remote workers. So on top of this, there's also an increase in the complexity of the supply chain itself, right? There are companies who are dealing with hundreds of suppliers as part of their supply chain. So all of this complexity provides a lot of opportunity for attackers because it's expanding the threat surface of what can be attacked, and attacks are becoming more frequent, more severe and more sophisticated. And this has also triggered around in the regulatory and mandates around the security needs. >>And these regulations are not just in the government sector, right? So it extends to critical infrastructure and eventually it also get into the private sector. In addition to this, organizations are also looking at their own internal compliance mandates. And this could be based on the industry in which they're operating in, or it could be their own security postures. And this is the landscape in which servers they're operating today. And given that servers are the foundational blocks of the data center, it becomes extremely important to protect them. And given how complex the modern server platforms are, it's also extremely difficult and it takes a lot of effort. And this means protecting everything from the supply chain to the manufacturing and then eventually the assuring the hardware and software integrity of the platforms and also the operations. And there are very few companies that go to the lens that Dell does in order to secure the server. We truly believe in the notion and the security mentality that, you know, security should enable our customers to go focus on their business and proactively innovate on their business and it should not be a burden to them. And we heavily invest to make that possible for our customers. >>So this is really important because the premise that I set up at the beginning of this was really that I, as of security pro, I'm not a security pro, but if I were, I wouldn't want to be doing all this infrastructure stuff because I now have all these new things I gotta deal with. I want a company like Dell who has the resources to build that security in to deal with the supply chain to ensure the providence, et cetera. So I'm glad you you, you hit on that, but so given what you just said, what does cybersecurity resilience mean from a server perspective? For example, are there specific principles that Dell adheres to that are non-negotiable? Let's say, how does Dell ensure that its customers can trust your server infrastructure? >>Yeah, like when, when it comes to security at Dell, right? It's ingrained in our product, so that's the best way to put it. And security is nonnegotiable, right? It's never an afterthought where we come up with a design and then later on figure out how to go make it secure, right? Our security development life cycle, the products are being designed to counter these threats right from the big. And in addition to that, we are also testing and evaluating these products continuously to identify vulnerabilities. We also have external third party audits which supplement this process. And in addition to this, Dell makes the commitment that we will rapidly respond to any mitigations and vulnerability, any vulnerabilities and exposures found out in the field and provide mitigations and patches for in attacking manner. So this security principle is also built into our server life cycle, right? Every phase of it. >>So we want our products to provide cutting edge capabilities when it comes to security. So as part of that, we are constantly evaluating what our security model is done. We are building on it and continuously improving it. So till a few years ago, our model was primarily based on the N framework of protect, detect and rigor. And it's still aligns really well to that framework, but over the past couple of years, we have seen how computers evolved, how the threads have evolved, and we have also seen the regulatory trends and we recognize the fact that the best security strategy for the modern world is a zero trust approach. And so now when we are building our infrastructure and tools and offerings for customers, first and foremost, they're cyber resilient, right? What we mean by that is they're capable of anticipating threats, withstanding attacks and rapidly recurring from attacks and also adapting to the adverse conditions in which they're deployed. The process of designing these capabilities and identifying these capabilities however, is done through the zero press framework. And that's very important because now we are also anticipating how our customers will end up using these capabilities at there and to enable their own zero trust IT environments and IT zero trusts deployments. We have completely adapted our security approach to make it easier for customers to work with us no matter where they are in their journey towards zero trust option. >>So thank you for that. You mentioned the, this framework, you talked about zero trust. When I think about n I think as well about layered approaches. And when I think about zero trust, I think about if you, if you don't have access to it, you're not getting access, you've gotta earn that, that access and you've got layers and then you still assume that bad guys are gonna get in. So you've gotta detect that and you've gotta response. So server infrastructure security is so fundamental. So my question is, what is Dell providing specifically to, for example, detect anomalies and breaches from unauthorized activity? How do you enable fast and easy or facile recovery from malicious incidents, >>Right? What is that is exactly right, right? Breachers are bound to happen and given how complex our current environment is, it's extremely distributed and extremely connected, right? Data and users are no longer contained with an offices where we can set up a perimeter firewall and say, Yeah, everything within that is good. We can trust everything within it. That's no longer true. The best approach to protect data and infrastructure in the current world is to use a zero trust approach, which uses the principles. Nothing is ever trusted, right? Nothing is trusted implicitly. You're constantly verifying every single user, every single device, and every single access in your system at every single level of your ID environment. And this is the principles that we use on power Edge, right? But with an increased focus on providing granular controls and checks based on the principles of these privileged access. >>So the idea is that service first and foremost need to make sure that the threats never enter and they're rejected at the point of entry, but we recognize breaches are going to occur and if they do, they need to be minimized such that the sphere of damage cost by attacker is minimized so they're not able to move from one part of the network to something else laterally or escalate their privileges and cause more damage, right? So the impact radius for instance, has to be radius. And this is done through features like automated detection capabilities and automation, automated remediation capabilities. So some examples are as part of our end to end boot resilience process, we have what they call a system lockdown, right? We can lock down the configuration of the system and lock on the form versions and all changes to the system. And we have capabilities which automatically detect any drift from that lockdown configuration and we can figure out if the drift was caused to authorized changes or unauthorized changes. >>And if it is an unauthorize change can log it, generate security alerts, and we even have capabilities to automatically roll the firm where, and always versions back to a known good version and also the configurations, right? And this becomes extremely important because as part of zero trust, we need to respond to these things at machine speed and we cannot do it at a human speed. And having these automated capabilities is a big deal when achieving that zero trust strategy. And in addition to this, we also have chassis inclusion detection where if the chassis, the box, the several box is opened up, it logs alerts, and you can figure out even later if there's an AC power cycle, you can go look at the logs to see that the box is opened up and figure out if there was a, like a known authorized access or some malicious actor opening and chain something in your system. >>Great, thank you for that lot. Lot of detail and and appreciate that. I want to go somewhere else now cuz Dell has a renowned supply chain reputation. So what about securing the, the supply chain and the server bill of materials? What does Dell specifically do to track the providence of components it uses in its systems so that when the systems arrive, a customer can be a hundred percent certain that that system hasn't been compromised, >>Right? And we've talked about how complex the modern supply chain is, right? And that's no different for service. We have hundreds of confidence on the server and a lot of these form where in order to be configured and run and this former competence could be coming from third parties suppliers. So now the complexity that we are dealing with like was the end to end approach and that's where Dell pays a lot of attention into assuring the security approach approaching and it starts all the way from sourcing competence, right? And then through the design and then even the manufacturing process where we are wetting the personnel leather factories and wetting the factories itself. And the factories also have physical controls, physical security controls built into them and even shipping, right? We have GPS tagging of packages. So all of this is built to ensure supply chain security. >>But a critical aspect of this is also making sure that the systems which are built in the factories are delivered to the customers without any changes or any tapper. And we have a feature called the secure component verification, which is capable of doing this. What the feature does this, when the system gets built in a factory, it generates an inventory of all the competence in the system and it creates a cryptographic certificate based on the signatures presented to this by the competence. And this certificate is stored separately and sent to the customers separately from the system itself. So once the customers receive the system at their end, they can run out to, it generates an inventory of the competence on the system at their end and then compare it to the golden certificate to make sure nothing was changed. And if any changes are detected, we can figure out if there's an authorized change or unauthorize change. >>Again, authorized changes could be like, you know, upgrades to the drives or memory and ized changes could be any sort of temper. So that's the supply chain aspect of it and bill of metal use is also an important aspect to galing security, right? And we provide a software bill of materials, which is basically a list of ingredients of all the software pieces in the platform. So what it allows our customers to do is quickly take a look at all the different pieces and compare it to the vulnerability database and see if any of the vulner which have been discovered out in the wild affected platform. So that's a quick way of figuring out if the platform has any known vulnerabilities and it has not been patched. >>Excellent. That's really good. My last question is, I wonder if you, you know, give us the sort of summary from your perspective, what are the key strengths of Dell server portfolio from a security standpoint? I'm really interested in, you know, the uniqueness and the strong suit that Dell brings to the table, >>Right? Yeah. We have talked enough about the complexity of the environment and how zero risk is necessary for the modern ID environment, right? And this is integral to Dell powered service. And as part of that like you know, security starts with the supply chain. We already talked about the second component verification, which is a beneath feature that Dell platforms have. And on top of it we also have a silicon place platform mode of trust. So this is a key which is programmed into the silicon on the black service during manufacturing and can never be changed after. And this immutable key is what forms the anchor for creating the chain of trust that is used to verify everything in the platform from the hardware and software integrity to the boot, all pieces of it, right? In addition to that, we also have a host of data protection features. >>Whether it is protecting data at risk in news or inflight, we have self encrypting drives which provides scalable and flexible encryption options. And this couple with external key management provides really good protection for your data address. External key management is important because you know, somebody could physically steam the server walk away, but then the keys are not stored on the server, it stood separately. So that provides your action layer of security. And we also have dual layer encryption where you can compliment the hardware encryption on the secure encrypted drives with software level encryption. Inion to this we have identity and access management features like multifactor authentication, single sign on roles, scope and time based access controls, all of which are critical to enable that granular control and checks for zero trust approach. So I would say like, you know, if you look at the Dell feature set, it's pretty comprehensive and we also have the flexibility built in to meet the needs of all customers no matter where they fall in the spectrum of, you know, risk tolerance and security sensitivity. And we also have the capabilities to meet all the regulatory requirements and compliance requirements. So in a nutshell, I would say that you know, Dell Power Service cyber resident infrastructure helps accelerate zero tested option for customers. >>Got it. So you've really thought this through all the various things that that you would do to sort of make sure that your server infrastructure is secure, not compromised, that your supply chain is secure so that your customers can focus on some of the other things that they have to worry about, which are numerous. Thanks Deepak, appreciate you coming on the cube and participating in the program. >>Thank you for having >>You're welcome. In a moment I'll be back to dig into the networking portion of the infrastructure. Stay with us for more coverage of a blueprint for trusted infrastructure and collaboration with Dell Technologies on the cube, your leader in enterprise and emerging tech coverage. We're back with a blueprint for trusted infrastructure and partnership with Dell Technologies in the cube. And we're here with Mahesh Nager, who is a consultant in the area of networking product management at Dell Technologies. Mahesh, welcome, good to see you. >>Hey, good morning Dell's, nice to meet, meet to you as well. >>Hey, so we've been digging into all the parts of the infrastructure stack and now we're gonna look at the all important networking components. Mahesh, when we think about networking in today's environment, we think about the core data center and we're connecting out to various locations including the cloud and both the near and the far edge. So the question is from Dell's perspective, what's unique and challenging about securing network infrastructure that we should know about? >>Yeah, so few years ago IT security and an enterprise was primarily putting a wrapper around data center out because it was constrained to an infrastructure owned and operated by the enterprise for the most part. So putting a rapid around it like a parameter or a firewall was a sufficient response because you could basically control the environment and data small enough control today with the distributed data, intelligent software, different systems, multi-cloud environment and asset service delivery, you know, the infrastructure for the modern era changes the way to secure the network infrastructure In today's, you know, data driven world, it operates everywhere and data has created and accessed everywhere so far from, you know, the centralized monolithic data centers of the past. The biggest challenge is how do we build the network infrastructure of the modern era that are intelligent with automation enabling maximum flexibility and business agility without any compromise on the security. We believe that in this data era, the security transformation must accompany digital transformation. >>Yeah, that's very good. You talked about a couple of things there. Data by its very nature is distributed. There is no perimeter anymore, so you can't just, as you say, put a rapper around it. I like the way you phrase that. So when you think about cyber security resilience from a networking perspective, how do you define that? In other words, what are the basic principles that you adhere to when thinking about securing network infrastructure for your customers? >>So our belief is that cybersecurity and cybersecurity resilience, they need to be holistic, they need to be integrated, scalable, one that span the entire enterprise and with a co and objective and policy implementation. So cybersecurity needs to span across all the devices and running across any application, whether the application resets on the cloud or anywhere else in the infrastructure. From a networking standpoint, what does it mean? It's again, the same principles, right? You know, in order to prevent the threat actors from accessing changing best destroy or stealing sensitive data, this definition holds good for networking as well. So if you look at it from a networking perspective, it's the ability to protect from and withstand attacks on the networking systems as we continue to evolve. This will also include the ability to adapt and recover from these attacks, which is what cyber resilience aspect is all about. So cybersecurity best practices, as you know, is continuously changing the landscape primarily because the cyber threats also continue to evolve. >>Yeah, got it. So I like that. So it's gotta be integrated, it's gotta be scalable, it's gotta be comprehensive, comprehensive and adaptable. You're saying it can't be static, >>Right? Right. So I think, you know, you had a second part of a question, you know, that says what do we, you know, what are the basic principles? You know, when you think about securing network infrastructure, when you're looking at securing the network infrastructure, it revolves around core security capability of the devices that form the network. And what are these security capabilities? These are access control, software integrity and vulnerability response. When you look at access control, it's to ensure that only the authenticated users are able to access the platform and they're able to access only the kind of the assets that they're authorized to based on their user level. Now accessing a network platform like a switch or a rotor for example, is typically used for say, configuration and management of the networking switch. So user access is based on say roles for that matter in a role based access control, whether you are a security admin or a network admin or a storage admin. >>And it's imperative that logging is enable because any of the change to the configuration is actually logged and monitored as that. Talking about software's integrity, it's the ability to ensure that the software that's running on the system has not been compromised. And, and you know, this is important because it could actually, you know, get hold of the system and you know, you could get UND desire results in terms of say validation of the images. It's, it needs to be done through say digital signature. So, so it's important that when you're talking about say, software integrity, a, you are ensuring that the platform is not compromised, you know, is not compromised and be that any upgrades, you know, that happens to the platform is happening through say validated signature. >>Okay. And now, now you've now, so there's access control, software integrity, and I think you, you've got a third element which is i I think response, but please continue. >>Yeah, so you know, the third one is about civil notability. So we follow the same process that's been followed by the rest of the products within the Dell product family. That's to report or identify, you know, any kind of a vulnerability that's being addressed by the Dell product security incident response team. So the networking portfolio is no different, you know, it follows the same process for identification for tri and for resolution of these vulnerabilities. And these are addressed either through patches or through new reasons via networking software. >>Yeah, got it. Okay. So I mean, you didn't say zero trust, but when you were talking about access control, you're really talking about access to only those assets that people are authorized to access. I know zero trust sometimes is a buzzword, but, but you I think gave it, you know, some clarity there. Software integrity, it's about assurance validation, your digital signature you mentioned and, and that there's been no compromise. And then how you respond to incidents in a standard way that can fit into a security framework. So outstanding description, thank you for that. But then the next question is, how does Dell networking fit into the construct of what we've been talking about Dell trusted infrastructure? >>Okay, so networking is the key element in the Dell trusted infrastructure. It provides the interconnect between the service and the storage world. And you know, it's part of any data center configuration for a trusted infrastructure. The network needs to have access control in place where only the authorized nels are able to make change to the network configuration and logging off any of those changes is also done through the logging capabilities. Additionally, we should also ensure that the configuration should provide network isolation between say the management network and the data traffic network because they need to be separate and distinct from each other. And furthermore, even if you look at the data traffic network and now you have things like segmentation isolated segments and via VRF or, or some micro segmentation via partners, this allows various level of security for each of those segments. So it's important you know, that, that the network infrastructure has the ability, you know, to provide all this, this services from a Dell networking security perspective, right? >>You know, there are multiple layer of defense, you know, both at the edge and in the network in this hardware and in the software and essentially, you know, a set of rules and a configuration that's designed to sort of protect the integrity, confidentiality, and accessibility of the network assets. So each network security layer, it implements policies and controls as I said, you know, including send network segmentation. We do have capabilities sources, centralized management automation and capability and scalability for that matter. Now you add all of these things, you know, with the open networking standards or software, different principles and you essentially, you know, reach to the point where you know, you're looking at zero trust network access, which is essentially sort of a building block for increased cloud adoption. If you look at say that you know the different pillars of a zero trust architecture, you know, if you look at the device aspect, you know, we do have support for security for example, we do have say trust platform in a trusted platform models tpms on certain offer products and you know, the physical security know plain, simple old one love port enable from a user trust perspective, we know it's all done via access control days via role based access control and say capability in order to provide say remote authentication or things like say sticky Mac or Mac learning limit and so on. >>If you look at say a transport and decision trust layer, these are essentially, you know, how do you access, you know, this switch, you know, is it by plain hotel net or is it like secure ssh, right? And you know, when a host communicates, you know, to the switch, we do have things like self-signed or is certificate authority based certification. And one of the important aspect is, you know, in terms of, you know, the routing protocol, the routing protocol, say for example BGP for example, we do have the capability to support MD five authentication between the b g peers so that there is no, you know, manages attack, you know, to the network where the routing table is compromised. And the other aspect is about second control plane is here, you know, you know, it's, it's typical that if you don't have a control plane here, you know, it could be flooded and you know, you know, the switch could be compromised by city denial service attacks. >>From an application test perspective, as I mentioned, you know, we do have, you know, the application specific security rules where you could actually define, you know, the specific security rules based on the specific applications, you know, that are running within the system. And I did talk about, say the digital signature and the cryptographic check that we do for authentication and for, I mean rather for the authenticity and the validation of, you know, of the image and the BS and so on and so forth. Finally, you know, the data trust, we are looking at, you know, the network separation, you know, the network separation could happen or VRF plain old wheel Ls, you know, which can bring about sales multi 10 aspects. We talk about some microsegmentation as it applies to nsx for example. The other aspect is, you know, we do have, with our own smart fabric services that's enabled in a fabric, we have a concept of c cluster security. So all of this, you know, the different pillars, they sort of make up for the zero trust infrastructure for the networking assets of an infrastructure. >>Yeah. So thank you for that. There's a, there's a lot to unpack there. You know, one of the premise, the premise really of this, this, this, this segment that we're setting up in this series is really that everything you just mentioned, or a lot of things you just mentioned used to be the responsibility of the security team. And, and the premise that we're putting forth is that because security teams are so stretched thin, you, you gotta shift the vendor community. Dell specifically is shifting a lot of those tasks to their own r and d and taking care of a lot of that. So, cuz scop teams got a lot of other stuff to, to worry about. So my question relates to things like automation, which can help and scalability, what about those topics as it relates to networking infrastructure? >>Okay, our >>Portfolio, it enables state of the automation software, you know, that enables simplifying of the design. So for example, we do have, you know, you know the fabric design center, you know, a tool that automates the design of the fabric and you know, from a deployment and you know, the management of the network infrastructure that are simplicities, you know, using like Ansible s for Sonic for example are, you know, for a better sit and tell story. You know, we do have smart fabric services that can automate the entire fabric, you know, for a storage solution or for, you know, for one of the workloads for example. Now we do help reduce the complexity by closely integrating the management of the physical and the virtual networking infrastructure. And again, you know, we have those capabilities using Sonic or Smart Traffic services. If you look at Sonic for example, right? >>It delivers automated intent based secure containerized network and it has the ability to provide some network visibility and Avan has and, and all of these things are actually valid, you know, for a modern networking infrastructure. So now if you look at Sonic, you know, it's, you know, the usage of those tools, you know, that are available, you know, within the Sonic no is not restricted, you know, just to the data center infrastructure is, it's a unified no, you know, that's well applicable beyond the data center, you know, right up to the edge. Now if you look at our north from a smart traffic OS 10 perspective, you know, as I mentioned, we do have smart traffic services which essentially, you know, simplifies the deployment day zero, I mean rather day one, day two deployment expansion plans and the lifecycle management of our conversion infrastructure and hyper and hyper conversion infrastructure solutions. And finally, in order to enable say, zero touch deployment, we do have, you know, a VP solution with our SD van capability. So these are, you know, ways by which we bring down the complexity by, you know, enhancing the automation capability using, you know, a singular loss that can expand from a data center now right to the edge. >>Great, thank you for that. Last question real quick, just pitch me, what can you summarize from your point of view, what's the strength of the Dell networking portfolio? >>Okay, so from a Dell networking portfolio, we support capabilities at multiple layers. As I mentioned, we're talking about the physical security for examples, say disabling of the unused interface. Sticky Mac and trusted platform modules are the things that to go after. And when you're talking about say secure boot for example, it delivers the authenticity and the integrity of the OS 10 images at the startup. And Secure Boot also protects the startup configuration so that, you know, the startup configuration file is not compromised. And Secure port also enables the workload of prediction, for example, that is at another aspect of software image integrity validation, you know, wherein the image is data for the digital signature, you know, prior to any upgrade process. And if you are looking at secure access control, we do have things like role based access control, SSH to the switches, control plane access control that pre do tags and say access control from multifactor authentication. >>We do have various tech ads for entry control to the network and things like CSE and PRV support, you know, from a federal perspective we do have say logging wherein, you know, any event, any auditing capabilities can be possible by say looking at the clog service, you know, which are pretty much in our transmitter from the devices overts for example, and last we talked about say network segment, you know, say network separation and you know, these, you know, separation, you know, ensures that are, that is, you know, a contained say segment, you know, for a specific purpose or for the specific zone and, you know, just can be implemented by a, a micro segmentation, you know, just a plain old wheel or using virtual route of framework VR for example. >>A lot there. I mean I think frankly, you know, my takeaway is you guys do the heavy lifting in a very complicated topic. So thank you so much for, for coming on the cube and explaining that in in quite some depth. Really appreciate it. >>Thank you indeed. >>Oh, you're very welcome. Okay, in a moment I'll be back to dig into the hyper-converged infrastructure part of the portfolio and look at how when you enter the world of software defined where you're controlling servers and storage and networks via software led system, you could be sure that your infrastructure is trusted and secure. You're watching a blueprint for trusted infrastructure made possible by Dell Technologies and collaboration with the cube, your leader in enterprise and emerging tech coverage, your own west product management security lead at for HCI at Dell Technologies hyper-converged infrastructure. Jerome, welcome. >>Thank you Dave. >>Hey Jerome, in this series of blueprint for trusted infrastructure, we've been digging into the different parts of the infrastructure stack, including storage servers and networking, and now we want to cover hyperconverged infrastructure. So my first question is, what's unique about HCI that presents specific security challenges? What do we need to know? >>So what's unique about hyper-converge infrastructure is the breadth of the security challenge. We can't simply focus on a single type of IT system. So like a server or storage system or a virtualization piece of software, software. I mean HCI is all of those things. So luckily we have excellent partners like VMware, Microsoft, and internal partners like the Dell Power Edge team, the Dell storage team, the Dell networking team, and on and on. These partnerships in these collaborations are what make us successful from a security standpoint. So let me give you an example to illustrate. In the recent past we're seeing growing scope and sophistication in supply chain attacks. This mean an attacker is going to attack your software supply chain upstream so that hopefully a piece of code, malicious code that wasn't identified early in the software supply chain is distributed like a large player, like a VMware or Microsoft or a Dell. So to confront this kind of sophisticated hard to defeat problem, we need short term solutions and we need long term solutions as well. >>So for the short term solution, the obvious thing to do is to patch the vulnerability. The complexity is for our HCI portfolio. We build our software on VMware, so we would have to consume a patch that VMware would produce and provide it to our customers in a timely manner. Luckily VX rail's engineering team has co engineered a release process with VMware that significantly shortens our development life cycle so that VMware would produce a patch and within 14 days we will integrate our own code with the VMware release we will have tested and validated the update and we will give an update to our customers within 14 days of that VMware release. That as a result of this kind of rapid development process, VHA had over 40 releases of software updates last year for a longer term solution. We're partnering with VMware and others to develop a software bill of materials. We work with VMware to consume their software manifest, including their upstream vendors and their open source providers to have a comprehensive list of software components. Then we aren't caught off guard by an unforeseen vulnerability and we're more able to easily detect where the software problem lies so that we can quickly address it. So these are the kind of relationships and solutions that we can co engineer with effective collaborations with our, with our partners. >>Great, thank you for that. That description. So if I had to define what cybersecurity resilience means to HCI or converged infrastructure, and to me my takeaway was you gotta have a short term instant patch solution and then you gotta do an integration in a very short time, you know, two weeks to then have that integration done. And then longer term you have to have a software bill of materials so that you can ensure the providence of all the components help us. Is that a right way to think about cybersecurity resilience? Do you have, you know, a additives to that definition? >>I do. I really think that's site cybersecurity and resilience for hci because like I said, it has sort of unprecedented breadth across our portfolio. It's not a single thing, it's a bit of everything. So really the strength or the secret sauce is to combine all the solutions that our partner develops while integrating them with our own layer. So let me, let me give you an example. So hci, it's a, basically taking a software abstraction of hardware functionality and implementing it into something called the virtualized layer. It's basically the virtual virtualizing hardware functionality, like say a storage controller, you could implement it in hardware, but for hci, for example, in our VX rail portfolio, we, our Vxl product, we integrated it into a product called vsan, which is provided by our partner VMware. So that portfolio of strength is still, you know, through our, through our partnerships. >>So what we do, we integrate these, these security functionality and features in into our product. So our partnership grows to our ecosystem through products like VMware, products like nsx, Horizon, Carbon Black and vSphere. All of them integrate seamlessly with VMware and we also leverage VMware's software, part software partnerships on top of that. So for example, VX supports multifactor authentication through vSphere integration with something called Active Directory Federation services for adfs. So there's a lot of providers that support adfs including Microsoft Azure. So now we can support a wide array of identity providers such as Off Zero or I mentioned Azure or Active Directory through that partnership. So we can leverage all of our partners partnerships as well. So there's sort of a second layer. So being able to secure all of that, that provides a lot of options and flexibility for our customers. So basically to summarize my my answer, we consume all of the security advantages of our partners, but we also expand on them to make a product that is comprehensively secured at multiple layers from the hardware layer that's provided by Dell through Power Edge to the hyper-converged software that we build ourselves to the virtualization layer that we get through our partnerships with Microsoft and VMware. >>Great, I mean that's super helpful. You've mentioned nsx, Horizon, Carbon Black, all the, you know, the VMware component OTH zero, which the developers are gonna love. You got Azure identity, so it's really an ecosystem. So you may have actually answered my next question, but I'm gonna ask it anyway cuz you've got this software defined environment and you're managing servers and networking and storage with this software led approach, how do you ensure that the entire system is secure end to end? >>That's a really great question. So the, the answer is we do testing and validation as part of the engineering process. It's not just bolted on at the end. So when we do, for example, VxRail is the market's only co engineered solution with VMware, other vendors sell VMware as a hyper converged solution, but we actually include security as part of the co-engineering process with VMware. So it's considered when VMware builds their code and their process dovetails with ours because we have a secure development life cycle, which other products might talk about in their discussions with you that we integrate into our engineering life cycle. So because we follow the same framework, all of the, all of the codes should interoperate from a security standpoint. And so when we do our final validation testing when we do a software release, we're already halfway there in ensuring that all these features will give the customers what we promised. >>That's great. All right, let's, let's close pitch me, what would you say is the strong suit summarize the, the strengths of the Dell hyper-converged infrastructure and converged infrastructure portfolio specifically from a security perspective? Jerome? >>So I talked about how hyper hyper-converged infrastructure simplifies security management because basically you're gonna take all of these features that are abstracted in in hardware, they're now abstracted in the virtualization layer. Now you can manage them from a single point of view, whether it would be, say, you know, in for VX rail would be b be center, for example. So by abstracting all this, you make it very easy to manage security and highly flexible because now you don't have limitations around a single vendor. You have a multiple array of choices and partnerships to select. So I would say that is the, the key to making it to hci. Now, what makes Dell the market leader in HCI is not only do we have that functionality, but we also make it exceptionally useful to you because it's co engineered, it's not bolted on. So I gave the example of spo, I gave the example of how we, we modify our software release process with VMware to make it very responsive. >>A couple of other features that we have specific just to HCI are digitally signed LCM updates. This is an example of a feature that we have that's only exclusive to Dell that's not done through a partnership. So we digitally signed our software updates so the user can be sure that the, the update that they're installing into their system is an authentic and unmodified product. So we give it a Dell signature that's invalidated prior to installation. So not only do we consume the features that others develop in a seamless and fully validated way, but we also bolt on our own a specific HCI security features that work with all the other partnerships and give the user an exceptional security experience. So for, for example, the benefit to the customer is you don't have to create a complicated security framework that's hard for your users to use and it's hard for your system administrators to manage it all comes in a package. So it, it can be all managed through vCenter, for example, or, and then the specific hyper, hyper-converged functions can be managed through VxRail manager or through STDC manager. So there's very few pains of glass that the, the administrator or user ever has to worry about. It's all self contained and manageable. >>That makes a lot of sense. So you've got your own infrastructure, you're applying your best practices to that, like the digital signatures, you've got your ecosystem, you're doing co-engineering with the ecosystems, delivering security in a package, minimizing the complexity at the infrastructure level. The reason Jerome, this is so important is because SecOps teams, you know, they gotta deal with cloud security, they gotta deal with multiple clouds. Now they have their shared responsibility model going across multiple cl. They got all this other stuff that they have to worry, they gotta secure the containers and the run time and and, and, and, and the platform and so forth. So they're being asked to do other things. If they have to worry about all the things that you just mentioned, they'll never get, you know, the, the securities is gonna get worse. So what my takeaway is, you're removing that infrastructure piece and saying, Okay guys, you now can focus on those other things that is not necessarily Dell's, you know, domain, but you, you know, you can work with other partners to and your own teams to really nail that. Is that a fair summary? >>I think that is a fair summary because absolutely the worst thing you can do from a security perspective is provide a feature that's so unusable that the administrator disables it or other key security features. So when I work with my partners to define, to define and develop a new security feature, the thing I keep foremost in mind is, will this be something our users want to use and our administrators want to administer? Because if it's not, if it's something that's too difficult or onerous or complex, then I try to find ways to make it more user friendly and practical. And this is a challenge sometimes because we are, our products operate in highly regulated environments and sometimes they have to have certain rules and certain configurations that aren't the most user friendly or management friendly. So I, I put a lot of effort into thinking about how can we make this feature useful while still complying with all the regulations that we have to comply with. And by the way, we're very successful in a highly regulated space. We sell a lot of VxRail, for example, into the Department of Defense and banks and, and other highly regulated environments and we're very successful there. >>Excellent. Okay, Jerome, thanks. We're gonna leave it there for now. I'd love to have you back to talk about the progress that you're making down the road. Things always, you know, advance in the tech industry and so would appreciate that. >>I would look forward to it. Thank you very much, Dave. >>You're really welcome. In a moment I'll be back to summarize the program and offer some resources that can help you on your journey to secure your enterprise infrastructure. I wanna thank our guests for their contributions in helping us understand how investments by a company like Dell can both reduce the need for dev sec up teams to worry about some of the more fundamental security issues around infrastructure and have greater confidence in the quality providence and data protection designed in to core infrastructure like servers, storage, networking, and hyper-converged systems. You know, at the end of the day, whether your workloads are in the cloud, on prem or at the edge, you are responsible for your own security. But vendor r and d and vendor process must play an important role in easing the burden faced by security devs and operation teams. And on behalf of the cube production content and social teams as well as Dell Technologies, we want to thank you for watching a blueprint for trusted infrastructure. Remember part one of this series as well as all the videos associated with this program and of course today's program are available on demand@thecube.net with additional coverage@siliconangle.com. And you can go to dell.com/security solutions dell.com/security solutions to learn more about Dell's approach to securing infrastructure. And there's tons of additional resources that can help you on your journey. This is Dave Valante for the Cube, your leader in enterprise and emerging tech coverage. We'll see you next time.

(upbeat music) (logo crystals tingle) >> Hi, everybody, welcome back to FalCon22, I'm Dave Vellante and you're watching theCube's continuous coverage, this is day two. We live in an API economy, but APIs, you know, they're sometimes vulnerable, Michael Nicosia is here, he's the Chief Operating Officer and co-founder of Salt Security, API Security Specialist, Michael, welcome to theCUBE, thanks for coming on. >> Thank you so much, Dave, glad to be here. >> You're very welcome. Why did you and your co-founder, is it Roy? >> Yeah. >> Why did you guys start Salt Security? >> So really easy, I mean, as you mentioned, the proliferation of APIs constantly is growing on a year to year basis. So in 2015, when he and I met, we had this idea that it was going to continue to grow and APIs were going to be critical to every organization from an innovation perspective, from a safety perspective and we thought that current tools out there couldn't protect against the new threat vector that we thought was going to happen. And, you know, you fast forward to 2022 and here we are, it's the largest growing threat vector from an API perspective because APIs are just growing like crazy. >> Right. Well, let's talk about the news, CrowdStrike made an investment in your company. >> Michael: Yes. >> Congratulations. >> Michael: Thank you. >> Tell us about that, why it's important, and to have a strategic partner like that. >> Yeah, so first of all, we're super thrilled about the partnership, I mean, it's amazing. And not only the partnership, the strategic investment for us just signifies the importance of our two companies in terms of what we want to do in the field together or in the market together. So the strategic investment is amazing, the partnership is even more amazing just because it's kind of like, you know, the first in its class from an API security perspective, we've got partners from the cloud providers and then the only other partnerships really have is with API Management vendors. So this is unique in that it goes outside the security ecosystem to provide this partnership and the nice thing about it is it's exclusive, excuse me, and it just continues to validate the leadership where we have an API security, as well as obviously a leadership that CrowdStrike has. >> Exclusive in the sense that CrowdStrike's not going to invest in another API competitor and you're not going to take investment from an endpoint- >> Michael: Exactly. >> Or something like that. >> Endpoint or, you know, really cloud workload situation. >> Anything within that vastly expanding portfolio. >> Michael: Exactly. >> So pretty much anybody. >> Michael: Exactly. >> Except network security, from what I saw in the keynote yesterday, that's sort of on the table, for now. So, okay, so why should customers care about this? What's the benefit to them? >> Yeah, so if you think about, the security profile of organizations and where they seem to have potential risk, threat vectors, you know, endpoint, you know, Cloud obviously API becomes a bigger, threat vector as well. So I think the partnership just solidifies the fact that we want to create a better security profile for organizations and we want to make it safe for them to innovate and continue to do what they do. So I think that's the importance and when you put the two together it just creates a larger value proposition, more stickiness from end point to cloud, to APIs. >> So we have a partner, theCUBE, and in New York city and it's called ETR and they do quarterly surveys of CISOs, CIOs, IT buyers, about 12 to 1500 a quarter. And so I was chatting with those guys last week, they knew we were going to be at CrowdStrike and so they ran some data for all the API security vendors and you guys were, you know they had like the Gartner Magic Quadrant but it's not, you know, vision and execution, it's spending momentum and like presence in their survey, it's like market share, mind share. >> Sure. >> You guys were up and to the right, like, way, way, way ahead, I presume that's why you got the attention of CrowdStrike. I found their data set to be incredibly good, that's how we found CrowdStrike years ago, like, "Wow, who's this company?" >> Yeah. >> You know, companies like CrowdStrike, Okta, Zscaler, Snowflake Off The Charts, but you guys were really noticeable. Talk about the spending momentum you're seeing with customers, where's that coming from? >> Yeah, I mean look, for us it's a continuing growing market, it's accelerating and we're still in the, you know, early stages of the market, which is amazing. But if you think about what organizations do, they innovate, right, they innovate through, you know, software, through applications or APIs. So if you think about, you know, how do they continue to innovate safely? They need a solution, like Salt Security to protect from any bad actors that could potentially create any breaches, vulnerabilities. So I think that that's why CISOs in particular are super excited about talking to us, making sure that they have all of their bases covered especially when it comes to applications that they have within their organization, which continues to grow. >> And not to not to be a methodology geek, but the methodology they use is to essentially say, is a customer spending more or less, they subtract the lesses from the mores and that's what you're left with. And one of the lesses is churn, and if you have high churn, you're spending momentum, >> you know- >> Micheal: Yeah. >> In their methodology goes into the tank. So you have obviously admitted you have very low churn is that what you're saying in the field? >> Micheal: Absolutely. >> Why is that? >> Yeah, I mean, again, I think it's, it goes back to the value that we bring to customers. I think, you know, our solution works, we're the only AI/ML-based solution with deep context so we can really take a closer granular look at the APIs, model those APIs, create a baseline and really protect against them. So I mean, our solution works and it works really well and I think we provide value in that, you know, CISOs don't have to worry about any bad actors trying to infiltrate their applications 'cause they know that Salt Security is there protecting them. >> I know you're not the tech guy but you're the founder, co-founder of a technology company so you got to be conversant in the tech, 'cause this is the way it is in our business, so tell us about the tech, what's so cool about it? What's the differentiation? >> Yeah, I guess, and I mentioned that it's really AI/ML based, you know, we leverage big data and it's really the context associated to that, which means that, you know, we can get into granular details of really baselining the API itself. And what we do really well is, because these are unique attacks and these attacks could be days, weeks, months and we're the only vendor that, that can really correlate across that timeline because of the context-based big data that we leverage to be able to, you know, spot these potential bad actors that we look for. >> And all this happens in the cloud or? >> Absolutely, it's all... >> You have a server in your office? >> No, no, it's all it's a hundred percent SaaS-based, Cloud-based solution, I think that's one of the reasons why the partnership with CrowdStrike is so amazing as well. >> Talk a little bit more about the synergies between CrowdStrike and Salt Security. >> Tons of synergies, I mean, if you think about from, you know, from the part of being a little fluffy culture, the two companies have similar cultures, we go after similar you know, first Cloud, innovative companies. If you think about kind of the technology that CrowdStrike has put forth, revolutionized the endpoint security, and now moving into the Cloud, you know, leveraging AI and ML, we're doing the exact same thing so I think there's a lot of synergies associated with that. And again, the final point that I'll make is that you know, we think together the, you know, better together story is, resonates just because if you think about all of the areas that you know have potential breaches, these threats, we kind of cover 'em all with the partnership. >> When I talk to a founding, you know, co-founder, who's a go to market pro, I like to ask them how did you know when to scale? I mean, you got to have product market fit, I see so many companies failing because they try to go to market before they have, they try to scale go to market before they have product market, but how did you do it? How did you know when to scale? >> You know, it's tricky, and you got to look at a couple of, you know, factors, you got to look at the market, you got to look at, you know, how much potential opportunity exists and you really need to look at, the momentum that is being established. You know, when you talk to CISOs, kind of, you know, talking to them about projects and how, how they prioritize projects and where API security fits, you know, once it begins to be the top three and you start that momentum and obviously you bringing in the revenue. I think that those are signs that we see, that we say, "Okay, we need to double down on making sure we've got coverage across the world in order for us to support demand." >> And you were the first sales rep, right? >> Michael: Yeah. >> Okay. >> Roy and I, I was the first AE, here was the first SE. >> Okay, but your early go-to market pros are probably different than what you're bringing in today, you didn't have, you know, a lot of BDRs at the time, but you guys were hands on consultants- >> Absolutely. >> Like sort of process consultants, sales folks, right? And then you codify that when you're ready to scale and now you're, is that kind of a, what you're doing? >> Absolutely, I mean, you nailed it, I mean, it's in the early stages, it's validating that there's a problem that exists in the market and how important is that problem, you know, to CISOs. So when we first started we met probably about 50 CISOs where we just had that conversation, not about sales, it was more about, "Hey we just want to talk to you about a problem we think exists in the market, love to get your reaction on that problem and then obviously how you're solving that problem and how much of a priority is that problem," How important is it to you? And then once you have those discussions then you can really find those individuals, early adopters if you will, that are ready to buy and then it kind of proliferates from there. >> And then you have a CRO , I presume, right? So what was that like finding him or her, is a really important first sales hire. >> Super important, yeah. >> How did you go about that? How long did it take? >> Yeah so it took about six to eight months and you know it's really tough because, you know, we look at cultural fit, above everything else. So it's not, that, "Can they do the job?" it's culturally, do they fit in? And you know, how much can that individual scale the organization? So there's a lot of factors associated, there's a lot of individuals associated to, you know with the interview process. So that's how we looked at it and obviously we wanted somebody that had experience in a company our size, was able to scale it and so on. The one tricky thing is, and I'll tell you this, is, you know, for Roy and I, you kind of have to let go a little bit, that was really tough, so knowing that you need to do that is something that- >> A little bit of founderitis? >> Micheal: Yeah. >> Dave: It's hard, right? >> Micheal: It's hard. >> Dave: Yeah, it's your baby. >> It's like, whaat? >> I get it, Michael, thanks so much for coming to theCUBE, congratulations on the news- >> Thank you Dave. >> The investment and good luck. >> Awesome, thank you so much, appreciate it. >> You're really welcome. All right, keep it right there, we'll be back right after this short break. Dave Vellante for theCUBE at FalCon22, CrowdStrike's big user event, we'll be right back. (cheerful bouncy music)

>> We're back at the ARIA Las Vegas. We're covering CrowdStrike's Fal.Con 22. First one since 2019. Dave Vellante and Dave Nicholson on theCUBE. Adam Meyers is here, he is the Senior Vice President of Intelligence at CrowdStrike. Adam, thanks for coming to theCUBE. >> Thanks for having me. >> Interesting times, isn't it? You're very welcome. Senior Vice President of Intelligence, tell us what your role is. >> So I run all of our intelligence offerings. All of our analysts, we have a couple hundred analysts that work at CrowdStrike tracking threat actors. There's 185 threat actors that we track today. We're constantly adding more of them and it requires us to really have that visibility and understand how they operate so that we can inform our other products: our XDR, our Cloud Workload Protections and really integrate all of this around the threat actor. >> So it's that threat hunting capability that CrowdStrike has. That's what you're sort of... >> Well, so think of it this way. When we launched the company 11 years ago yesterday, what we wanted to do was to tell customers, to tell people that, well, you don't have a malware problem, you have an adversary problem. There are humans that are out there conducting these attacks, and if you know who they are what they're up to, how they operate then you're better positioned to defend against them. And so that's really at the core, what CrowdStrike started with and all of our products are powered by intelligence. All of our services are our OverWatch and our Falcon complete, all powered by intelligence because we want to know who the threat actors are and what they're doing so we can stop them. >> So for instance like you can stop known malware. A lot of companies can stop known malware, but you also can stop unknown malware. And I infer that the intelligence is part of that equation, is that right? >> Absolutely. That that's the outcome. That's the output of the intelligence but I could also tell you who these threat actors are, where they're operating out of, show you pictures of some of them, that's the threat intel. We are tracking down to the individual persona in many cases, these various threats whether they be Chinese nation state, Russian threat actors, Iran, North Korea, we track as I said, quite a few of these threats. And over time, we develop a really robust deep knowledge about who they are and how they operate. >> Okay. And we're going to get into some of that, the big four and cyber. But before we do, I want to ask you about the eCrime index stats, the ECX you guys call it a little side joke for all your nerds out there. Maybe you could explain that Adam >> Assembly humor. >> Yeah right, right. So, but, what is that index? You guys, how often do you publish it? What are you learning from that? >> Yeah, so it was modeled off of the Dow Jones industrial average. So if you look at the Dow Jones it's a composite index that was started in the late 1800s. And they took a couple of different companies that were the industrial component of the economy back then, right. Textiles and railroads and coal and steel and things like that. And they use that to approximate the overall health of the economy. So if you take these different stocks together, swizzle 'em together, and figure out some sort of number you could say, look, it's up. The economy's doing good. It's down, not doing so good. So after World War II, everybody was exuberant and positive about the end of the war. The DGI goes up, the oil crisis in the seventies goes down, COVID hits goes up, sorry, goes down. And then everybody realizes that they can use Amazon still and they can still get the things they need goes back up with the eCrime index. We took that approach to say what is the health of the underground economy? When you read about any of these ransomware attacks or data extortion attacks there are criminal groups that are working together in order to get things spammed out or to buy credentials and things like that. And so what the eCrime index does is it takes 24 different observables, right? The price of a ransom, the number of ransom attacks, the fluctuation in cryptocurrency, how much stolen material is being sold for on the underground. And we're constantly computing this number to understand is the eCrime ecosystem healthy? Is it thriving or is it under pressure? And that lets us understand what's going on in the world and kind of contextualize it. Give an example, Microsoft on patch Tuesday releases 56 vulnerabilities. 11 of them are critical. Well guess what? After hack Tuesday. So after patch Tuesday is hack Wednesday. And so all of those 11 vulnerabilities are exploitable. And now you have threat actors that have a whole new array of weapons that they can deploy and bring to bear against their victims after that patch Tuesday. So that's hack Wednesday. Conversely we'll get something like the colonial pipeline. Colonial pipeline attack May of 21, I think it was, comes out and all of the various underground forums where these ransomware operators are doing their business. They freak out because they don't want law enforcement. President Biden is talking about them and he's putting pressure on them. They don't want this ransomware component of what they're doing to bring law enforcement, bring heat on them. So they deplatform them. They kick 'em off. And when they do that, the ransomware stops being as much of a factor at that point in time. And the eCrime index goes down. So we can look at holidays, and right around Thanksgiving, which is coming up pretty soon, it's going to go up because there's so much online commerce with cyber Monday and such, right? You're going to see this increase in online activity; eCrime actors want to take advantage of that. When Christmas comes, they take vacation too; they're going to spend time with their families, so it goes back down and it stays down till around the end of the Russian Orthodox Christmas, which you can probably extrapolate why that is. And then it goes back up. So as it's fluctuating, it gives us the ability to really just start tracking what that economy looks like. >> Realtime indicator of that crypto. >> I mean, you talked about, talked about hack Wednesday, and before that you mentioned, you know, the big four, and I think you said 185 threat actors that you're tracking, is 180, is number 185 on that list? Somebody living in their basement in their mom's basement or are the resources necessary to get on that list? Such that it's like, no, no, no, no. this is very, very organized, large groups of people. Hollywood would have you believe that it's guy with a laptop, hack Wednesday, (Dave Nicholson mimics keyboard clacking noises) and everything done. >> Right. >> Are there individuals who are doing things like that or are these typically very well organized? >> That's a great question. And I think it's an important one to ask and it's both it tends to be more, the bigger groups. There are some one-off ones where it's one or two people. Sometimes they get big. Sometimes they get small. One of the big challenges. Have you heard of ransomware as a service? >> Of course. Oh my God. Any knucklehead can be a ransomwarist. >> Exactly. So we don't track those knuckleheads as much unless they get onto our radar somehow, they're conducting a lot of operations against our customers or something like that. But what we do track is that ransomware as a service platform because the affiliates, the people that are using it they come, they go and, you know, it could be they're only there for a period of time. Sometimes they move between different ransomware services, right? They'll use the one that's most useful for them that that week or that month, they're getting the best rate because it's rev sharing. They get a percentage that platform gets percentage of the ransom. So, you know, they negotiate a better deal. They might move to a different ransomware platform. So that's really hard to track. And it's also, you know, I think more important for us to understand the platform and the technology that is being used than the individual that's doing it. >> Yeah. Makes sense. Alright, let's talk about the big four. China, Iran, North Korea, and Russia. Tell us about, you know, how you monitor these folks. Are there different signatures for each? Can you actually tell, you know based on the hack who's behind it? >> So yeah, it starts off, you know motivation is a huge factor. China conducts espionage, they do it for diplomatic purposes. They do it for military and political purposes. And they do it for economic espionage. All of these things map to known policies that they put out, the Five Year Plan, the Made in China 2025, the Belt and Road Initiative, it's all part of their efforts to become a regional and ultimately a global hegemon. >> They're not stealing nickels and dimes. >> No they're stealing intellectual property. They're stealing trade secrets. They're stealing negotiation points. When there's, you know a high speed rail or something like that. And they use a set of tools and they have a set of behaviors and they have a set of infrastructure and a set of targets that as we look at all of these things together we can derive who they are by motivation and the longer we observe them, the more data we get, the more we can get that attribution. I could tell you that there's X number of Chinese threat groups that we track under Panda, right? And they're associated with the Ministry of State Security. There's a whole other set. That's too associated with the People's Liberation Army Strategic Support Force. So, I mean, these are big operations. They're intelligence agencies that are operating out of China. Iran has a different set of targets. They have a different set of motives. They go after North American and Israeli businesses right now that's kind of their main operation. And they're doing something called hack and lock and leak. With a lock and leak, what they're doing is they're deploying ransomware. They don't care about getting a ransom payment. They're just doing it to disrupt the target. And then they're leaking information that they steal during that operation that brings embarrassment. It brings compliance, regulatory, legal impact for that particular entity. So it's disruptive >> The chaos creators that's.. >> Well, you know I think they're trying to create a they're trying to really impact the legitimacy of some of these targets and the trust that their customers and their partners and people have in them. And that is psychological warfare in a certain way. And it, you know is really part of their broader initiative. Look at some of the other things that they've done they've hacked into like the missile defense system in Israel, and they've turned on the sirens, right? Those are all things that they're doing for a specific purpose, and that's not China, right? Like as you start to look at this stuff, you can start to really understand what they're up to. Russia very much been busy targeting NATO and NATO countries and Ukraine. Obviously the conflict that started in February has been a huge focus for these threat actors. And then as we look at North Korea, totally different. They're doing, there was a major crypto attack today. They're going after these crypto platforms, they're going after DeFi platforms. They're going after all of this stuff that most people don't even understand and they're stealing the crypto currency and they're using it for revenue generation. These nuclear weapons don't pay for themselves, their research and development don't pay for themselves. And so they're using that cyber operation to either steal money or steal intelligence. >> They need the cash. Yeah. >> Yeah. And they also do economic targeting because Kim Jong Un had said back in 2016 that they need to improve the lives of North Koreans. They have this national economic development strategy. And that means that they need, you know, I think only 30% of North Korea has access to reliable power. So having access to clean energy sources and renewable energy sources, that's important to keep the people happy and stop them from rising up against the regime. So that's the type of economic espionage that they're conducting. >> Well, those are the big four. If there were big five or six, I would presume US and some Western European countries would be on there. Do you track, I mean, where United States obviously has you know, people that are capable of this we're out doing our thing, and- >> So I think- >> That defense or offense, where do we sit in this matrix? >> Well, I think the big five would probably include eCrime. We also track India, Pakistan. We track actors out of Columbia, out of Turkey, out of Syria. So there's a whole, you know this problem is getting worse over time. It's proliferating. And I think COVID was also, you know a driver there because so many of these countries couldn't move human assets around because everything was getting locked down. As machine learning and artificial intelligence and all of this makes its way into the cameras at border and transfer points, it's hard to get a human asset through there. And so cyber is a very attractive, cheap and deniable form of espionage and gives them operational capabilities, not, you know and to your question about US and other kind of five I friendly type countries we have not seen them targeting our customers. So we focus on the threats that target our customers. >> Right. >> And so, you know, if we were to find them at a customer environment sure. But you know, when you look at some of the public reporting that's out there, the malware that's associated with them is focused on, you know, real bad people, and it's, it's physically like crypted to their hard drive. So unless you have sensor on, you know, an Iranian or some other laptop that might be target or something like that. >> Well, like Stuxnet did. >> Yeah. >> Right so. >> You won't see it. Right. See, so yeah. >> Well Symantec saw it but way back when right? Back in the day. >> Well, I mean, if you want to go down that route I think it actually came from a company in the region that was doing the IR and they were working with Symantec. >> Oh, okay. So, okay. So it was a local >> Yeah. I think Crisis, I think was the company that first identified it. And then they worked with Symantec. >> It Was, they found it, I guess, a logic controller. I forget what it was. >> It was a long time ago, so I might not have that completely right. >> But it was a seminal moment in the industry. >> Oh. And it was a seminal moment for Iran because you know, that I think caused them to get into cyber operations. Right. When they realized that something like that could happen that bolstered, you know there was a lot of underground hacking forums in Iran. And, you know, after Stuxnet, we started seeing that those hackers were dropping their hacker names and they were starting businesses. They were starting to try to go after government contracts. And they were starting to build training offensive programs, things like that because, you know they realized that this is an opportunity there. >> Yeah. We were talking earlier about this with Shawn and, you know, in the nuclear war, you know the Cold War days, you had the mutually assured destruction. It's not as black and white in the cyber world. Right. Cause as, as Robert Gates told me, you know a few years ago, we have a lot more to lose. So we have to be somewhat, as the United States, careful as to how much of an offensive posture we take. >> Well here's a secret. So I have a background on political science. So mutually assured destruction, I think is a deterrent strategy where you have two kind of two, two entities that like they will destroy each other if they so they're disinclined to go down that route. >> Right. >> With cyber I really don't like that mutually assured destruction >> That doesn't fit right. >> I think it's deterrents by denial. Right? So raising the cost, if they were to conduct a cyber operation, raising that cost that they don't want to do it, they don't want to incur the impact of that. Right. And think about this in terms of a lot of people are asking about would China invade Taiwan. And so as you look at the cost that that would have on the Chinese military, the POA, the POA Navy et cetera, you know, that's that deterrents by denial, trying to, trying to make the costs so high that they don't want to do it. And I think that's a better fit for cyber to try to figure out how can we raise the cost to the adversary if they operate against our customers against our enterprises and that they'll go someplace else and do something else. >> Well, that's a retaliatory strike, isn't it? I mean, is that what you're saying? >> No, definitely not. >> It's more of reducing their return on investment essentially. >> Yeah. >> And incenting them- disincening them to do X and sending them off somewhere else. >> Right. And threat actors, whether they be criminals or nation states, you know, Bruce Lee had this great quote that was "be like water", right? Like take the path of least resistance, like water will. Threat actors do that too. So, I mean, unless you're super high value target that they absolutely have to get into by any means necessary, then if you become too hard of a target, they're going to move on to somebody that's a little easier. >> Makes sense. Awesome. Really appreciate your, I could, we'd love to have you back. >> Anytime. >> Go deeper. Adam Myers. We're here at Fal.Con 22, Dave Vellante, Dave Nicholson. We'll be right back right after this short break. (bouncy music plays)

(upbeat music) >> Hello everyone, and welcome to Fal.Con 2022, CrowdStrike's big user conference. You're watching the Cube. My name is Dave Vallante. I'm here with my co-host David Nicholson. CrowdStrike is a company that was founded over 10 years ago. This is about 11 years, almost to the day. They're 2 billion company in revenue terms. They're growing at about 60% a year. They've got a path they've committed to wall street. They've got a path to $5 billion by mid decade. They got a $40 billion market cap. They're free, free cash flow positive and trying to build essentially a generational company with a very growing Tam and a modern platform. CrowdStrike has the fundamental belief that the unstoppable breach is a myth. David Nicholson, even though CSOs don't believe that, CrowdStrike is on a mission. Right? >> I didn't hear the phrase. Zero trust mentioned in the keynote >> Right. >> What was mentioned was this idea that CrowdStrike isn't simply a tool, it's a platform. And obviously it takes a platform to get to 5 billion. >> Yeah. So let's talk about the keynote. George Kurtz, the CEO came on. I thought the keynote was, was measured, but very substantive. It was not a lot of hype in there. Most security conferences, the two exceptions are this one and Reinforce, Amazon's big security conference. Steven Schmidt. The first time I was at a Reinforce said "All this narrative about security is such a bad industry" and "We're not doing a great job." And "It's so scary." That doesn't help the industry. George Kurtz sort of took a similar message. And you know what, Dave? When I think of security outside the context of IT I think of like security guards >> Right. >> Like protecting the billionaires. Right? That's a powerful, you know, positive thing. It's not really a defensive movement even though it is defensive but so that was kind of his posture there. But he talked about essentially what I call, not his words permanent changes in the, in the in the cyber defense industry, subsequent to the pandemic. Again, he didn't specifically mention the pandemic but he alluded to, you know, this new world that we live in. Fal.Con is a hundred sessions, eight tracks. And really his contention is we're in the early innings. These guys got 20,000 customers. And I think they got the potential to have hundreds of thousands. >> Yeah. Yeah. So, if I'm working with a security company I want them to be measured. I'm not looking for hype. I don't want those. I don't want those guards to be in disco shirts. I want them in black suits. So, you know, so the, the, the point about measured is is I think a positive one. I was struck by the competence of the people who were on stage today. I have seen very very large companies become kind of bureaucratic. And sometimes you don't get the best of the best up on stage. And we saw a lot of impressive folks. >> Yeah. Michael Santonis get up, but before we get to him. So, a couple points that Kurtz made he said, "digital transformation is needed to bring modern architectures to IT. And that brings modern security." And he laid out that whole sort of old way, new way very Andy Jassy-like old guard, new guard. He didn't hit on it that hard but he basically said "security is all about mitigating risk." And he mentioned that the the CSO I say CSO, he says CSO or CSO has a seat at the board. Now, many CSOs are board level participants. And then he went into the sort of four pillars of, of workload, and the areas that they focus on. So workload to them is end point, identity, and then data. They don't touch network security. That's where they partner with the likes of Cisco, >> Right. >> And Palo Alto networks. But then they went deep into identity threat protection, data, which is their observability platform from an acquisition called Humio. And then they went big time into XDR. We're going to talk about all this stuff. He said, "data is the new digital currency." Talked a lot about how they're now renaming, Humio, Log Scale. That's their Splunk killer. We're going to talk about that all week. And he talked a little bit about the single agent architecture. That is kind of the linchpin of CrowdStrike's architecture. And then Michael Santonis, the CTO came on and did a deep dive into each of those, and really went deep into XDR extended, right? Detection and response. XDR building on EDR. >> Yeah. I think the subject of XDR is something we'll be, we'll be touching on a lot. I think in the next two days. I thought the extension into observability was very, very interesting. When you look at performance metrics, where things are gathering those things in and being able to use a single agent to do so. That speaks to this idea that they are a platform and not just a tool. It's easy to say that you aspire to be a platform. I think that's a proof point. On the subject, by the way of their fundamental architecture. Over the years, there have been times when saying that your infrastructure requires an agent that would've been a deal killer. People say "No agents!" They've stuck to their guns because they know that the best way to deliver what they deliver is to have an agent in the environment. And it has proven to be the right strategy. >> Well, this is one of the things I want to explore with the technical architects that come on here today is, how do you build a lightweight agent that can do everything that you say it's going to do? Because they started out at endpoint, and then they've extended it to all these other modules, you know, identity. They're now into observability. They've got this data platform. They just announced that acquisition of another company they bought Preempt, which is their identity. They announced Responsify, responsify? Reposify, which is sort of extends the observability and gives them visualization or visibility. And I'm like, how do you take? How do you keep an agent lightweight? That's one of the things I want to better understand. And then the other is, as you get into XDR I thought Michael Santonis was pretty interesting. He had black hat last month. He did a little video, you know. >> That was great >> Man in the street, what's XDR what's XDR what's XDR. I thought the best response was, somebody said "a holistic approach to end point security." And so it's really an evolution of, of EDR. So we're going to talk about that. But, how do you keep an agent lightweight and still support all these other capabilities? That's something I really want to dig into, you know, without getting bloated. >> Yeah, Yeah. I think it's all about the TLAs, Dave. It's about the S, it's about SDKs and APIs and having an ecosystem of partners that will look at the lightweight agent and then develop around it. Again, going back to the idea of platform, it's critical. If you're trying to do it all on your own, you get bloat. If you try to be all things to all people with your agent, if you try to reverse engineer every capability that's out there, it doesn't work. >> Well that's one of the things that, again I want to explore because CrowdStrike is trying to be a generational company. In the Breaking Analysis that we published this week. One of the things I said, "In order to be a generational company you have to have a strong ecosystem." Now the ecosystem here is respectable, you know, but it's obviously not AWS class. You know, I think Snowflake is a really good example, ServiceNow. This feels to me like ServiceNow circa 2013. >> Yeah. >> And we've seen how ServiceNow has evolved. You know, Okta, bought Off Zero to give them the developer angle. We heard a little bit about a developer platform today. I want to dig into that some more. And we heard a lot about everybody hates their DLP. I want to get rid of my DLP, data loss prevention. And so, and the same thing with the SIM. One of the ETR round table, Eric Bradley, our colleague at a round table said "If it weren't for the compliance requirements, I would replace my SIM with XDR." And so that's again, another interesting topic. CrowdStrike, cloud native, lightweight agent, you know, some really interesting tuck in acquisitions. Great go-to-market, you know, not super hype just product that works and gets stuff done, you know, seems to have a really good, bright future. >> Yeah, no, I would agree. Definitely. No hype necessary. Just constant execution moving forward. It's clearly something that will be increasingly in demand. Another subject that came up that I thought was interesting, in the keynote, was this idea of security for elections, extending into the realm of misinformation and disinformation which are both very very loaded terms. It'll be very interesting to see how security works its way into that realm in the future. >> Yeah, yeah, >> Yeah. >> Yeah, his guy, Kevin Mandia, who is the CEO of Mandiant, which just got acquired. Google just closed the deal for $5.4 billion. I thought that was kind of light, by the way, I thought Mandiant was worth more than that. Still a good number, but, and Kevin, you know was the founder and, >> Great guy. >> they were self-funded. >> Yeah, yeah impressive. >> So. But I thought he was really impressive. He talked about election security in terms of hardening you know, the election infrastructure, but then, boom he went right to what I see as the biggest issue, disinformation. And so I'm sitting there asking myself, okay how do you deal with that? And what he talked about was mapping network effects and monitoring network effects, >> Right. >> to see who's pumping the disinformation and building career streams to really monitor those network effects, positive, you know, factual or non-factual network or information. Because a lot of times, you know, networks will pump factual information to build credibility. Right? >> Right. >> And get street cred, earn that trust. You know, you talk about zero trust. And then pump disinformation into the network. So they've now got a track. We'll get, we have Kevin Mandia on later with Sean Henry who's the CSO yeah, the the CSO or C S O, chief security officer of CrowdStrike >> more TLA. Well, so, you can think of it as almost the modern equivalent of the political ad where the candidate at the end says I support this ad or I stand behind whatever's in this ad. Forget about trying to define what is dis or misinformation. What is opinion versus fact. Let's have a standard for finding, for exposing where the information is coming from. So if you could see, if you're reading something and there is something that is easily de-code able that says this information is coming from a troll farm of a thousand bots and you can sort of examine the underlying ethos behind where this information is coming from. And you can take that into consideration. Personally, I'm not a believer in trying to filter stuff out. Put the garbage out there, just make sure people know where the garbage is coming from so they can make decisions about it. >> So I got a thought on that because, Kevin Mandia touched on it. Again, I want to ask about this. He said, so this whole idea of these, you know detecting the bots and monitoring the networks. Then he said, you can I think he said something that's to the effect of. "You can go on the offensive." And I'm thinking, okay, what does that mean? So for instance, you see it all the time. Anytime I see some kind of fact put out there, I got to start reading the comments and like cause I like to see both sides, you know. I'm right down the middle. And you'll go down and like 40 comments down, you're like, oh this is, this is fake. This video was edited, >> Right. >> Da, da, da, da, and then a bunch of other people. But then the bots take over and that gets buried. So, maybe going on the offensive is to your point. Go ahead and put it out there. But then the bots, the positive bots say, okay, by the way, this is fake news. This is an edited video FYI. And this is who put it out and here's the bot graph or something like that. And then you attack the bots with more bots and then now everybody can sort of of see it, you know? And it's not like you don't have to, you know email your friend and saying, "Hey dude, this is fake news." >> Right, right. >> You know, Do some research. >> Yeah. >> Put the research out there in volume is what you're saying. >> Yeah. So, it's an, it's just I thought it was an interesting segue into another area of security under the heading of election security. That is fraught with a lot of danger if done wrong, if done incorrectly, you know, you you get into the realm of opinion making. And we should be free to see information, but we also should have access to information about where the information is coming from. >> The other narrative that you hear. So, everything's down today again and I haven't checked lately, but security generally, we wrote about this in our Breaking Analysis. Security, somewhat, has held up in the stock market better than the broad tech market. Why? And the premise is, George Kurt said this on the last conference call, earnings call, that "security is non-discretionary." At the same time he did say that sales cycles are getting a little longer, but we see this as a positive for CrowdStrike. Because CrowdStrike, their mission, or one of their missions is to consolidate all these point tools. We've talked many, many times in the Cube, and in Breaking Analysis and on Silicon Angle, and on Wikibon, how the the security business use too many point tools. You know this as a former CTO. And, now you've got all these stove pipes, the number one challenge the CSOs face is lack of talent. CrowdStrike's premise is they can consolidate that with the Fal.Con platform, and have a single point of control. "Single pane of glass" to use that bromide. So, the question is, is security really non-discretionary? My answer to that is yes and no. It is to a sense, because security is the number one priority. You can't be lax on security. But at the same time the CSO doesn't have an open checkbook, >> Right. >> He or she can't just say, okay, I need this. I need that. I need this. There's other competing initiatives that have to be taken in balance. And so, we've seen in the ETR spending data, you know. By the way, everything's up relative to where it was, pre you know, right at the pandemic, right when, pandemic year everything was flat to down. Everything's up, really up last year, I don't know 8 to 10%. It was expected to be up 8% this year, let's call it 6 to 7% in 21. We were calling for 7 to 8% this year. It's back down to like, you know, 4 or 5% now. It's still healthy, but it's softer. People are being more circumspect. People aren't sure about what the fed's going to do next. Interest rates, you know, loom large. A lot of uncertainty out here. So, in that sense, I would say security is not non-discretionary. Sorry for the double negative. What's your take? >> I think it's less discretionary. >> Okay. >> Food, water, air. Non-discretionary. (David laughing) And then you move away in sort of gradations from that point. I would say that yeah, it is, it falls into the category of less-discretionary. >> Alright. >> Which is a good place to be. >> Dave Nicholson and David Vallante here. Two days of wall to wall coverage of Fal.Con 2022, CrowdStrike's big user conference. We got some great guests. Keep it right there, we'll be right back, right after this short break. (upbeat music)

from the cube studios in palo alto in boston bringing you data driven insights from the cube and etr this is breaking analysis with dave vellante by the dip has been been an effective strategy since the market bottomed in early march last year the approach has been especially successful in tech and even more so for those tech names that one were well positioned for the forced march to digital i sometimes call it i.e remote work online commerce data centric platforms and certain cyber security plays and two already had the cloud figured out the question on investors minds is where to go from here should you avoid some of the high flyers that are richly valued with eye-popping multiples or should you continue to buy the dip and if so which companies that capitalized on the trends from last year will see permanent shifts in spending patterns that make them a solid long-term play hello and welcome to this week's wikibon cube insights powered by etr in this breaking analysis we shine the spotlight on three companies that may be candidates for a buy the dip strategy and it's our pleasure to welcome in ivana delevco who's the chief investment officer and founder of spear alpha a new research-centric etf focused on industrial technology ivana is a long-time equity analyst with a background in both long and short investing ivana welcome to the program thanks so much for coming on thanks for having me david yeah it's really our pleasure i i want to start with your etf and give the folks a bit more background about you first you know we gotta let people know i'm not an investment pro i'm not an advisor i don't make stock recommendations i don't sell investments so you got to do your own research i have a lot of data so happy to share it but you got to understand your own risks you of course yvonne on the other hand you do offer investment services and so people before investing got to carefully review all the available available investment docs understand what you're getting into before you invest now with that out of the way ivana i have some stats up here on this slide your spear you're a newly launched female lead firm that does deep research into the supply chain we're going to talk about that you try to uncover as i understand it under-appreciated industrial tech firms and some really pretty cool areas that we list here but tell us a little bit more about your background and your etf so thanks for having me david my background is in industrial research and industrial technology investments i've spent the past 15 years covering this space and what we've seen over the past five years is technology changes that are really driving fundamental shifts in industrial manufacturing processes so whether this is 5g connectivity innovation in the software stack increasing compute speeds all of these are major technological advancements that are impacting uh traditional manufacturers so what we try to do is assess speak to these firms and assess who is at the leading and who is at the lagging end of this digital transformation and we're trying to assess what vendors they're using what processes they're implementing and that is how we generate most of our investment ideas okay great and and we show on the bottom of of this sort of intro slide if you will uh so one of the processes that you use and one of the things that that is notable a lot of people compare you uh to kathy woods are investments when you came out uh i think you use a different process i mean maybe there are some similarities in terms of disruption but at the bottom of this slide it shows a mckinsey sort of graphic that that i think informs people as to how you really dig into the supply chain from a research standpoint is that right absolutely so for us it's all about understanding the supply chain going deep in the supply chain and gather data points from primary sources that we can then translate into investment opportunities so if you look at this mckinsey graph uh you will see that there is a lot of opportunity to for these companies to transform themselves both on the front end which means better revenue better products and on their operation side which means lower cost whether it's through better operations or through better processes on the the back end so what we do is we will speak to a traditional manufacturing company and ask them okay well what do you use for better product development and they will give us the name of the firms and give us an assessment of what's the differences between the competitors why they like one versus the other so then we're gonna take the data and we will put it into our financial model and we'll understand the broader market for it um the addressable market the market share that the company has and will project the growth so for these higher growth stocks that that you cover the main alpha generation uh potential here is to understand what the amount of growth these companies will generate over the next 10 to 20 years so it's really all about projecting growth in the next three years in the next five years and where will growth ultimately settle in in the next 10 to 20 years love it we're gonna have a fun conversation because today we're going to get into your thesis for cooper snowflake and z scalar we're going to bring in some of our own data some of our data from etr and and why you think these companies may be candidates for long-term growth and and be buy the dip stock so to do that i hacked up this little comparison slide we're showing here i do this for context our audience knows i'm not a cfa or a valuation expert but we like to do simple comparisons just to give people context and a sense of relative size growth and valuation and so this chart attempts to do that so what i did is i took the most recent quarterly revenue for cooper snowflake and z scalar multiplied it by four to get a run rate we included servicenow in the table just for baseline reference because bill mcdermott as we've reported aspires to make service now the next great enterprise software company alongside with salesforce and oracle and some of the others and and all these companies that we list here that through the three here they aspire to do so in their own domain so we're displaying the market cap from friday morning september 10th we calculated a revenue run rate multiple and we show the quarterly revenue growth and what this data does is gives you a sense of the three companies they're well on their way to a billion dollars in revenue it underscores the relationship between revenue growth and valuation snowflake being the poster child for that dynamic savannah i know you do much more detailed financial analysis but let's talk about these companies in order maybe start with koopa they just crushed their quarter i mean they blew away consensus on the top line what else about the company do you like and why is it on your by the dip list so just to back up david on valuation these companies investors either directly or indirectly value on a dcf basis and what happened at the beginning of the year as interest rates started increasing people started freaking out and once you plug in 100 basis points higher interest rate in your dcf model you get significant price downside so that really drove a lot of the pullback at the beginning of the year right now where we stand today interest rates haven't really moved all that significantly off the bot of the bottom they're still around the same levels maybe a little bit higher but those are not the types of moves that are going to drive significant downside in this stock so as things have stabilized here a lot of these opportunities look pretty attractive on that basis so koopa specifically came out of our um if you go back to that uh the chart of like where the opportunities lie in um in across the manufacturing uh um enterprise koopa is really focused on business pen management so they're really trying to help companies reduce their cost uh and they're a leader in the space uh they're unique uh unique in that they're cloud-based so the feedback we've been hearing from from our companies that use it jetblue uses it train technologies uses it the feedback we've been hearing is that they love the ease of implementation so it's very easy to implement and it drives real savings um savings for these companies so we see in our dcf model we see multiple years of this 30 40 percent growth and that's really driving our price target yeah and we can i can confirm that i mean i mean just anecdotally you know you know we serve a lot of the technology community and many of our clients are saying hey okay you know when you go to do invoicing or whatever you work with procurement it's koopa you know this is some ariba that's kind of the legacy which is sap we'll talk about that a little later but let's talk about snowflake um you know snowflake we've been tracking them very closely we know the management there we've watched them through their last two companies now here and have been following that company early on since since really 2015. tell us why you like snowflake um and and maybe why you think it can continue its rapid growth thanks david so first of all i need to compliment you on your research on the company on the technology side so where we come in is more from understanding where our companies can use soft snowflake and where snowflake can add value so what we've been hearing from our companies is the challenge that they're facing is that everybody's moving to the cloud but it's not as simple as just send your data to the cloud and call aws and they're gonna generate more revenue for your solve your cost problem so what we've been hearing is that companies need to find tools that are easy to use where they can use their own domain expertise and just plug and play so um ansys is one of the companies we covered the dust simulation they've found snowflake to be an extremely useful tool in sales lead generation and within sales crm systems have been around for a while and they're they've really been implemented but analyzing sales numbers is something that is new to this company some some of our companies don't even know what their sales are even when they look back after the quarter is closed so tools like this help um companies do easy analytics and therefore drive revenue and cost savings growth so we see really big runway for for this company and i think the most misunderstood part about it is that people view it as a warehousing data warehousing play while this is all about compute and the company does a good job separating the two and what our their customers like or like the companies that we cover like about it is that it can lower their compute costs um and make it much easier much more easily manageable for them great and we're going to talk about more about each of these companies but let's talk about z-scaler a bit i mean z-scaler is a company we've been very excited about and identified them kind of early on they've definitely benefited from the move to cloud generally and specifically the remote work uh situation with the cyber threats etc but tell us why you like z-scaler so interestingly z-scaler um we like the broader security space um the broader cyber security space and interestingly our companies are not yet spending to the level that is commensurate with the increase in attack rate so we think this is a trend that is really going to accelerate as we go forward um my own board 20 of the time on the last board meeting was spent on cyber security what we're doing and this is a pretty simple operation that that we're running here so you can imagine for a large enterprise with thousands of people all around the world um needing to be on a single simple system z-scaler really fits well here very easy to implement several of our industrial companies use it siemens uses it ge uses it and they've had great great experience with it excellent i just want to take a quick look at how some of these names have performed over the last year and and what if anything this data tells us this is a chart comparing the past 12 months performance of of those four companies uh that we just talked about and we added in you know servicenow z scalar as you can see has outperformed the other despite your commentary on discounted cash flow snowflake is underperformed really precisely for the reasons that you mentioned not to mention the fact that it was pretty highly valued and you can see relative to the nas but it's creeping back lately after very strong earnings even though the stock dropped after it beat earnings because the street wants the cfo to say to guide even higher than maybe as mike scarpelli feels is prudent and you can see cooper has also underperformed relatively speaking i mean it absolutely destroyed consensus this week the stock went up but it's been off with the the weaker market this week i know you like to take a longer term view but but anything you would add here yeah so interestingly both z-scaler and koopa were in the camp of as we went into earnings expectations were already pretty high because few of their competitors reported very strong results so this scalar yesterday their revenue growth was was pretty strong the stock is down today uh and the reason is because people were kind of caught up a little bit in the noise of this quarter growth is 57 last quarter it was 60 like is this a deceleration we don't see it as that at all and the company brought up one point that i thought was extremely interesting which is as their deal sizes are getting larger it takes a little longer time for them to see the revenue come through so it takes a little bit of time to for you to see it into from billings into into revenue same thing with cooper very strong earnings report but i think expectations were already pretty high going into it uh given the service now and um and anna plan as well reported strong results so i think it's all about positioning so we love these setups where you can buy the deep in on this opportunity where like people get caught up in um short-term noise and and it creates good entry points excellent i i want to bring in some data from our partner etr and see if you have any comments ivana so what we're showing here is a two-dimensional chart we like to show this uh very frequently it's based on a survey of between a thousand and fifteen hundred chief information officers and technology buyers every quarter this is from their most recent july survey the vertical axis shows net score which is a measure of spending momentum i mean this it measures the net percentage of customers in the survey that are spending more on a particular product or platform in other words it essentially subtracts the percentage of customers spending less from those spending more which yields a net score it's more granular than that but basically that's what it does the horizontal axis is market share or pervasiveness in the data set it's not revenue market share like you get from idc it's it's a mention market share and now that red dotted line at the 40 percent mark on the vertical represents an elevated level in other words anything above 40 percent we consider notable and we've plotted our three by the dip companies and included some of their competitors for context and you can see we added salesforce servicenow and oracle and that orange ellipse because they're some of the bigger names in the software business so let's take these in alphabetical order ivana starting with koopa in the blue you can see we plotted them next to sap's ariba and you can see cooper has stronger spending momentum but not as much presence in the market so to me my influence is oh that's an opportunity for them to steal share more modern technology you know more facile and of course oracle has products in this space but the oracle dot includes all oracle products not just the procurement stuff but uh maybe your thoughts on this absolutely i love this chart i think that's your spot on this would be the same way i would interpret the chart where um increased spending momentum is is a sign of the company providing products that people like and we we expect to see cooper's share grow market share grow over time as well so let's come back to the chart and i want to i want to really point out the green ellipse this is the data zone if you will uh and we're like a broken record on this program with snowflake has performed unbelievably well in net score and spending momentum every quarter the dtr has captured enough end sample in its survey holding near or above 80 percent its net score consistently is has been up there and we've plotted data bricks in that zone it's been expected right that data bricks is going to do an ipo this year late last month company raised 1.6 billion in a private round so i guess that was either a strategy to delay the ipo or raise a bunch more cash and give late investors a low risk bite at the apple you know pre-ipo as we saw with snowflake last year what we didn't plot here are some of snowflake's biggest competitors ivana who also happen to be their partners most notably the big cloud players all who have their own database offerings aws microsoft and google now you've said snowflake is much more than a database company i wonder if you could add some color here yeah that's a very good point david uh basically the the driver of the thesis in snowflake is all about acceleration and spending and what we are seeing is the customers that are signed up on their platform today they're not even spending they're probably spending less than five percent of what they can ultimately spend on this product and the reason is because they don't yet know what the ultimate applications are for this right so you're gonna start with putting the data in a format you can use and you need to come up with use cases or how are you actually going to use this data so back to the example that i gave with answers the first use case that they found was trying to optimize leads there could be like 100 other use cases and they're coming up with with those on a daily basis so i would expect um this score to keep keep uh keep up pretty high or or go even higher as we as people figure out how they can use this product you know the buy-the-dip thesis on snowflake was great last quarter because the stock pulled back after they announced earnings and when we reported we said you know mike the the company see well cleveland research came out remember they got the dip on that and we looked at the data and we said mike scarpelli said that you know we're going to probably as a percentage of overall customers decelerate the net net new logos but we're going deeper into the customer base and that's exactly what's happening with with snowflake but okay let's bring up the slide again last but not least the z scaler we love z scalar we named z scaler in 2019 as an emerging four-star security company along with crowdstrike and octa and we said these three should be on your radar and as you see we've plotted z scalar with octa who with its it's its recent move into to converging identity and governance uh it gets kind of interesting uh we plotted them with palo alto as well another cyber security player that we've covered extensively we love octa in addition to z-scaler we great respect for palo alto and you'll note all of them are over that 40 percent line these are disruptors they're benefiting well not so much palo alto they're more legacy but the the other two are benefiting from that shift to work from home cloud security modern tech stack uh the acquisition that octa-made of of of auth0 and again z scalar cloud security getting rid of a lot of hardware uh really has a huge tailwind at its back if on a zscaler you know they've benefited from the huge my cloud migration trend what are your thoughts on the company so i actually love all three companies that are there right and the point is people are just going to spend more money whether you are on the cloud of the cloud the data centers need more security as well so i think there is a strong case to be made for all three with this scaler the upside is that it's just very easy to use very easy to implement and if you're somebody that is just setting up infrastructure on the cloud there is no reason for you to call any other competitor right with palo alto the case there is that if you have an established um security platfor if you're on their security platform the databa on the data center side uh they they did introduce through several acquisitions a pretty attractive cloud offering as well so they've been gaining share as well in the space and and the company does look pretty attractive on valiation basis so for us cyber security is really all about rising tide lifts all boats here right so you can have a pure play like this scaler uh that benefits from the cloud but even somebody like palo alto is pretty well positioned um to benefit yeah we think so too over a year ago we reported on the valuation divergence between palo alto and fortinet fortinet was doing a better job moving to the cloud and obviously serves more of a mid-market space palo alto had some go-to-market execution challenges we said at the time they're going to get through those and when we talk to chief information security officers palo alto is like the gold standard they're the thought leader they want to work with them but at the same time they also want to participate in some of these you know modern cloud stacks so i we agree there's plenty of room for all three um just to add a bit more color and drill into the spending data a little bit more this slide here takes that net score and shows the progression since january 2019 and you can see a snowflake just incredible in terms of its ability to maintain that elevated net score as we talked about and the table on the insert it shows you the number of responses and all three of these companies have been getting more mentions over time but snowflake and z scale are now both well over 100 n in the survey each quarter and the other notable piece here and this is really important you can see all three are coming out of the isolation economy with the spending uptick nice upticks shown in the most recent survey so that's again another positive but i want to close ivana with kind of making the bull and bear case and have you address really the risks to the buy the dip scenario so look there are a lot of reasons to like these companies we talked about them cooper they've got earnings momentum you know management on the call side had very strong end market demand this the stock you know has underperformed the nasdaq you know this year snowflake and zscaler they also have momentum snowflake get this enormous tam uh although they were punished for not putting a hard number on it which is ridiculous in my opinion i mean the thing is it's huge um the investors were just kind of you know wanting a little binky baby blanket but they all have modern tech in the cloud and really importantly this shows in the etr surveys you know the momentum that they have so very high retention is the other point i wanted to make the very very low churn of these companies however cooper's management despite the blowout quarter they gave kind of underwhelming guidance they've cited headwinds uh they've with the the the lamisoft uh migration to their cloud platform snowflake is kind of like price to perfection so maybe that's an advantage because every every little negative news is going to going to cause the company to dip but it's you know it's pretty high value because salutman and scarpelli everybody expects them to surpass what happened at servicenow which was a rocket ship and it could be all argued that all three are richly priced and overvalued so but ivana you're looking out as you said a couple of years three years maybe even five years how do you think about the potential downside risks in in your by the dip scenario you buy every dip you looking for bigger dips or what's your framework there so what we try to do is really look every quarter the company reports is there something that's driving fundamental change to the story or is it a one-off situation where people are just misunderstanding what the company is reporting so in the case we kind of addressed some of the earnings that that were reported but with koopa we think the man that management is guiding conservatively as they should so we're not very concerned about their ability to execute on on the guidance and and to exceed the guidance with snowflake price to perfection that's never a good idea to avoid a stock uh because it just shows that there is the company is doing a great job executing right so um we are looking for reports like the cleveland report where they would be like negative on the stock and that would be an entry point uh for us so broadly we apply by the deep philosophy but not not if something fundamentally changes in the story and none of these three are showing any signs of fundamental change okay we're going to leave it right there thanks to my guest today ivana tremendous having you would love to have you back great to see you thank you david and def you definitely want to check out sprx and the spear etf now remember i publish each week on wikibon.com and siliconangle.com these episodes they're all available as podcasts all you do is search breaking analysis podcasts you can always connect with me on twitter i'm at d vallante or email me at david.vellante at siliconangle.com love the comments on linkedin don't forget to check out etr.plus for all the survey action this is dave vellante for the cube insights powered by etr be well and we'll see you next time [Music] you

>>Yeah, >>yeah. Hello and welcome to Day two at Beyond. I am so excited that you've chosen to join the building a vibrant data ecosystem track. I might be just a little bit biased, but I think it's going to be the best track of the day. My name is Mallory Lassen and I run partner Marketing here, a thought spot, and that might give you a little bit of a clue as to why I'm so excited about the four sessions we're about to hear from. We'll start off hearing from two thought spotters on how the power of embrace can allow you to directly query on the cloud data warehouse of your choice Next up. And I shouldn't choose favorites, but I'm very excited to watch Cindy housing moderate a panel off true industry experts. We'll hear from Deloitte Snowflake and Eagle Alfa as they describe how you can enrich your organization's data and better understand and benchmark by using third party data. They may even close off with a prediction or two about the future that could prove to be pretty thought provoking. So I'd stick around for that. Next we'll hear from the cloud juggernaut themselves AWS. We'll even get to see a live demo using TV show data, which I'm pretty sure is near and dear to our hearts. At this point in time and then last, I'm very excited to welcome our customer from T Mobile. They're going to describe how they partnered with whip pro and developed a full solution, really modernizing their analytics and giving self service to so many employees. We'll see what that's done for them. But first, let's go over to James Bell Z and Ana Son on the zero to search session. James, take us away. >>Thanks, Mallory. I'm James Bell C and I look after the solutions engineering and customer success teams have thought spot here in Asia Pacific and Japan today I'm joined by my colleague Anderson to give you a look at just how simple and quick it is to connect thought spot to your cloud data warehouse and extract value from the data within in the demonstration, and I will show you just how we can connect to data, make it simple for the business to search and then search the data itself or within this short session. And I want to point out that everything you're going to see in the demo is Run Live against the Cloud Data Warehouse. In this case, we're using snowflake, and there's no cashing of data or summary tables in terms of what you're going to see. But >>before we >>jump into the demo itself, I just like to provide a very brief overview of the value proposition for thought spot. If you're already familiar with thought spot, this will come as no surprise. But for those new to the platform, it's all about empowering the business to answer their own questions about data in the most simple way possible Through search, the personalized user experience provides a familiar search based way for anyone to get answers to their questions about data, not just the analysts. The search, indexing and ranking makes it easy to find the data you're looking for using business terms that you understand. While the smart ranking constantly adjust the index to ensure the most relevant information is provided to you. The query engine removes the complexity of SQL and complex joint paths while ensuring that users will always get thio the correct answers their questions. This is all backed up by an architecture that's designed to be consumed entirely through a browser with flexibility on deployment methods. You can run thought spot through our thoughts about cloud offering in your own cloud or on premise. The choice is yours, so I'm sure you're thinking that all sounds great. But how difficult is it to get this working? Well, I'm happy to tell you it's super easy. There's just forced steps to unlock the value of your data stored in snowflake, Red Shift, Google, Big Query or any of the other cloud data warehouses that we support. It's a simple is connecting to the Cloud Data Warehouse, choosing what data you want to make available in thought spot, making it user friendly. That column that's called cussed underscore name in the database is great for data management, but when users they're searching for it, they'll probably want to use customer or customer name or account or even client. Also, the business shouldn't need to know that they need to get data from multiple tables or the joint parts needed to get the correct results in thought spot. The worksheet allows you to make all of this simple for the users so they can simply concentrate on getting answers to their questions on Once the worksheet is ready, you can start asking those questions by now. I'm sure you're itching to see this in action. So without further ado, I'm gonna hand over to Anna to show you exactly how this works over to you. Anna, >>In this demo, I'm going to go to cover three areas. First, we'll start with how simple it is to get answers to your questions in class spot. Then we'll have a look at how to create a new connection to Cloud Data Warehouse. And lastly, how to create a use of friendly data layer. Let's get started to get started. I'm going to show you the ease off search with thoughts Spot. As you can see thought spot is or were based. I'm simply lobbying. Divide a browser. This means you don't need to install an application. Additionally, possible does not require you to move any data. So all your data stays in your cloud data warehouse and doesn't need to be moved around. Those sports called differentiator is used experience, and that is primarily search. As soon as we come into the search bar here, that's what suggestion is guiding uses through to the answers? Let's let's say that I would wanna have a look at spending across the different product categories, and we want Thio. Look at that for the last 12 months, and we also want to focus on a trending on monthly. And just like that, we get our answer straightaway without alive from Snowflake. Now let's say we want to focus on 11 product category here. We want to have a look at the performance for finished goods. As I started partially typing my search them here, Thoughts was already suggesting the data value that's available for me to use as a filter. The indexing behind the scene actually index everything about the data which allowed me to get to my data easily and quickly as an end user. Now I've got my next to my data answer here. I can also go to the next level of detail in here. In third spot to navigate on the next level of detail is simply one click away. There's no concept off drill path, pre defined drill path in here. That means we've ordered data that's available to me from Snowflake. I'm able to navigate to the level of detail. Allow me to answer those questions. As you can see as a business user, I don't need to do any coding. There's no dragon drop to get to the answer that I need right here. And she can see other calculations are done on the fly. There is no summary tables, no cubes building are simply able to ask the questions. Follow my train or thoughts, and this provides a better use experience for users as anybody can search in here, the more we interact with the spot, the more it learns about my search patterns and make those suggestions based on the ranking in here and that a returns on the fly from Snowflake. Now you've seen example of a search. Let's go ahead and have a look at How do we create a connection? Brand new one toe a cloud at a warehouse. Here we are here, let me add a new connection to the data were healthy by just clicking at new connection. Today we're going to connect Thio retail apparel data step. So let's start with the name. As you can see, we can easily connect to all the popular data warehouse easily. By just one single click here today, we're going to click to Snowflake. I'm gonna ask some detail he'd let me connect to my account here. Then we quickly enter those details here, and this would determine what data is available to me. I can go ahead and specify database to connect to as well, but I want to connect to all the tables and view. So let's go ahead and create a connection. Now the two systems are talking to each other. I can see all the data that's available available for me to connect to. Let's go ahead and connect to the starter apparel data source here and expanding that I can see all the data tables as available to me. I could go ahead and click on any table here, so there's affect herbal containing all the cells information. I also have the store and product information here I can make. I can choose any Data column that I want to include in my search. Available in soft spot, what can go ahead and select entire table, including all the data columns. I will. I would like to point out that this is important because if any given table that you have contains hundreds of columns it it may not be necessary for you to bring across all of those data columns, so thoughts would allow you to select what's relevant for your analysis. Now that's selected all the tables. Let's go ahead and create a connection. Now force what confirms the data columns that we have selected and start to read the medic metadata from Snowflake and automatically building that search index behind the scene. Now, if your daughter does contain information such as personal, identifiable information, then you can choose to turn those investing off. So none of that would be, um, on a hot spots platform. Now that my tables are ready here, I can actually go ahead and search straight away. Let's go ahead and have a look at the table here. I'm going to click on the fact table heat on the left hand side. It shows all the data column that we've brought across from Snowflake as well as the metadata that also brought over here as well. A preview off the data shows me off the data that's available on my snowflake platform. Let's take a look at the joints tap here. The joint step shows may relationship that has already been defined the foreign and primary care redefining snowflake, and we simply inherited he in fourth spot. However, you don't have toe define all of this relationship in snowflake to add a joint. He is also simple and easy. If I click on at a joint here, I simply select the table that I wanted to create a connection for. So select the fact table on the left, then select the product table onto the right here and then simply selected Data column would wish to join those two tables on Let's select Product ID and clicking next, and that's always required to create a joint between those two tables. But since we already have those strong relationship brought over from Snow Flag, I won't go ahead and do that Now. Now you have seen how the tables have brought over Let's go and have a look at how easy is to search coming to search here. Let's start with selecting the data table would brought over expanding the tables. You can see all the data column that we have previously seen from snowflake that. Let's say I wanna have a look at sales in last year. Let's start to type. And even before I start to type anything in the search bar passport already showing me all those suggestions, guiding me to the answers that's relevant to my need. Let's start with having a look at sales for 2019. And I want to see this across monthly for my trend and out off all of these product line he. I also want to focus on a product line called Jackets as I started partially typing the product line jacket for sport, already proactively recommending me all the matches that it has. So all the data values available for me to search as a filter here, let's go ahead and select jacket. And just like that, I get my answer straight away from Snowflake. Now that's relatively simple. Let's try something a little bit more complex. Let's say I wanna have a look at sales comparing across different regions, um, in us. So I want compare West compared to Southwest, and then I want to combat it against Midwest as well as against based on still and also want to see these trending monthly as well. Let's have look at monthly. If you can see that I can use terms such as monthly Key would like that to look at different times. Buckets. Now all of these is out of the box. As she can see, I didn't have to do any indexing. I didn't have to do any formulas in here. As long as there is a date column in the data set, crossbows able to dynamically calculate those time bucket so she can see. Just by doing that search, I was able to create dynamic groupings segment of different sales across the United States on the sales data here. Now that we've done doing search, you can see that across different tables here might not be the most user friendly layer we don't want uses having to individually select tables. And then, um, you know, selecting different columns with cryptic names in here. We want to make this easy for users, and that's when a work ship comes in. But those were were sheet encapsulate all of the data you want to make available for search as well as formulas, as well as business terminologies that the users are familiar with for a specific business area. Let's start with adding the daughter columns we need for this work shape. Want to slack all of the tables that we just brought across from Snowflake? Expanding each of those tables from the facts type of want sales from the fax table. We want sales as well as the date. Then on the store's table. We want store name as well as the stay eating, then expanding to the product we want name and finally product type. Now that we've got our work shit ready, let's go ahead and save it Now, in order to provide best experience for users to search, would want to optimize the work sheet here. So coming to the worksheet here, you can see the data column that we have selected. Let's start with changing this name to be more user friendly, so let's call it fails record. They will want to call it just simply date, store name, call it store, and then we also want state to be in lower case product name. Simply call it product and finally, product type can also further optimize this worksheet by adding, uh, other areas such as synonyms, so allow users to use terms of familiar with to do that search. So in sales, let's call this revenue and we all cannot also further configure the geo configuration. So want to identify state in here as state for us. And finally, we want Thio. Also add more friendly on a display on a currency. So let's change the currency type. I want to show it in U. S. Dollars. That's all we need. So let's try to change and let's get started on our search now coming back to the search here, Let's go ahead. Now select out worksheet that we have just created. If I don't select any specific tables or worksheets, force what Simply a search across everything that's available to you. Expanding the worksheet. We can see all of the data columns in heat that's we've made available and clicking on search bar for spot already. Reckon, making those recommendations in here to start off? Let's have a look at I wanna have a look at the revenue across different states for here today, so let's use the synonym that we have defined across the different states and we want to see this for here today. Um yesterday as well. I know that I also want to focus on the product line jacket that we have seen before, so let's go ahead and select jacket. Yeah, and just like that, I was able to get the answer straight away in third spot. Let's also share some data label here so we can see exactly the Mount as well to state that police performance across us in here. Now I've got information about the sales of jackets on the state. I want to ask next level question. I want to draw down to the store that has been selling these jackets right Click e. I want to drill down. As you can see out of the box. I didn't have to pre define any drill paths on a target. Reports simply allow me to navigate to the next level of detail to answer my own questions. One Click away. Now I see the same those for the jackets by store from year to date, and this is directly from snowflake data life Not gonna start relatively simple question. Let's go ahead and ask a question that's a little bit more complex. Imagine one. Have a look at Silas this year, and I want to see that by month, month over month or so. I want to see a month. Yeah, and I also want to see that our focus on a sale on the last week off the month. So that's where we see most. Sales comes in the last week off the month, so I want to focus on that as well. Let's focus on last week off each month. And on top of that, I also want to only focus on the top performing stores from last year. So I want to focus on the top five stores from last year, so only store in top five in sales store and for last year. And with that, we also want to focus just on the populist product types as well. So product type. Now, this could be very reasonable question that a business user would like to ask. But behind the scenes, this could be quite complex. But First part takes cares, or the complexity off the data allow the user to focus on the answer they want to get to. If we quickly have a look at the query here, this shows how forceful translate the search that were put in there into queries into that, we can pass on the snowflake. As you can see, the search uses all three tables as well shooting, utilizing the joints and the metadata layer that we have created. Switching over to the sequel here, this sequel actually generate on the fly pass on the snowflake in order for the snowflake to bring back to result and presented in the first spot. I also want to mention that in the latest release Off Hot Spot, we also bringing Embraced um, in the latest version, Off tosspot 6.3 story Q is also coming to embrace. That means one click or two analysis. Those who are in power users to monitor key metrics on kind of anomalies, identify leading indicators and isolate trends, as you can see in a matter of minutes. Using thought spot, we were able to connect to most popular on premise or on cloud data warehouses. We were able to get blazing fast answers to our searches, allow us to transform raw data to incite in the speed off thoughts. Ah, pass it back to you, James. >>Thanks, Anna. Wow, that was awesome. It's incredible to see how much committee achieved in such a short amount of time. I want to close this session by referring to a customer example of who, For those of you in the US, I'm sure you're familiar with who, Lou. But for our international audience, who Lou our immediate streaming service similar to a Netflix or Disney Plus, As you can imagine, the amount of data created by a service like this is massive, with over 32 million subscribers and who were asking questions of over 16 terabytes of data in snow folk. Using regular B I tools on top of this size of data would usually mean using summary or aggregate level data, but with thoughts. What? Who are able to get granular insights into the data, allowing them to understand what they're subscribes of, watching how their campaigns of performing and how their programming is being received, and take advantage of that data to reduce churn and increase revenue. So thank you for your time today. Through the session, you've seen just how simple it is to get thought spot up and running on your cloud data warehouse toe. Unlock the value of your data and minutes. If you're interested in trying this on your own data, you can sign up for a free 14 day trial of thoughts. What cloud? Right now? Thanks again, toe Anna for such awards and demo. And if you have any questions, please feel free to let us know. >>Awesome. Thank you, James and Anna. That was incredible. To see it in action and how it all came together on James. We do actually have a couple of questions in our last few minutes here, Anna. >>The first one will be >>for you. Please. This will be a two part question. One. What Cloud Data Warehouses does embrace support today. And to can we use embrace to connect to multiple data warehouses. Thank you, Mallory. Today embrace supports. Snowflake Google, Big query. Um, Red shift as you assign that Teradata advantage and essay Bahana with more sources to come in the future. And, yes, you can connect on live query from notable data warehouses. Most of our enterprise customers have gotta spread across several data warehouses like just transactional data and red Shift and South will start. It's not like, excellent on James will have the final question go to you, You please. Are there any size restrictions for how much data thought spot can handle? And does one need to optimize their database for performance, for example? Aggregations. >>Yeah, that's a great question. So, you know, as we've just heard from our customer, who there's, there's really no limits in terms of the amount of data that you can bring into thoughts Ponant connect to. We have many customers that have, in excess of 10 terabytes of data that they're connecting to in those cloud data warehouses. And, yeah, there's there's no need to pre aggregate or anything. Thought Spot works best with that transactional level data being able to get right down into the details behind it and surface those answers to the business uses. >>Excellent. Well, thank you both so much. And for everyone at home watching thank you for joining us for that session. You have a few minutes toe. Get up, get some water, get a bite of food. What? You won't want to miss this next panel in it. We have our chief data strategy off Officer Cindy, Housing speaking toe experts in the field from Deloitte Snowflake and Eagle Alfa. All on best practices for leveraging external data sources. See you there

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Live coverage here for eight of us. Reinvent 2020. Virtual normally were on the show floor getting all of the interviews and talking about the top newsmakers and we have one of them here on the Cube were remote. I'm John for your host of the Cube. George Ellis Eros, GM and director of product manager for AWS. Talking about Wavelength George. Welcome to the remote Cube Cube. Virtual. Thanks for coming on. >>Good to be here. Thanks for having a John >>Eso Andy's Kino. One of the highlights last year, I pointed out that the five g thing is gonna be huge with the L A Wavelength Metro thing going on this year. Same thing. Mawr Proofpoint S'more expansion. Take us through what was announced this year. What's the big update on wavelength? >>Yes, so John Wavelength essentially brings a W services at the edge of the five G network, allowing our AWS customers and developers to reach their own end users and devices. Five devices with very low latency enabling a number off emerging applications ranging from industrial automation and I O. T. All the way to weigh AR VR smart cities, connected vehicles and much more this year we announced earlier in the year the general availability of wavelength in two locations one in the Bay Area and one in the Boston area. And since then we've seen we've been growing with Verizon or five D partner in the U. S. And and increasing that coverage in multiple off the larger U. S cities, including Miami and D. C in New York. And we launched Las Vegas yesterday at Andy's keynote with Verizon. We also announced that we are going toe to have a global footprint with K d D I in Japan launching a wavelength in Tokyo with SK detail SK Telecom in in South Korea or launching indigestion and with Vodafone in London >>so significant its expansion. Um, we used to call these points of presence back in the old days. I don't know what you call them now. I guess they're just zones like you calling them zones, but this really is gonna be a critical edge network, part of the edge, whether it's stadiums, metro area things and the density and the group is awesome. And everyone loves at about five gs. More of a business at less consumer. When you think about it, what has been some of the response as you guys had deployed mawr, What's the feedback? Um, can you take us through what the response has been? What's it been like? What have been some of the observations? >>Yeah, customers air really excited with the promise of five G and really excited to get their hands on these new capabilities that we're offering. Um, And they're telling us, you know, some consistent feedback that we're getting is that they're telling us that they love that they can use the same A W s, a P I S and tools and services that they used today in the region to get their hands on this new capabilities. So that's being pretty pretty consistent. Feedback these off use and the you know, Sometimes customers tell us that within a day they are able to deploy their applications in web. So that's a that's pretty consistent there. We've seen customers across a number of areas arranging, you know, from from manufacturing to healthcare to a ar and VR and broadcasting and live streaming all the way to smart cities and and connected vehicles. So a number of customers in these areas are using wavelength. Some of my favorite you know, examples are in in actually connected vehicles where you really can see that future materialized. You get, you know, customers like LG that are building the completely secularized vehicle, tow everything platform, and customers like safari that allow multiple devices to do, you know, talkto the Waveland, the closest Waveland Zone process. All of those device data streams at the edge. And then, um, it back. You know messages to the drivers, like for emergency situations, or even construct full dynamic maps for consumption off the off the vehicle themselves. >>I mean, it's absolutely awesome. And, you know, one of things that someone Dave Brown yesterday around the C two and the trend with smaller compute. You have the compute relationship at the edge to moving back and forth so I can see those dots connecting and looking forward to see how that plays out. Sure, and it will enable more capabilities. I do want to get your your thoughts, or you could just for the audience and our perspective just define the difference between wavelength and local zones because we know what regions are. Amazon regions are well understood all around the world. But now you have this new concept called locals owns part of wavelength, not part of wavelengths. Are they different technology? Can you just explain? Take him in to exclaim wavelength versus local zones how they work together? >>Yeah, So let me take a step back at AWS. Basically, what we're trying to do is we're trying to enable our customers to reach their end users with low latency and great performance, wherever those end users are and whatever network they're they're using to get connected, whether that's the five g mobile network with the Internet or in I o t Network. So we have a number of products that help our customers do that. And we expect, like, in months off other areas of the AWS platform, that customers are gonna pick and twos and mix and match and combine some of these products toe master use case. So when you're talking about wavelength and local zones, wavelength is about five g. There is obviously a lot off excitement as you said yourself about five g about the promise off those higher throughput. They're Lowell agencies. You know, the large number of devices supported and with wavelengths were enabling our customers toe to make the most of that. You know, of the five G technology and toe work on these emerging new use cases and applications that we talked about When it comes to local zones, we're talking more about extending AWS out two more locations. So if you think about you mentioned AWS regions, we have 24 regions in another five coming. Those are worldwide and enabled most of our customers to run their workloads. You know all of their workloads with low latency and adequate performance across the world. But we are hearing from customers that they want AWS in more locations. So local zones basically bring a W S extend those regions to more locations by bringing a W s closer to population I t and industrial centers. You know, l A is a great example of that. We launched the lay last year toe to local zones in L. A and toe toe a mainly at the media and entertainment customers that are, you know, in the L. A Metro, and we've seen customers like Netflix, for example, moving their artist workstations to the local zones. If they were to move that somewhere, you know, to the cloud somewhere further out the Laden's, he might have been too much for their ass artists work clothes and having some local AWS in the L. A. Metro allows them to finally move those workstation to the cloud while preserving that user experience. You know, interacting with the workstations that's happened. The cloud. >>So just like in conceptualizing is local zone, like a base station is in the metro point of physical location. Is it outpost on steroids? Been trying to get the feel for what it is >>you can think off regions consisting off availability zones. So these are, you know, data center clusters that deliver AWS services. So a local zone is much like an availability zone. But instead of being co located with the rest of the region, is in another locations that, for example, in L. A. Rather than being, you know, in in Virginia, let's say, um, they are internally. We use the same technology that we use for outpost, I suppose, is another great example of how AWS is getting closer to customers for on premises. Deployments were using much of the same technology that you you probably know as Nitro System and a number of other kind of technology that we've been working on for years, actually, toe make all this possible. >>You know, anyone who's been to a football game or any kind of stadium knows you got a great WiFi signal, but you get terrible bandwidth that is essentially kind of the back hall component for the telecom geeks out there. This is kind of what we're talking about here, right? We're talking about more of an expansionary at that edge on throughput, not just signal. So there's, you know, there's there's a wireless signal, and it's like really conductivity riel functionality for applications. >>Yeah, and many. Many of those use case that we're talking about are about, you know, immersive experiences for for end users. So with five t, you get that increasing throughput, you can get up to 10 GPS. You know, it is much higher with what you get 40. You also get lower latents is, but in order to really get make the most out of five G. You need to have the cloud services closer to the end user. So that's what Wavelength is doing is bringing all of those cloud services closer to the end user and combined with five G delivers on these on these applications. You know, um, a couple of customers are actually doing very, very, very exciting things on immersive application, our own immersive experiences. Um, why be VR is a customer that's working on wavelength today to deliver a full 3 60 video off sports events, and it's like you're there. They basically take all of those video streams. They process them in the waving zone and then put them back down to your to your VR headset. But don't you have seen those? We are headsets there, these bulky, awkward, big things because we can do a lot of the processing now at the edge rather than on the heads of itself. We are envisioning that these headsets will Will will string down to something that's indistinguishable potential from, you know, your glasses, making that user experience much better. >>Yeah, from anything from first responders toe large gatherings of people having immersive experiences, it's only gonna get better. Jorge. Thanks for coming on. The Cuban explaining wavelength graduates on the news and expansion. A lot more cities. Um, what's your take for reinvent while I got you? What's the big take away for you this year? Obviously. Virtual, but what's the big moment for you? >>Well, I think that the big moment for me is that we're continuing to, you know, to deliver for our customers. Obviously, a very difficult year for everyone and being able to, you know, with our help off our customers and our partners deliver on the reinvent promised this year as well. It is really impressed for >>me. All right. Great to have you on. Congratulations on local news. Great to see Andy pumping up wavelength. Ah, lot more work. We'll check in with you throughout the year. A lot to talk about. A lot of societal issues and certainly a lot of a lot of controversy as well as tech for good, great stuff. Thanks for coming. I appreciate it. >>Thanks for having me. Thanks. >>Okay, That's the cube. Virtual. I'm John for your host. Thanks for watching. We'll be back with more coverage from reinvent 2023 weeks of coverage. Walter Wall here in the Cube. Thanks for watching. Yeah,

>>from around the globe. It's the Cube with digital coverage of AI ops Virtual Forum Brought to you by Broadcom Welcome back to the AI Ops Virtual Forum. Lisa Martin here with Srinivasan Rajagopal, the head of products and strategy at Broadcom Raj Welcome. >>Good to be here, Lisa. >>I'm excited for our conversation, so I wanted to dive right into a term that we hear all the time. Operational excellence, right? We hear it everywhere in marketing, etcetera. But why is it so important to organizations as they head into 2021 tell us how ai ops as a platform can help? >>Yeah. Thank you. First off, I wanna I wanna welcome our viewers back and I'm very excited Toe share more info on this topic. You know, here's what we believe. As we work with large organizations, we see all our organizations are poised toe get out off the pandemic and look for growth for their own business and helping customers get through this tough time. So fiscal year 2021 we believe, is going to be a combination off, you know, resiliency and agility at the at the same time. So operational excellence is critical because the business has become mawr digital, right? There are gonna be three things that are gonna be more sticky. You know, remote work is gonna be more sticky. Um, cost savings and efficiency is going to be an imperative for organizations. And the continued acceleration of digital transformation off enterprises at scale is going to be in reality. So when you put all these three things together as a team, that is, you know, that's working behind the scenes toe help the businesses succeed. Operational excellence is going to be make or break for organizations. >>Russia with that said, if we kind of strip it down to the key capabilities, what are those key capabilities that companies need to be looking for in an AI ops solution? >>Yeah, you know. So first foremost AI ops means many things to many, many folks. So let's take a moment to simply define it. The way we defined AI ops is it's a system off intelligence human augmented system that brings together full visibility across app, infra and network elements that brings together despite of data sources on provides actionable intelligence and uniquely offers intelligent automation. Now the technology many folks draw is the self driving car, right? I mean, we are in the world of Tesla's, but, you know, but self driving data center is is too far away, right? Autonomous systems are still far away. However, you know, application off the I M l techniques toe help deal with volume velocity, veracity of information. Eyes is critical. So that's how we look at AI ops and some of the key capabilities that we that we that we work with our customers to help them around 48 years. Right? First one is eyes and years. What we call full stack, observe ability. If you do not know what is happening in your systems, you know that that serve up your business services, it's gonna be pretty hard to do anything in terms of responsiveness, right? So from stack of their ability, the second piece is what we call actionable insights. So when you have disparaged data sources, tool sprawls, data coming at you from, you know from database systems, I T systems, customer management systems, ticketing systems, how do you find the needle from the haystack? And how do you respond rapidly from a myriad off problems? A sea off read The third area is what we call intelligent automation. Well, Identifying the problem toe Act on is important and then acting on. Automating that and creating a recommendation system where you know you could be proactive about that is even more important. And finally, all of this focuses on efficiency. What about effectiveness? Effectiveness comes when you create a feedback loop when what happens in production is related to your support systems and your developers so that they can respond rapidly. So we call that continuous feedback. So these are the four key capabilities that you know you should look for in an AI ops system. And that's what we offer us. >>Alright, Russia. There's four key capabilities that businesses need to be looking for. I'm wondering how those help to align business and i t. It's again like operational excellence. It's something that we talk about a lot is the alignment of business and I t a lot more challenging. Is your something done right? But I want you to explain how can a iob help with that alignment and align? I t outputs to business outcomes. >>So you know, one of the things I'm going to say something that this, that is that is simple. But it's harder. Alignment is not on systems. Alignment is with people, right? So when people align when organizations aligned, when cultures align, dramatic things can happen. So in the context off AI ops, we see when when saris aligned with the develops engineers and information architects. And, uh, you know, I t operators, you know, they enable organizations to reduce the gap between intent and outcome or output an outcome that said, you know, these personas need mechanisms toe help them better align, right, help them Better visual. I see the you know what we call single source of truth, right? So there are four key things that I wanna call out when we work with large enterprises. We find that customer journey alignment with the you know what we call I T systems is critical. So how do you understand your business imperatives and your customer journey goals? Whether it is card toe purchase or whether it is, you know, Bill shock scenarios and swan alignment on customer journey to your I T systems is one area that you can reduce the gap. The second area is how do you create a scenario where your teams can find problems before your customers do right out. It's scenarios and so on. So that's the second area off alignment. The third area off alignment is how can you measure business impact driven services right? There are several services that an organization off course as the 19 system. Some services are more critical to the business. Well, then, others and thes change in a dynamic environment. So how do you How do you understand that? How do you measure that? And how? How do you find the gaps there? So that that's the 3rd 80 off alignment that we that we help. And last but not least, there are. There are things like NPS scores and others that that help us understand alignment. But those are more long term. But in the in the context off, you know, operating digitally. You want to use customer experience and, you know single business outcome as as a key alignment factor, and then work with your systems of engagement and systems of interaction, along with your key personas to create that alignment. It's a people process technology challenge, actually. >>So where is one of the things that you said there is that it's imperative for the business toe. Find a problem before a customer does. And you talked about outages there. That's always a goal for businesses, right to prevent those outages. How can Ai ops help with that? >>Yeah, so, you know, out they just talk, you know, go to resiliency off a system, right? And they also goto have, you know, agility off the same system. You know, if you are a customer and if you're ripping up your mobile happened, it takes more than you know, three milliseconds. You know, you're probably losing that customer, right? So I would just mean different things, you know? And there's an interesting website called don't detector dot com that actually tracks all the outages of publicly available services, whether it's your bank or your, you know, telecom service or mobile service and so on and so forth. In fact, the key question around outages for from from you know, executives are the question of Are you ready? Right? Are you ready to respond to the needs off your customers and your business? Are you ready toe rapidly to solve an issue that is impacting customer experience and therefore satisfaction. Are you creating a digital trust system where customers can be, You know, you know, customers can feel that their information is secure when they transact with you. All of these getting toe the notion of resiliency and outages. Now, you know, one of the things that I often you know work with customers around, you know, that we find is the radius off. Impact is important when you deal with outages. What I mean by that is problems occur, right? How do you respond? How quickly do you take? Two seconds? Two minutes, 20 minutes. Two hours, 20 hours. Right To resolve that problem. That radius of impact is important. That's where you know you have to bring again. People process technology together to solve that. And the key thing is, you need a system of intelligence that can aid you your teams, you know, look at the same set of parameters so that you can respond faster. That's the key here. >>But as we look at digital transformation at scale, Raj, how does a apps help influence that? >>You know, I'm gonna take a slightly long winded way to answer this question. See, when it comes to digital transformation at scale, the focus on business purpose and business outcome becomes extremely critical. And then the alignment off that to your digital supply chain right are the are the are the key factors that differentiate vintners in the in their digital transformation game. Really? What we have seen with with winners is they operate very differently. Like, for example, you know, 19 assures its digital business outcomes by shoes per second, right apple buy iPhones per per minute. Tesla by model threes per month. Are you getting getting it right? I mean, you wanna have, ah, clear business outcome, which is a measure off your business. In effect, I mean, easy right, which which my daughter use. And I use very well, right? You know, they measured by revenue per hour, right? I mean, so these are key measures, and when you have a key business outcome measure like that, you can align everything else because you know what these measures you know, for a bank, it may be deposits per month. Right now, when you move money from checking account to savings account or when you do direct deposits, those are you know, banks need liquidity and so on and so forth. But, you know, the key thing is that single business outcome has a starburst effect inside the I T. Organization that touches a single money movement from checking account to savings account can touch about 75 disparage systems internally. Right? So those think about right. I mean, all we're doing is moving money from checking accounts savings account. Now that goats in tow, a IittIe production system, there are several applications. There is a database there is there are infrastructures, their load balancers, that our webs, you know, the Web server components, which then touches your your middleware component, which is a queuing system right, which then touches your transactional system on. Do you know which may be on your mainframes what we call mobile toe mainframe scenario, right? And we're not done yet. Then you have a security and regulatory compliance system that you have to touch a fraud prevention system that you have to touch right, a State Department regulation that you may have to meet and on and on and on, right? This is the challenge that I t operation teams phase. And when you have millions of customers transacting right? Certainly this challenge cannot be, you know, managed by, you know, human beings alone. So therefore, you need a system off intelligence that augments human intelligence and acts as you, you know, your your eyes and ears in of a toe point pinpoint. Their problems are right. So digital transformation at scale really requires a well thought out ai ops system a platform and open extensible platform that you know, that is heterogeneous in nature because their stools problems in organizations. There is, uh, you know, a lot of data bases in systems. There are million's off, you know, customers and hundreds off partners and vendors, you know, making up that digital supply chain. So, you know, AI ops is at the center off, enabling an organization achieved digital up, you know, transformation at scale. Last but not least, you need continuous feedback loop. Continuous feedback loop is the ability for a production system toe. Inform your develops teams your finance teams, your customer experience teams your cost Modeling teams about what is going on say that they can so that they can reduce the intent outcome gap. All of this need to come together. What we call biz obs for ideal abs. >>That was a great example of how you talked about the Starburst effect. Actually never thought about it in that way. When you give the banking example but what you should is the magnitude of systems, the fact that people alone really need help with that and why intelligent automation and air ops could be transformative and enable that scale. Raj, it's always a pleasure to talk with you. Thanks for joining me today. Yeah, >>great to be here >>and we'll be right back with our next segment.

>>from around the globe. It's the Cube with digital coverage of AI ops Virtual Forum Brought to you by Broadcom Welcome back to the Broadcom AI Ops Virtual Forum Lisa Martin here talking with Usman Naseer Global Product Management at Verizon we spend Welcome back. >>Uh huh. Hello, Good >>to see you. So 2020 The year of that needs no explanation. With the year of massive challenges, I wanted to get your take on the challenges that organizations are facing this year as the demand to deliver digital products and services has never been higher. >>Yeah, I e I think this is something is so close to all the part part right? It's something that's impacted the whole world equally. And I think regardless off which industry you win, you have been impacted by this in one form or the other and the i c t industry, the information and communication technology industry. You know, Verizon being really massive player in that whole arena, it has just been sort of struck with this massive confirmation that we have talked about for a long time. We have talked about these remote surgery capabilities whereby you got patients in Kenya were being treated by experts sitting in London or New York and also this whole consciousness about, you know, our carbon footprint and being environmentally conscious. This pandemic has taught a school of that and brought this to the forefront off organizational priority, right? The demand. I think that Zaveri natural consequence of everybody sitting at home. And the only thing that can keep things still going is the data communication, Right? But I would just say that that is what kind of at the heart of all of this. Just imagine if we are to realize any of these targets that the world is world leadership is setting for themselves. Hey, we have >>to be carbon >>neutral by Xia as a country as a geography, etcetera etcetera. You know, all of these things require you to have this remote working capability this remote interaction, not just between human but machine to machine interaction. And this is a unique value chain which is now getting created that you've got people we're communicating with other people or were communicating with other machines. But the communication is much more. I won't even use the term really time because we've used real time for voice and video, etcetera. We're talking low latency microsecond to see and making that can either cut somebody's, you know, um, our trees or that could actually go and remove the tumor, that kind of stuff. So that has become a reality. Everybody's asking for it. Remote learning, being an extremely massive requirement where, you know, we've had to enable these thes virtual classrooms ensuring the type of connectivity, ensuring the type of type of privacy which is just so, so critical. You can't just have everybody you know, Go on the internet and access the data source. You have to be. I'm sorry about the integrity and security of >>that. They've >>had the foremost. So I think all of these things, Yes. We have not been caught off guard. We were should be pretty forward looking in our, you know, plans in our evolution. But yes, it does this fast track a journey that we would probably the least we would have taken in three years. It has brought that down to two quarters where we had to execute them. >>Right? Massive acceleration. All right, so you articulated the challenges really well and a lot of the realities that many of our viewers air facing. Let's talk now about motivations ai ops as a tool as a catalyst for helping organizations overcome those challenges. >>So, yeah, now all that I said you can imagine, you know, it requires microsecond the sea and making which human being on this planet can do microsecond the sea and making on complex network infrastructure, which is impacting, and user applications which have multitudes off effect. You know, in real life, I used the example of a remote surgeon. Just imagine, if you know, even because you just lose your signal on the quality of that communication for that microsecond, it could be the difference between killing somebody in saving somebody's life. Is that particular? We talk about autonomous vehicles way talk about the transition to electric vehicles, smart motorways, etcetera, etcetera in federal environment. How is all of that going to work? You have so many different components coming in. You don't just have a natural can security anymore. You have software defined networking that's coming becoming a part of this. You have mobile edge computing that is rented for the technologies. Five g enables we're talking augmented reality. We're talking virtual reality all of these things require that resource is. And while we carbon conscious, we don't just wanna build a billionaire, a terrorist on the planet, right? We we have to make sure that resource is air given on demand and the best way of re sources can be given on demand and could be most efficient. Is that we're making is being made at million microsecond. And those resource is our accordingly being distribute. Right? If you're 10 flying on, people sipping their coffee is having teeth talking to somebody else. You know, just being away on holiday. I don't think we're gonna be able to handle that world that we have already stepped into. Risen's five g has already started businesses on the transformational journey where they're talking about end user experience, personalization. You're gonna have, you know, events where people are going to go. And it's going to be three dimensional experiences that are purely customized for you. How How does that all happen without this intelligence having their and a network with all of these multiple layers assaults spectrum, it doesn't just need to be intuitive. Hey, this is my private I p traffic. This is public traffic. You know it has to now be into or this is an application that to privatize over another has to be intuitive to the criticality in the context, off those transactions again that surgeons surgery is much more important than husband sitting and playing a video game. >>Yeah, I'm glad that you think that that's excellent. Let's go into some specific use cases. What are in some of the examples that you gave? Let's kind of dig deeper into some of that. What you think are the lowest hanging fruit for organizations, kind of pan industry to go after here. >>Excellent, right? And I think this just like different ways to look at the lowest timing food. Like for somebody like Verizon, who is the managed services provider, you know, very comprehensive medicines. But we obviously have food timing much lower than potentially for some of our customers who want to go on that journey, right? So for them to just >>go and try and >>harness the power of help, the food's might be a bit higher hanging. But for somebody like God, the immediate ones would be to reduce the number off alarms that are being generated by these overlays services. You've got your basic network. Then you've got your software defined networking. On top of that, you have your hybrid clouds. You have your edge computing coming on top of that, you know? So ALOF this means if there is an outrage on one device on the network, gonna make this very real for everybody, right? It's right out. I'm not divisive. Network does not stop all of those multiple applications for monitoring tools from raising havoc and raising thousands off alarms and everyone capacity. If people are attending to those thousands off alarms, it's like you having a police force. And there's a burglary in one bank and the alarm goes off in $50. How you gonna make the best use of your police force? You're gonna go investigate 50 banks? You wanna investigate one where the problem is. So it's as realize that and I think that's the first wind where people can save so much cost, which is currently being wasted. And resource is running around primary figure stuff up immediately. Anti this with network and security network and security is something which has eluded even the most. You know, amazing off brings in or engineering. Well, we took it. We have network expert, separate people. Security experts separate people to look for different things. But there are security events that can impact the performance of the network and then use your application, cetera, etcetera, which could be falsely attributed to the network. And then if you've got multiple parties, which are then which have to clear stakeholders, you can imagine the blame game that goes on pointing fingers, taking names, not taking responsibility. That is how all this happened. This is the only way to bring it all together to say Okay, this is what takes priority. If there's an event that has happened, what is its correlation to the other downstream systems, devices, components and user applications. And it subsequently, you know, like isolating into the right cause where you can most effectively resolve that problem. Certainly, I would say on demand virtualized resource virtualized resource is the heart and soul of the spirit of status that you can have them on them up so you can automate the allocation of these. Resource is based on, you know, customers consumption, their peaks, their crimes. All of that comes in. You see Hey, typically on a Wednesday, their traffic goes up significantly from this particular application. You know, going to this particular data center, you could have this automated this AI ops, which is just providing those resource, is, you know, on demand and tell us to have a much better commercial engagement with customers and just a much better service assurance model. And then one more thing on top of that, which is very critical, is that, as I was saying, giving that intelligence to the network to start having context of the criticality of a transaction that doesn't exist to it. You can't have that because for that you need to have this, you know, multi layer data. You need to have multiple system which are monitoring and controlling different aspects of your overall and user application value chain to be communicating with each other. And, you know, that's that's the only way to sort of achieve that goal. And that only happens with AI off. It's not possible with them. You can paradise Comdex. >>So Guzman, you clearly articulated some obvious low hanging for use cases that organizations can go after. Let's talk now about some of the considerations you talked about the importance of the network in AI ops. The approach, I assume, needs to be modular support needs to be heterogeneous. Talk to us about some of those key considerations that you would recommend >>absolutely. So again, basically starting with the network. Because if there is, if the network sitting at the middle of all of this is not working, then things from communicate with each other, right? And the cloud doesn't work. Nothing. None of this person has hit the hardest all of this. But then subsequently, when you talk about machine to machine communication or i o T. Which is the biggest transformation to spend, every company is going priority now to drive those class efficiencies enhancements. We've got some experience. The integrity off the tab becomes paramount, right? The security integrity of that. How do you maintain integrity off your detail beyond just the secured network components that Trevor right? That's where you get into the whole arena Blockchain technology where you have these digital signatures or barcodes that machine then and then an intelligent system is automatically able to validate and verify the integrity of the data and the commands that are being executed by those and you determine. But I think the terminal. So I o. T machines, right, that is paramount. And if anybody is not keeping that into their equation, that in its own self, is any eye off system that is therefore maintaining the integrity off your commands and your quote that sits on those those machines Right. Second, you have your network. You need to have any off platform, which is able to rationalize all the fat network information, etcetera. And couple that with that. The integrity peace. Because for the management, ultimately, they need to have a co haven't view off the analytics, etcetera, etcetera. They need to. They need to know where the problems are again, right? So let's see if there's a problem with the integrity off the commands that are being executed by a machine. That's a much bigger problems than not being able to communicate with that machine. And the first thing because you'd rather not talk to the machine or haven't do anything if it's going to start doing the wrong thing, So I think that's where it's just very intuitive. It's natural. You have to have subsequently if you have some kind of say and let me use that use case Off Autonomous comes again. I think we're going to see in the next five years it's much water rates, etcetera. It will set for autonomous because it's much more efficient. It's much more space, etcetera, etcetera. So whether that equation you're gonna have systems which will be specialist in looking at aspects and Trump's actions related to those systems, for example, an autonomous moving vehicle's brakes are much more important than the Vipers, Right? So this kind of intelligence, there will be multiple systems who have to sit and nobody has to. One person has to go and on these systems, I think these systems should be open source enough that you are able to integrate them, right? If something sitting in the cloud you were able to integrate for that with obviously the regard off the security and integrity off their data, that has two covers from one system to the extremely. >>So I'm gonna borrow that integrity theme for a second as we go into our last question. And that is this kind of take a macro. Look at the overall business impact that AI ops can help customers make. I'm thinking of, you know, the integrity of teams aligning business and I t. Which we probably can't talk about enough. We're helping organizations really effectively measure KP eyes that deliver that digital experience that all of us demanding consumers expect. What's the overall impact? What would you say in separation? >>So I think the overall impact is a lot. Of course, that customers and businesses give me term got prior to the term enterprises defense was inevitable. There's something that for the first time will come to light. And it's something that is going to, you know, start driving cost efficiencies and consciousness and awareness within their own business, which is obviously going to have, you know, abdominal kind of an effect. So what example being that, you know, you have a problem? Isolation? I talked about network security, this multilayered architectural which enables this new world of five g um, at the heart of all of it. It is to identify the problem to the source, right? Not be bogged down by 15 different things that are going wrong. What is causing those 15 things to go wrong, right that speed to isolation and its own self can make millions and millions off dollars to organizations every organization. Next one is obviously overall impacted customer experience. The five g waas. You can have your customers expecting experiences from you, even if you're not expecting to deliver them in 2021 2022. You'll have customers asking for those experiences or walking away if you do not provide those experiences. So for it's almost like a business can do nothing. Every year they don't have to reinvest if they just want to die on the wine. Businesses want to remain relevant. Businesses want to adopt the latest and greatest in technology, which enables them to, you know, have that superiority and continue it. So from that perspective that continue ity, we're ready that there are intelligence system sitting, rationalizing information and making this in supervised by people, of course, who were previously making some of those here. >>That was a great summary because you're right, you know, with how demanding consumers are. We don't get what we want. Quickly we turn right, we go somewhere else, and we could find somebody that can meet those expectations. So it was spent Thanks for doing a great job of clarifying the impact and the value that AI ops can bring to organizations. That sounds really now is we're in this even higher demand for digital products and services, which is not going away. It's probably going to only increase. It's table stakes for any organization. Thank you so much for joining me today and giving us your thoughts. >>Pleasure. Thank you. >>We'll be right back with our next segment.

>>Hi, everyone. And thanks for joining us today for our session on the 2020 Data Drivers Award winners. I'm excited to be here today with you. I'm a lease. Bergeron, vice president, product marketing for snowflake. Thes rewards are intended to recognize companies and individuals for using snowflakes, data cloud to drive innovation and impact in their organizations. Before we start our conversations, I want to quickly congratulate all of our award winners. First in the business awards are data driver of the year is Cisco. Our machine learning master is you Nipper, Our data sharing leader is Rakuten. Our data application of the year is observed and our data for good award goes to door dash for the individual and team awards. We first have the cost. Jane, Chief Digital officer of Paccar. We have a militiamen, director of cybersecurity and data science winning our data science Manager of the Year award at Comcast for a date. A pioneer of the year. We have Faisal KP, who's our senior manager of enterprise data Services at Pizza Hut. And lastly, we have our best data team going to McKesson, led by Jimmy Herff Data and Analytics platform leader Huge congratulations to all of these winners. It was very difficult to pick them amongst amazing set of nominations. So now let's dive into our conversations. We'll start with the data driver of the year. Representing Cisco today is Robbie. I'm a month do director data platform, data and analytics. >>Let me welcome everybody to the wonderful. Within a few years before Cisco used to be a company, you know, in making the decisions partly with the data and partly with the cuts. Because, you know, the data is told in multiple places the trading is not done right and things like that. So we, you know, really understood it. You know what was a challenge in the organism? By then we defined the data strategy on we put in a few plants in place, and it is working very well. But what is more important is basically how we provide the data towards data scientists and the data community in Cisco. I'm making them available in a highly available scalable on the elastic platforms. That's where you know, snowflake came into picture really very well for arrest, along with the other data strategies that we have had in place more importantly, data. Democratization was a key. You know, you along with the simplification, something technologies involved in the past. Our clients need to be worrying, laudable the technologies involved, you know, for example, we used to manage her before we make it. Snowflake Andi Snowflake, in a solve all of these problems for us with the ease on it. Really helping enabling a data data given ordinances in our >>system. In the data sharing leaders category, Rockhampton was our winner. We have mark staying trigger VP of analytics here to share their story. I >>wanna thank Snowflake for the award, and it's an honor to be a today. The ease of use of snowflake has allowed projects to move forward innovation to move forward in a way that it simply couldn't have done on old Duke systems or or or other platforms. And I think the truth the same is true for us on a lot of the similar topics, but also in the data sharing space, data sharing is a part off innovation. Like I think, most of the tech companies we work with certainly are business partners, merchants, but also with a range of other service providers and other technology vendors, um on other companies that we strategically share data with 2 May benefit of their service or thio to allow data modeling or advanced data collaboration or strategic business deals using the data and evaluated with the data on. But I think if you look Greece snowflake, you would see a lot of time and effort money going to just establishing that data connection that often involved substantial investments in technology data pipelines, risk evaluation, hashing, encrypt encryption. Security on what we found with snowflakes sharing functionality is that we can not eliminate those concerns, but that the technology just supports the ability to share data securely easily, quickly in a way that we could never do >>previously. Now we have a really inspiring winner of the data for good award door dash with their Project Dash Initiative here to speak about their work is act shot near Engineering manager >>Thank you sports to snowflake for recognizing us for this initiative. Eso For those of you who don't know, Dash, the logistics technology platform company that connects people with the best in their cities and Project Dash, our flagship social impact program, uses the door dash logistics platform to tackle the challenges like hunger and food waste. It was launched in 2018 on over the first two years in partnership with food recovery organizations, we powered the delivery off over £2 million of surplus food from businesses to hunger relief agencies across the U. S. And Canada. Andi simply do Toko with tremendous need has a much we were ableto power. The delivery often estimated 5.8 million meals to food insecure communities and frontline workers across 48 states on the 3.5 million off. These meals have been delivered since much. We do all of our analysis for our business functions from like product development to skills and social impact in snowflake On the numbers I just provided here actually have come from Snowflake on. We have used it to provide various forms of reporting, tow our government and non profit partners on this snowflake. We can help them understand the impact, analyzed friends and ensure complaints in cases where we are supporting efforts for agencies like FEMA, our USDA onda. Lastly, our team is really excited to be recognized by snowflake for using data for good. It has reminded us to continue doubling down on our commitment to using our product and expertise to partner with communities we operated. Thank you again. >>The winner of the machine Learning Master's word is unit for Energy. Viola Sarcoma Data Innovation leader is here on behalf of unit for >>Hello, everyone, Thanks for having me here. It's really a pleasure. And we were really proud to get this award. It means a lot for you. Nipper. It's huge recognition for our effort since last couple of years assed part of our journey and also a celebration off our success now for you. Newport. It would not be possible to start looking at Advanced Analytics techniques, not having a solid data foundation in place. And that's where we invested a lot in our cloud data platform in the cloud back by snowflake. Having this platform allowed us to employ advanced analytics techniques, combining data from Markit from fundamental data, different other sources of data like weather and extracting new friends, new signals that basically help us to partly or even in some cases fully automate some trading strategy. And we believe this will be really fundamental for for the future off raiding in our company and we will definitely invest in this area in the future. >>Our data application of the year is observed. Observers recognizes the most innovative, data driven application built on Snowflake and representing observed today is their CEO, Jeremy Burton. >>Let me just echo the thanks from the other folks on the coal. I mean snowflakes, separation of storage. Compute. I can't overstate what a really big deal it is. Um, it means that we can ingest in store data. Really? For the price of Amazon s three on board, we're in a category where vendors of historically charged for volume of data ingested. So you can imagine this really represents huge savings. Um, in addition, and maybe on a more technical note, snowflakes, elastic architectures really enables us to direct queries appropriately, based on the complexity of the query. So small queries or simple queries weaken director extra small warehouses and complex queries. We can direct, you know, for Excel. Or I think even a six x l is either there are on its way. The key thing there is that users they're not sitting around waiting for results to appear regardless of the query complexity. So I mean, really? The separation storage compute on the elastic architectures is a really big deal for us. >>Turning to the data Pioneer of the Year Award, I'm excited to be here with Faisal KP, senior manager of Enterprise Data Services from Pizza Hut. >>First of all, thank you, Snowflake, for giving this wonderful person. I think it means a lot for us in terms of validating what we're doing. I think we were one of the earlier adopters of Snowflake. We saw the vision of snowflake, you know, stories. Russell's computer separation on all the goodies, right? Right from back in 2017, I believe what snowflake enabled us is to actually get the scale with very little manpower, which is needed to man the entire system. So on the Super Bowl day, we have, you know, the entire crew literally a boardroom where the right from the CME, most of the CEOs to all the folks will be sitting and watching what is happening in the system. And we have to do a lot of real time analytics during that time. So with snowflake, you know, way used the elasticity of the platform we use, you know, platform you know their solutions, like snow pipe to basically automate the data ingestion coming through various channels, from the commas, from the stores, everything simultaneously. So as soon as the program is done, you know, we can scale scale down to our normal volume, which means we can, you know, way can save a lot. Of course. So definitely it snowflake has been game changer for us in terms of how we provide real time analytics. Our systems are used by thousands off restaurants throughout the country and, you know, by hundreds of franchisees. So the scale is something we have achieved with a lot of ability and success. >>In the category of data science Manager of the Year Award, we have a mission Min, director of cybersecurity and data science at Comcast. >>So thank you for having me and thank you for this wonderful award. So one of the biggest challenges you see in this other security spaces the tremendous amount of data that we have to compute every day to find the gold haystack. So one of the big challenges we overcame with by uniting snowflake was how do we go from like my other counterparts on the panel have said Theo operational overhead of maintaining a large data store and moved to more of results driven and data focused environment. And, you know, part of that journey was really the tremendous leadership. Comcast saying, You know, we want Thio through our day to day lives by relying less on operational work and Maura on answering questions. And so you know, over the last year we've really put Snowflake at the center of our ecosystem, knowing that it's elastic platform and its ability scale infinitely have given us the ability to dream big and use it to drop five cybersecurity. And while it's traditionally used for cybersecurity, we're starting to see the benefits right away and the beauty of the snowflake. Ecos, Miss. We're now able to enable folks that not traditionally have big data skills, but they have standards, sequel skills, and they could still work in the snowflake platform. So, you know, the transition to cloud has been very powerful for us as an organization. But I think the end story, the real takeaways, by moving our secretary operation to the cloud, we're now been able to enable more people and get the results they were looking for. You know, as other people have said fast, people hate to wait. So the scale of snowflake really shines. >>Yeah. Now, let's hear from our data Executive of the year. The Cost. Jane. Chief Digital Officer Packer. >>Thank you very much, Snowflake, for this really incredible recognition and honor of the work we're doing it back. Are we began. The first step in this process was for us to develop an enterprise Great data platform in the cloud capable off managing every aspect of data at scale. This this platform includes snowflake as our analytics data warehouse amongst many other technologies that we used for ingestion of data, data processing, uh, data governance, transactional, uh, needs and others. So this platform, once developed, has really helped us leverage data across the broad pack. Our systems and applications globally very efficiently and is enabling pack are, as a result to enhance every aspect. Selfish business with data. >>Ah, big congratulations again to all of the winners of the 2020 Data Drivers Awards. Thanks so much for joining us for a great conversation. And we hope that you enjoy the rest of the data cloud summit

>>don't talk mhm, >>Okay, thing is my presentation on coherent nonlinear dynamics and combinatorial optimization. This is going to be a talk to introduce an approach we're taking to the analysis of the performance of coherent using machines. So let me start with a brief introduction to easing optimization. The easing model represents a set of interacting magnetic moments or spins the total energy given by the expression shown at the bottom left of this slide. Here, the signal variables are meditate binary values. The Matrix element J. I. J. Represents the interaction, strength and signed between any pair of spins. I. J and A Chive represents a possible local magnetic field acting on each thing. The easing ground state problem is to find an assignment of binary spin values that achieves the lowest possible value of total energy. And an instance of the easing problem is specified by giving numerical values for the Matrix J in Vector H. Although the easy model originates in physics, we understand the ground state problem to correspond to what would be called quadratic binary optimization in the field of operations research and in fact, in terms of computational complexity theory, it could be established that the easing ground state problem is np complete. Qualitatively speaking, this makes the easing problem a representative sort of hard optimization problem, for which it is expected that the runtime required by any computational algorithm to find exact solutions should, as anatomically scale exponentially with the number of spends and for worst case instances at each end. Of course, there's no reason to believe that the problem instances that actually arrives in practical optimization scenarios are going to be worst case instances. And it's also not generally the case in practical optimization scenarios that we demand absolute optimum solutions. Usually we're more interested in just getting the best solution we can within an affordable cost, where costs may be measured in terms of time, service fees and or energy required for a computation. This focuses great interest on so called heuristic algorithms for the easing problem in other NP complete problems which generally get very good but not guaranteed optimum solutions and run much faster than algorithms that are designed to find absolute Optima. To get some feeling for present day numbers, we can consider the famous traveling salesman problem for which extensive compilations of benchmarking data may be found online. A recent study found that the best known TSP solver required median run times across the Library of Problem instances That scaled is a very steep route exponential for end up to approximately 4500. This gives some indication of the change in runtime scaling for generic as opposed the worst case problem instances. Some of the instances considered in this study were taken from a public library of T SPS derived from real world Veil aside design data. This feels I TSP Library includes instances within ranging from 131 to 744,710 instances from this library with end between 6880 13,584 were first solved just a few years ago in 2017 requiring days of run time and a 48 core to King hurts cluster, while instances with and greater than or equal to 14,233 remain unsolved exactly by any means. Approximate solutions, however, have been found by heuristic methods for all instances in the VLS i TSP library with, for example, a solution within 0.14% of a no lower bound, having been discovered, for instance, with an equal 19,289 requiring approximately two days of run time on a single core of 2.4 gigahertz. Now, if we simple mindedly extrapolate the root exponential scaling from the study up to an equal 4500, we might expect that an exact solver would require something more like a year of run time on the 48 core cluster used for the N equals 13,580 for instance, which shows how much a very small concession on the quality of the solution makes it possible to tackle much larger instances with much lower cost. At the extreme end, the largest TSP ever solved exactly has an equal 85,900. This is an instance derived from 19 eighties VLSI design, and it's required 136 CPU. Years of computation normalized to a single cord, 2.4 gigahertz. But the 24 larger so called world TSP benchmark instance within equals 1,904,711 has been solved approximately within ophthalmology. Gap bounded below 0.474%. Coming back to the general. Practical concerns have applied optimization. We may note that a recent meta study analyzed the performance of no fewer than 37 heuristic algorithms for Max cut and quadratic pioneer optimization problems and found the performance sort and found that different heuristics work best for different problem instances selected from a large scale heterogeneous test bed with some evidence but cryptic structure in terms of what types of problem instances were best solved by any given heuristic. Indeed, their their reasons to believe that these results from Mexico and quadratic binary optimization reflected general principle of performance complementarity among heuristic optimization algorithms in the practice of solving heart optimization problems there. The cerise is a critical pre processing issue of trying to guess which of a number of available good heuristic algorithms should be chosen to tackle a given problem. Instance, assuming that any one of them would incur high costs to run on a large problem, instances incidence, making an astute choice of heuristic is a crucial part of maximizing overall performance. Unfortunately, we still have very little conceptual insight about what makes a specific problem instance, good or bad for any given heuristic optimization algorithm. This has certainly been pinpointed by researchers in the field is a circumstance that must be addressed. So adding this all up, we see that a critical frontier for cutting edge academic research involves both the development of novel heuristic algorithms that deliver better performance, with lower cost on classes of problem instances that are underserved by existing approaches, as well as fundamental research to provide deep conceptual insight into what makes a given problem in, since easy or hard for such algorithms. In fact, these days, as we talk about the end of Moore's law and speculate about a so called second quantum revolution, it's natural to talk not only about novel algorithms for conventional CPUs but also about highly customized special purpose hardware architectures on which we may run entirely unconventional algorithms for combinatorial optimization such as easing problem. So against that backdrop, I'd like to use my remaining time to introduce our work on analysis of coherent using machine architectures and associate ID optimization algorithms. These machines, in general, are a novel class of information processing architectures for solving combinatorial optimization problems by embedding them in the dynamics of analog, physical or cyber physical systems, in contrast to both MAWR traditional engineering approaches that build using machines using conventional electron ICS and more radical proposals that would require large scale quantum entanglement. The emerging paradigm of coherent easing machines leverages coherent nonlinear dynamics in photonic or Opto electronic platforms to enable near term construction of large scale prototypes that leverage post Simoes information dynamics, the general structure of of current CM systems has shown in the figure on the right. The role of the easing spins is played by a train of optical pulses circulating around a fiber optical storage ring. A beam splitter inserted in the ring is used to periodically sample the amplitude of every optical pulse, and the measurement results are continually read into a refugee A, which uses them to compute perturbations to be applied to each pulse by a synchronized optical injections. These perturbations, air engineered to implement the spin, spin coupling and local magnetic field terms of the easing Hamiltonian, corresponding to a linear part of the CME Dynamics, a synchronously pumped parametric amplifier denoted here as PPL and Wave Guide adds a crucial nonlinear component to the CIA and Dynamics as well. In the basic CM algorithm, the pump power starts very low and has gradually increased at low pump powers. The amplitude of the easing spin pulses behaviors continuous, complex variables. Who Israel parts which can be positive or negative, play the role of play the role of soft or perhaps mean field spins once the pump, our crosses the threshold for parametric self oscillation. In the optical fiber ring, however, the attitudes of the easing spin pulses become effectively Qantas ized into binary values while the pump power is being ramped up. The F P J subsystem continuously applies its measurement based feedback. Implementation of the using Hamiltonian terms, the interplay of the linear rised using dynamics implemented by the F P G A and the threshold conversation dynamics provided by the sink pumped Parametric amplifier result in the final state of the optical optical pulse amplitude at the end of the pump ramp that could be read as a binary strain, giving a proposed solution of the easing ground state problem. This method of solving easing problem seems quite different from a conventional algorithm that runs entirely on a digital computer as a crucial aspect of the computation is performed physically by the analog, continuous, coherent, nonlinear dynamics of the optical degrees of freedom. In our efforts to analyze CIA and performance, we have therefore turned to the tools of dynamical systems theory, namely, a study of modifications, the evolution of critical points and apologies of hetero clinic orbits and basins of attraction. We conjecture that such analysis can provide fundamental insight into what makes certain optimization instances hard or easy for coherent using machines and hope that our approach can lead to both improvements of the course, the AM algorithm and a pre processing rubric for rapidly assessing the CME suitability of new instances. Okay, to provide a bit of intuition about how this all works, it may help to consider the threshold dynamics of just one or two optical parametric oscillators in the CME architecture just described. We can think of each of the pulse time slots circulating around the fiber ring, as are presenting an independent Opio. We can think of a single Opio degree of freedom as a single, resonant optical node that experiences linear dissipation, do toe out coupling loss and gain in a pump. Nonlinear crystal has shown in the diagram on the upper left of this slide as the pump power is increased from zero. As in the CME algorithm, the non linear game is initially to low toe overcome linear dissipation, and the Opio field remains in a near vacuum state at a critical threshold. Value gain. Equal participation in the Popeo undergoes a sort of lazing transition, and the study states of the OPIO above this threshold are essentially coherent states. There are actually two possible values of the Opio career in amplitude and any given above threshold pump power which are equal in magnitude but opposite in phase when the OPI across the special diet basically chooses one of the two possible phases randomly, resulting in the generation of a single bit of information. If we consider to uncoupled, Opio has shown in the upper right diagram pumped it exactly the same power at all times. Then, as the pump power has increased through threshold, each Opio will independently choose the phase and thus to random bits are generated for any number of uncoupled. Oppose the threshold power per opio is unchanged from the single Opio case. Now, however, consider a scenario in which the two appeals air, coupled to each other by a mutual injection of their out coupled fields has shown in the diagram on the lower right. One can imagine that depending on the sign of the coupling parameter Alfa, when one Opio is lazing, it will inject a perturbation into the other that may interfere either constructively or destructively, with the feel that it is trying to generate by its own lazing process. As a result, when came easily showed that for Alfa positive, there's an effective ferro magnetic coupling between the two Opio fields and their collective oscillation threshold is lowered from that of the independent Opio case. But on Lee for the two collective oscillation modes in which the two Opio phases are the same for Alfa Negative, the collective oscillation threshold is lowered on Lee for the configurations in which the Opio phases air opposite. So then, looking at how Alfa is related to the J. I. J matrix of the easing spin coupling Hamiltonian, it follows that we could use this simplistic to a p o. C. I am to solve the ground state problem of a fair magnetic or anti ferro magnetic ankles to easing model simply by increasing the pump power from zero and observing what phase relation occurs as the two appeals first start delays. Clearly, we can imagine generalizing this story toe larger, and however the story doesn't stay is clean and simple for all larger problem instances. And to find a more complicated example, we only need to go to n equals four for some choices of J J for n equals, for the story remains simple. Like the n equals two case. The figure on the upper left of this slide shows the energy of various critical points for a non frustrated and equals, for instance, in which the first bifurcated critical point that is the one that I forget to the lowest pump value a. Uh, this first bifurcated critical point flows as symptomatically into the lowest energy easing solution and the figure on the upper right. However, the first bifurcated critical point flows to a very good but sub optimal minimum at large pump power. The global minimum is actually given by a distinct critical critical point that first appears at a higher pump power and is not automatically connected to the origin. The basic C am algorithm is thus not able to find this global minimum. Such non ideal behaviors needs to become more confident. Larger end for the n equals 20 instance, showing the lower plots where the lower right plot is just a zoom into a region of the lower left lot. It can be seen that the global minimum corresponds to a critical point that first appears out of pump parameter, a around 0.16 at some distance from the idiomatic trajectory of the origin. That's curious to note that in both of these small and examples, however, the critical point corresponding to the global minimum appears relatively close to the idiomatic projector of the origin as compared to the most of the other local minima that appear. We're currently working to characterize the face portrait topology between the global minimum in the antibiotic trajectory of the origin, taking clues as to how the basic C am algorithm could be generalized to search for non idiomatic trajectories that jump to the global minimum during the pump ramp. Of course, n equals 20 is still too small to be of interest for practical optimization applications. But the advantage of beginning with the study of small instances is that we're able reliably to determine their global minima and to see how they relate to the 80 about trajectory of the origin in the basic C am algorithm. In the smaller and limit, we can also analyze fully quantum mechanical models of Syrian dynamics. But that's a topic for future talks. Um, existing large scale prototypes are pushing into the range of in equals 10 to the 4 10 to 5 to six. So our ultimate objective in theoretical analysis really has to be to try to say something about CIA and dynamics and regime of much larger in our initial approach to characterizing CIA and behavior in the large in regime relies on the use of random matrix theory, and this connects to prior research on spin classes, SK models and the tap equations etcetera. At present, we're focusing on statistical characterization of the CIA ingredient descent landscape, including the evolution of critical points in their Eigen value spectra. As the pump power is gradually increased. We're investigating, for example, whether there could be some way to exploit differences in the relative stability of the global minimum versus other local minima. We're also working to understand the deleterious or potentially beneficial effects of non ideologies, such as a symmetry in the implemented these and couplings. Looking one step ahead, we plan to move next in the direction of considering more realistic classes of problem instances such as quadratic, binary optimization with constraints. Eso In closing, I should acknowledge people who did the hard work on these things that I've shown eso. My group, including graduate students Ed winning, Daniel Wennberg, Tatsuya Nagamoto and Atsushi Yamamura, have been working in close collaboration with Syria Ganguly, Marty Fair and Amir Safarini Nini, all of us within the Department of Applied Physics at Stanford University. On also in collaboration with the Oshima Moto over at NTT 55 research labs, Onda should acknowledge funding support from the NSF by the Coherent Easing Machines Expedition in computing, also from NTT five research labs, Army Research Office and Exxon Mobil. Uh, that's it. Thanks very much. >>Mhm e >>t research and the Oshie for putting together this program and also the opportunity to speak here. My name is Al Gore ism or Andy and I'm from Caltech, and today I'm going to tell you about the work that we have been doing on networks off optical parametric oscillators and how we have been using them for icing machines and how we're pushing them toward Cornum photonics to acknowledge my team at Caltech, which is now eight graduate students and five researcher and postdocs as well as collaborators from all over the world, including entity research and also the funding from different places, including entity. So this talk is primarily about networks of resonate er's, and these networks are everywhere from nature. For instance, the brain, which is a network of oscillators all the way to optics and photonics and some of the biggest examples or metal materials, which is an array of small resonate er's. And we're recently the field of technological photonics, which is trying thio implement a lot of the technological behaviors of models in the condensed matter, physics in photonics and if you want to extend it even further, some of the implementations off quantum computing are technically networks of quantum oscillators. So we started thinking about these things in the context of icing machines, which is based on the icing problem, which is based on the icing model, which is the simple summation over the spins and spins can be their upward down and the couplings is given by the JJ. And the icing problem is, if you know J I J. What is the spin configuration that gives you the ground state? And this problem is shown to be an MP high problem. So it's computational e important because it's a representative of the MP problems on NPR. Problems are important because first, their heart and standard computers if you use a brute force algorithm and they're everywhere on the application side. That's why there is this demand for making a machine that can target these problems, and hopefully it can provide some meaningful computational benefit compared to the standard digital computers. So I've been building these icing machines based on this building block, which is a degenerate optical parametric. Oscillator on what it is is resonator with non linearity in it, and we pump these resonate er's and we generate the signal at half the frequency of the pump. One vote on a pump splits into two identical photons of signal, and they have some very interesting phase of frequency locking behaviors. And if you look at the phase locking behavior, you realize that you can actually have two possible phase states as the escalation result of these Opio which are off by pie, and that's one of the important characteristics of them. So I want to emphasize a little more on that and I have this mechanical analogy which are basically two simple pendulum. But there are parametric oscillators because I'm going to modulate the parameter of them in this video, which is the length of the string on by that modulation, which is that will make a pump. I'm gonna make a muscular. That'll make a signal which is half the frequency of the pump. And I have two of them to show you that they can acquire these face states so they're still facing frequency lock to the pump. But it can also lead in either the zero pie face states on. The idea is to use this binary phase to represent the binary icing spin. So each opio is going to represent spin, which can be either is your pie or up or down. And to implement the network of these resonate er's, we use the time off blood scheme, and the idea is that we put impulses in the cavity. These pulses air separated by the repetition period that you put in or t r. And you can think about these pulses in one resonator, xaz and temporarily separated synthetic resonate Er's if you want a couple of these resonator is to each other, and now you can introduce these delays, each of which is a multiple of TR. If you look at the shortest delay it couples resonator wanted to 2 to 3 and so on. If you look at the second delay, which is two times a rotation period, the couple's 123 and so on. And if you have and minus one delay lines, then you can have any potential couplings among these synthetic resonate er's. And if I can introduce these modulators in those delay lines so that I can strength, I can control the strength and the phase of these couplings at the right time. Then I can have a program will all toe all connected network in this time off like scheme, and the whole physical size of the system scales linearly with the number of pulses. So the idea of opium based icing machine is didn't having these o pos, each of them can be either zero pie and I can arbitrarily connect them to each other. And then I start with programming this machine to a given icing problem by just setting the couplings and setting the controllers in each of those delight lines. So now I have a network which represents an icing problem. Then the icing problem maps to finding the face state that satisfy maximum number of coupling constraints. And the way it happens is that the icing Hamiltonian maps to the linear loss of the network. And if I start adding gain by just putting pump into the network, then the OPI ohs are expected to oscillate in the lowest, lowest lost state. And, uh and we have been doing these in the past, uh, six or seven years and I'm just going to quickly show you the transition, especially what happened in the first implementation, which was using a free space optical system and then the guided wave implementation in 2016 and the measurement feedback idea which led to increasing the size and doing actual computation with these machines. So I just want to make this distinction here that, um, the first implementation was an all optical interaction. We also had an unequal 16 implementation. And then we transition to this measurement feedback idea, which I'll tell you quickly what it iss on. There's still a lot of ongoing work, especially on the entity side, to make larger machines using the measurement feedback. But I'm gonna mostly focused on the all optical networks and how we're using all optical networks to go beyond simulation of icing Hamiltonian both in the linear and non linear side and also how we're working on miniaturization of these Opio networks. So the first experiment, which was the four opium machine, it was a free space implementation and this is the actual picture off the machine and we implemented a small and it calls for Mexico problem on the machine. So one problem for one experiment and we ran the machine 1000 times, we looked at the state and we always saw it oscillate in one of these, um, ground states of the icing laboratoria. So then the measurement feedback idea was to replace those couplings and the controller with the simulator. So we basically simulated all those coherent interactions on on FB g. A. And we replicated the coherent pulse with respect to all those measurements. And then we injected it back into the cavity and on the near to you still remain. So it still is a non. They're dynamical system, but the linear side is all simulated. So there are lots of questions about if this system is preserving important information or not, or if it's gonna behave better. Computational wars. And that's still ah, lot of ongoing studies. But nevertheless, the reason that this implementation was very interesting is that you don't need the end minus one delight lines so you can just use one. Then you can implement a large machine, and then you can run several thousands of problems in the machine, and then you can compare the performance from the computational perspective Looks so I'm gonna split this idea of opium based icing machine into two parts. One is the linear part, which is if you take out the non linearity out of the resonator and just think about the connections. You can think about this as a simple matrix multiplication scheme. And that's basically what gives you the icing Hambletonian modeling. So the optical laws of this network corresponds to the icing Hamiltonian. And if I just want to show you the example of the n equals for experiment on all those face states and the history Graham that we saw, you can actually calculate the laws of each of those states because all those interferences in the beam splitters and the delay lines are going to give you a different losses. And then you will see that the ground states corresponds to the lowest laws of the actual optical network. If you add the non linearity, the simple way of thinking about what the non linearity does is that it provides to gain, and then you start bringing up the gain so that it hits the loss. Then you go through the game saturation or the threshold which is going to give you this phase bifurcation. So you go either to zero the pie face state. And the expectation is that Theis, the network oscillates in the lowest possible state, the lowest possible loss state. There are some challenges associated with this intensity Durban face transition, which I'm going to briefly talk about. I'm also going to tell you about other types of non aerodynamics that we're looking at on the non air side of these networks. So if you just think about the linear network, we're actually interested in looking at some technological behaviors in these networks. And the difference between looking at the technological behaviors and the icing uh, machine is that now, First of all, we're looking at the type of Hamilton Ian's that are a little different than the icing Hamilton. And one of the biggest difference is is that most of these technological Hamilton Ian's that require breaking the time reversal symmetry, meaning that you go from one spin to in the one side to another side and you get one phase. And if you go back where you get a different phase, and the other thing is that we're not just interested in finding the ground state, we're actually now interesting and looking at all sorts of states and looking at the dynamics and the behaviors of all these states in the network. So we started with the simplest implementation, of course, which is a one d chain of thes resonate, er's, which corresponds to a so called ssh model. In the technological work, we get the similar energy to los mapping and now we can actually look at the band structure on. This is an actual measurement that we get with this associate model and you see how it reasonably how How? Well, it actually follows the prediction and the theory. One of the interesting things about the time multiplexing implementation is that now you have the flexibility of changing the network as you are running the machine. And that's something unique about this time multiplex implementation so that we can actually look at the dynamics. And one example that we have looked at is we can actually go through the transition off going from top A logical to the to the standard nontrivial. I'm sorry to the trivial behavior of the network. You can then look at the edge states and you can also see the trivial and states and the technological at states actually showing up in this network. We have just recently implement on a two D, uh, network with Harper Hofstadter model and when you don't have the results here. But we're one of the other important characteristic of time multiplexing is that you can go to higher and higher dimensions and keeping that flexibility and dynamics, and we can also think about adding non linearity both in a classical and quantum regimes, which is going to give us a lot of exotic, no classical and quantum, non innate behaviors in these networks. Yeah, So I told you about the linear side. Mostly let me just switch gears and talk about the nonlinear side of the network. And the biggest thing that I talked about so far in the icing machine is this face transition that threshold. So the low threshold we have squeezed state in these. Oh, pios, if you increase the pump, we go through this intensity driven phase transition and then we got the face stays above threshold. And this is basically the mechanism off the computation in these O pos, which is through this phase transition below to above threshold. So one of the characteristics of this phase transition is that below threshold, you expect to see quantum states above threshold. You expect to see more classical states or coherent states, and that's basically corresponding to the intensity off the driving pump. So it's really hard to imagine that it can go above threshold. Or you can have this friends transition happen in the all in the quantum regime. And there are also some challenges associated with the intensity homogeneity off the network, which, for example, is if one opioid starts oscillating and then its intensity goes really high. Then it's going to ruin this collective decision making off the network because of the intensity driven face transition nature. So So the question is, can we look at other phase transitions? Can we utilize them for both computing? And also can we bring them to the quantum regime on? I'm going to specifically talk about the face transition in the spectral domain, which is the transition from the so called degenerate regime, which is what I mostly talked about to the non degenerate regime, which happens by just tuning the phase of the cavity. And what is interesting is that this phase transition corresponds to a distinct phase noise behavior. So in the degenerate regime, which we call it the order state, you're gonna have the phase being locked to the phase of the pump. As I talked about non degenerate regime. However, the phase is the phase is mostly dominated by the quantum diffusion. Off the off the phase, which is limited by the so called shallow towns limit, and you can see that transition from the general to non degenerate, which also has distinct symmetry differences. And this transition corresponds to a symmetry breaking in the non degenerate case. The signal can acquire any of those phases on the circle, so it has a you one symmetry. Okay, and if you go to the degenerate case, then that symmetry is broken and you only have zero pie face days I will look at. So now the question is can utilize this phase transition, which is a face driven phase transition, and can we use it for similar computational scheme? So that's one of the questions that were also thinking about. And it's not just this face transition is not just important for computing. It's also interesting from the sensing potentials and this face transition, you can easily bring it below threshold and just operated in the quantum regime. Either Gaussian or non Gaussian. If you make a network of Opio is now, we can see all sorts off more complicated and more interesting phase transitions in the spectral domain. One of them is the first order phase transition, which you get by just coupling to Opio, and that's a very abrupt face transition and compared to the to the single Opio phase transition. And if you do the couplings right, you can actually get a lot of non her mission dynamics and exceptional points, which are actually very interesting to explore both in the classical and quantum regime. And I should also mention that you can think about the cup links to be also nonlinear couplings. And that's another behavior that you can see, especially in the nonlinear in the non degenerate regime. So with that, I basically told you about these Opio networks, how we can think about the linear scheme and the linear behaviors and how we can think about the rich, nonlinear dynamics and non linear behaviors both in the classical and quantum regime. I want to switch gear and tell you a little bit about the miniaturization of these Opio networks. And of course, the motivation is if you look at the electron ICS and what we had 60 or 70 years ago with vacuum tube and how we transition from relatively small scale computers in the order of thousands of nonlinear elements to billions of non elements where we are now with the optics is probably very similar to 70 years ago, which is a table talk implementation. And the question is, how can we utilize nano photonics? I'm gonna just briefly show you the two directions on that which we're working on. One is based on lithium Diabate, and the other is based on even a smaller resonate er's could you? So the work on Nana Photonic lithium naive. It was started in collaboration with Harvard Marko Loncar, and also might affair at Stanford. And, uh, we could show that you can do the periodic polling in the phenomenon of it and get all sorts of very highly nonlinear processes happening in this net. Photonic periodically polls if, um Diabate. And now we're working on building. Opio was based on that kind of photonic the film Diabate. And these air some some examples of the devices that we have been building in the past few months, which I'm not gonna tell you more about. But the O. P. O. S. And the Opio Networks are in the works. And that's not the only way of making large networks. Um, but also I want to point out that The reason that these Nana photonic goblins are actually exciting is not just because you can make a large networks and it can make him compact in a in a small footprint. They also provide some opportunities in terms of the operation regime. On one of them is about making cat states and Opio, which is, can we have the quantum superposition of the zero pie states that I talked about and the Net a photonic within? I've It provides some opportunities to actually get closer to that regime because of the spatial temporal confinement that you can get in these wave guides. So we're doing some theory on that. We're confident that the type of non linearity two losses that it can get with these platforms are actually much higher than what you can get with other platform their existing platforms and to go even smaller. We have been asking the question off. What is the smallest possible Opio that you can make? Then you can think about really wavelength scale type, resonate er's and adding the chi to non linearity and see how and when you can get the Opio to operate. And recently, in collaboration with us see, we have been actually USC and Creole. We have demonstrated that you can use nano lasers and get some spin Hamilton and implementations on those networks. So if you can build the a P. O s, we know that there is a path for implementing Opio Networks on on such a nano scale. So we have looked at these calculations and we try to estimate the threshold of a pos. Let's say for me resonator and it turns out that it can actually be even lower than the type of bulk Pip Llano Pos that we have been building in the past 50 years or so. So we're working on the experiments and we're hoping that we can actually make even larger and larger scale Opio networks. So let me summarize the talk I told you about the opium networks and our work that has been going on on icing machines and the measurement feedback. And I told you about the ongoing work on the all optical implementations both on the linear side and also on the nonlinear behaviors. And I also told you a little bit about the efforts on miniaturization and going to the to the Nano scale. So with that, I would like Thio >>three from the University of Tokyo. Before I thought that would like to thank you showing all the stuff of entity for the invitation and the organization of this online meeting and also would like to say that it has been very exciting to see the growth of this new film lab. And I'm happy to share with you today of some of the recent works that have been done either by me or by character of Hong Kong. Honest Group indicates the title of my talk is a neuro more fic in silica simulator for the communities in machine. And here is the outline I would like to make the case that the simulation in digital Tektronix of the CME can be useful for the better understanding or improving its function principles by new job introducing some ideas from neural networks. This is what I will discuss in the first part and then it will show some proof of concept of the game and performance that can be obtained using dissimulation in the second part and the protection of the performance that can be achieved using a very large chaos simulator in the third part and finally talk about future plans. So first, let me start by comparing recently proposed izing machines using this table there is elected from recent natural tronics paper from the village Park hard people, and this comparison shows that there's always a trade off between energy efficiency, speed and scalability that depends on the physical implementation. So in red, here are the limitation of each of the servers hardware on, interestingly, the F p G, a based systems such as a producer, digital, another uh Toshiba beautification machine or a recently proposed restricted Bozeman machine, FPD A by a group in Berkeley. They offer a good compromise between speed and scalability. And this is why, despite the unique advantage that some of these older hardware have trust as the currency proposition in Fox, CBS or the energy efficiency off memory Sisters uh P. J. O are still an attractive platform for building large organizing machines in the near future. The reason for the good performance of Refugee A is not so much that they operate at the high frequency. No, there are particular in use, efficient, but rather that the physical wiring off its elements can be reconfigured in a way that limits the funding human bottleneck, larger, funny and phenols and the long propagation video information within the system. In this respect, the LPGA is They are interesting from the perspective off the physics off complex systems, but then the physics of the actions on the photos. So to put the performance of these various hardware and perspective, we can look at the competition of bringing the brain the brain complete, using billions of neurons using only 20 watts of power and operates. It's a very theoretically slow, if we can see and so this impressive characteristic, they motivate us to try to investigate. What kind of new inspired principles be useful for designing better izing machines? The idea of this research project in the future collaboration it's to temporary alleviates the limitations that are intrinsic to the realization of an optical cortex in machine shown in the top panel here. By designing a large care simulator in silicone in the bottom here that can be used for digesting the better organization principles of the CIA and this talk, I will talk about three neuro inspired principles that are the symmetry of connections, neural dynamics orphan chaotic because of symmetry, is interconnectivity the infrastructure? No. Next talks are not composed of the reputation of always the same types of non environments of the neurons, but there is a local structure that is repeated. So here's the schematic of the micro column in the cortex. And lastly, the Iraqi co organization of connectivity connectivity is organizing a tree structure in the brain. So here you see a representation of the Iraqi and organization of the monkey cerebral cortex. So how can these principles we used to improve the performance of the icing machines? And it's in sequence stimulation. So, first about the two of principles of the estimate Trian Rico structure. We know that the classical approximation of the car testing machine, which is the ground toe, the rate based on your networks. So in the case of the icing machines, uh, the okay, Scott approximation can be obtained using the trump active in your position, for example, so the times of both of the system they are, they can be described by the following ordinary differential equations on in which, in case of see, I am the X, I represent the in phase component of one GOP Oh, Theo f represents the monitor optical parts, the district optical Parametric amplification and some of the good I JoJo extra represent the coupling, which is done in the case of the measure of feedback coupling cm using oh, more than detection and refugee A and then injection off the cooking time and eso this dynamics in both cases of CNN in your networks, they can be written as the grand set of a potential function V, and this written here, and this potential functionally includes the rising Maccagnan. So this is why it's natural to use this type of, uh, dynamics to solve the icing problem in which the Omega I J or the eyes in coping and the H is the extension of the icing and attorney in India and expect so. Not that this potential function can only be defined if the Omega I j. R. A. Symmetric. So the well known problem of this approach is that this potential function V that we obtain is very non convicts at low temperature, and also one strategy is to gradually deformed this landscape, using so many in process. But there is no theorem. Unfortunately, that granted conventions to the global minimum of There's even Tony and using this approach. And so this is why we propose, uh, to introduce a macro structures of the system where one analog spin or one D O. P. O is replaced by a pair off one another spin and one error, according viable. And the addition of this chemical structure introduces a symmetry in the system, which in terms induces chaotic dynamics, a chaotic search rather than a learning process for searching for the ground state of the icing. Every 20 within this massacre structure the role of the er variable eyes to control the amplitude off the analog spins toe force. The amplitude of the expense toe become equal to certain target amplitude a uh and, uh, and this is done by modulating the strength off the icing complaints or see the the error variable E I multiply the icing complaint here in the dynamics off air d o p. O. On then the dynamics. The whole dynamics described by this coupled equations because the e I do not necessarily take away the same value for the different. I thesis introduces a symmetry in the system, which in turn creates security dynamics, which I'm sure here for solving certain current size off, um, escape problem, Uh, in which the X I are shown here and the i r from here and the value of the icing energy showing the bottom plots. You see this Celtics search that visit various local minima of the as Newtonian and eventually finds the global minimum? Um, it can be shown that this modulation off the target opportunity can be used to destabilize all the local minima off the icing evertonians so that we're gonna do not get stuck in any of them. On more over the other types of attractors I can eventually appear, such as limits I contractors, Okot contractors. They can also be destabilized using the motivation of the target and Batuta. And so we have proposed in the past two different moderation of the target amateur. The first one is a modulation that ensure the uh 100 reproduction rate of the system to become positive on this forbids the creation off any nontrivial tractors. And but in this work, I will talk about another moderation or arrested moderation which is given here. That works, uh, as well as this first uh, moderation, but is easy to be implemented on refugee. So this couple of the question that represent becoming the stimulation of the cortex in machine with some error correction they can be implemented especially efficiently on an F B. G. And here I show the time that it takes to simulate three system and also in red. You see, at the time that it takes to simulate the X I term the EI term, the dot product and the rising Hamiltonian for a system with 500 spins and Iraq Spain's equivalent to 500 g. O. P. S. So >>in >>f b d a. The nonlinear dynamics which, according to the digital optical Parametric amplification that the Opa off the CME can be computed in only 13 clock cycles at 300 yards. So which corresponds to about 0.1 microseconds. And this is Toby, uh, compared to what can be achieved in the measurements back O C. M. In which, if we want to get 500 timer chip Xia Pios with the one she got repetition rate through the obstacle nine narrative. Uh, then way would require 0.5 microseconds toe do this so the submission in F B J can be at least as fast as ah one g repression. Uh, replicate pulsed laser CIA Um, then the DOT product that appears in this differential equation can be completed in 43 clock cycles. That's to say, one microseconds at 15 years. So I pieced for pouring sizes that are larger than 500 speeds. The dot product becomes clearly the bottleneck, and this can be seen by looking at the the skating off the time the numbers of clock cycles a text to compute either the non in your optical parts or the dog products, respect to the problem size. And And if we had infinite amount of resources and PGA to simulate the dynamics, then the non illogical post can could be done in the old one. On the mattress Vector product could be done in the low carrot off, located off scales as a look at it off and and while the guide off end. Because computing the dot product involves assuming all the terms in the product, which is done by a nephew, GE by another tree, which heights scarce logarithmic any with the size of the system. But This is in the case if we had an infinite amount of resources on the LPGA food, but for dealing for larger problems off more than 100 spins. Usually we need to decompose the metrics into ah, smaller blocks with the block side that are not you here. And then the scaling becomes funny, non inner parts linear in the end, over you and for the products in the end of EU square eso typically for low NF pdf cheap PGA you the block size off this matrix is typically about 100. So clearly way want to make you as large as possible in order to maintain this scanning in a log event for the numbers of clock cycles needed to compute the product rather than this and square that occurs if we decompose the metrics into smaller blocks. But the difficulty in, uh, having this larger blocks eyes that having another tree very large Haider tree introduces a large finding and finance and long distance start a path within the refugee. So the solution to get higher performance for a simulator of the contest in machine eyes to get rid of this bottleneck for the dot product by increasing the size of this at the tree. And this can be done by organizing your critique the electrical components within the LPGA in order which is shown here in this, uh, right panel here in order to minimize the finding finance of the system and to minimize the long distance that a path in the in the fpt So I'm not going to the details of how this is implemented LPGA. But just to give you a idea off why the Iraqi Yahiko organization off the system becomes the extremely important toe get good performance for similar organizing machine. So instead of instead of getting into the details of the mpg implementation, I would like to give some few benchmark results off this simulator, uh, off the that that was used as a proof of concept for this idea which is can be found in this archive paper here and here. I should results for solving escape problems. Free connected person, randomly person minus one spring last problems and we sure, as we use as a metric the numbers of the mattress Victor products since it's the bottleneck of the computation, uh, to get the optimal solution of this escape problem with the Nina successful BT against the problem size here and and in red here, this propose FDJ implementation and in ah blue is the numbers of retrospective product that are necessary for the C. I am without error correction to solve this escape programs and in green here for noisy means in an evening which is, uh, behavior with similar to the Cartesian mission. Uh, and so clearly you see that the scaring off the numbers of matrix vector product necessary to solve this problem scales with a better exponents than this other approaches. So So So that's interesting feature of the system and next we can see what is the real time to solution to solve this SK instances eso in the last six years, the time institution in seconds to find a grand state of risk. Instances remain answers probability for different state of the art hardware. So in red is the F B g. A presentation proposing this paper and then the other curve represent Ah, brick a local search in in orange and silver lining in purple, for example. And so you see that the scaring off this purpose simulator is is rather good, and that for larger plant sizes we can get orders of magnitude faster than the state of the art approaches. Moreover, the relatively good scanning off the time to search in respect to problem size uh, they indicate that the FPD implementation would be faster than risk. Other recently proposed izing machine, such as the hope you know, natural complimented on memories distance that is very fast for small problem size in blue here, which is very fast for small problem size. But which scanning is not good on the same thing for the restricted Bosman machine. Implementing a PGA proposed by some group in Broken Recently Again, which is very fast for small parliament sizes but which canning is bad so that a dis worse than the proposed approach so that we can expect that for programs size is larger than 1000 spins. The proposed, of course, would be the faster one. Let me jump toe this other slide and another confirmation that the scheme scales well that you can find the maximum cut values off benchmark sets. The G sets better candidates that have been previously found by any other algorithms, so they are the best known could values to best of our knowledge. And, um or so which is shown in this paper table here in particular, the instances, uh, 14 and 15 of this G set can be We can find better converse than previously known, and we can find this can vary is 100 times faster than the state of the art algorithm and CP to do this which is a very common Kasich. It s not that getting this a good result on the G sets, they do not require ah, particular hard tuning of the parameters. So the tuning issuing here is very simple. It it just depends on the degree off connectivity within each graph. And so this good results on the set indicate that the proposed approach would be a good not only at solving escape problems in this problems, but all the types off graph sizing problems on Mexican province in communities. So given that the performance off the design depends on the height of this other tree, we can try to maximize the height of this other tree on a large F p g a onda and carefully routing the components within the P G A and and we can draw some projections of what type of performance we can achieve in the near future based on the, uh, implementation that we are currently working. So here you see projection for the time to solution way, then next property for solving this escape programs respect to the prime assize. And here, compared to different with such publicizing machines, particularly the digital. And, you know, 42 is shown in the green here, the green line without that's and, uh and we should two different, uh, hypothesis for this productions either that the time to solution scales as exponential off n or that the time of social skills as expression of square root off. So it seems, according to the data, that time solution scares more as an expression of square root of and also we can be sure on this and this production show that we probably can solve prime escape problem of science 2000 spins, uh, to find the rial ground state of this problem with 99 success ability in about 10 seconds, which is much faster than all the other proposed approaches. So one of the future plans for this current is in machine simulator. So the first thing is that we would like to make dissimulation closer to the rial, uh, GOP oh, optical system in particular for a first step to get closer to the system of a measurement back. See, I am. And to do this what is, uh, simulate Herbal on the p a is this quantum, uh, condoms Goshen model that is proposed described in this paper and proposed by people in the in the Entity group. And so the idea of this model is that instead of having the very simple or these and have shown previously, it includes paired all these that take into account on me the mean off the awesome leverage off the, uh, European face component, but also their violence s so that we can take into account more quantum effects off the g o p. O, such as the squeezing. And then we plan toe, make the simulator open access for the members to run their instances on the system. There will be a first version in September that will be just based on the simple common line access for the simulator and in which will have just a classic or approximation of the system. We don't know Sturm, binary weights and museum in term, but then will propose a second version that would extend the current arising machine to Iraq off F p g. A, in which we will add the more refined models truncated, ignoring the bottom Goshen model they just talked about on the support in which he valued waits for the rising problems and support the cement. So we will announce later when this is available and and far right is working >>hard comes from Universal down today in physics department, and I'd like to thank the organizers for their kind invitation to participate in this very interesting and promising workshop. Also like to say that I look forward to collaborations with with a file lab and Yoshi and collaborators on the topics of this world. So today I'll briefly talk about our attempt to understand the fundamental limits off another continues time computing, at least from the point off you off bullion satisfy ability, problem solving, using ordinary differential equations. But I think the issues that we raise, um, during this occasion actually apply to other other approaches on a log approaches as well and into other problems as well. I think everyone here knows what Dorien satisfy ability. Problems are, um, you have boolean variables. You have em clauses. Each of disjunction of collaterals literally is a variable, or it's, uh, negation. And the goal is to find an assignment to the variable, such that order clauses are true. This is a decision type problem from the MP class, which means you can checking polynomial time for satisfy ability off any assignment. And the three set is empty, complete with K three a larger, which means an efficient trees. That's over, uh, implies an efficient source for all the problems in the empty class, because all the problems in the empty class can be reduced in Polian on real time to reset. As a matter of fact, you can reduce the NP complete problems into each other. You can go from three set to set backing or two maximum dependent set, which is a set packing in graph theoretic notions or terms toe the icing graphs. A problem decision version. This is useful, and you're comparing different approaches, working on different kinds of problems when not all the closest can be satisfied. You're looking at the accusation version offset, uh called Max Set. And the goal here is to find assignment that satisfies the maximum number of clauses. And this is from the NPR class. In terms of applications. If we had inefficient sets over or np complete problems over, it was literally, positively influenced. Thousands off problems and applications in industry and and science. I'm not going to read this, but this this, of course, gives a strong motivation toe work on this kind of problems. Now our approach to set solving involves embedding the problem in a continuous space, and you use all the east to do that. So instead of working zeros and ones, we work with minus one across once, and we allow the corresponding variables toe change continuously between the two bounds. We formulate the problem with the help of a close metrics. If if a if a close, uh, does not contain a variable or its negation. The corresponding matrix element is zero. If it contains the variable in positive, for which one contains the variable in a gated for Mitt's negative one, and then we use this to formulate this products caused quote, close violation functions one for every clause, Uh, which really, continuously between zero and one. And they're zero if and only if the clause itself is true. Uh, then we form the define in order to define a dynamic such dynamics in this and dimensional hyper cube where the search happens and if they exist, solutions. They're sitting in some of the corners of this hyper cube. So we define this, uh, energy potential or landscape function shown here in a way that this is zero if and only if all the clauses all the kmc zero or the clauses off satisfied keeping these auxiliary variables a EMS always positive. And therefore, what you do here is a dynamics that is a essentially ingredient descend on this potential energy landscape. If you were to keep all the M's constant that it would get stuck in some local minimum. However, what we do here is we couple it with the dynamics we cooperated the clothes violation functions as shown here. And if he didn't have this am here just just the chaos. For example, you have essentially what case you have positive feedback. You have increasing variable. Uh, but in that case, you still get stuck would still behave will still find. So she is better than the constant version but still would get stuck only when you put here this a m which makes the dynamics in in this variable exponential like uh, only then it keeps searching until he finds a solution on deer is a reason for that. I'm not going toe talk about here, but essentially boils down toe performing a Grady and descend on a globally time barren landscape. And this is what works. Now I'm gonna talk about good or bad and maybe the ugly. Uh, this is, uh, this is What's good is that it's a hyperbolic dynamical system, which means that if you take any domain in the search space that doesn't have a solution in it or any socially than the number of trajectories in it decays exponentially quickly. And the decay rate is a characteristic in variant characteristic off the dynamics itself. Dynamical systems called the escape right the inverse off that is the time scale in which you find solutions by this by this dynamical system, and you can see here some song trajectories that are Kelty because it's it's no linear, but it's transient, chaotic. Give their sources, of course, because eventually knowledge to the solution. Now, in terms of performance here, what you show for a bunch off, um, constraint densities defined by M overran the ratio between closes toe variables for random, said Problems is random. Chris had problems, and they as its function off n And we look at money toward the wartime, the wall clock time and it behaves quite value behaves Azat party nominally until you actually he to reach the set on set transition where the hardest problems are found. But what's more interesting is if you monitor the continuous time t the performance in terms off the A narrow, continuous Time t because that seems to be a polynomial. And the way we show that is, we consider, uh, random case that random three set for a fixed constraint density Onda. We hear what you show here. Is that the right of the trash hold that it's really hard and, uh, the money through the fraction of problems that we have not been able to solve it. We select thousands of problems at that constraint ratio and resolve them without algorithm, and we monitor the fractional problems that have not yet been solved by continuous 90. And this, as you see these decays exponentially different. Educate rates for different system sizes, and in this spot shows that is dedicated behaves polynomial, or actually as a power law. So if you combine these two, you find that the time needed to solve all problems except maybe appear traction off them scales foreign or merely with the problem size. So you have paranormal, continuous time complexity. And this is also true for other types of very hard constraints and sexual problems such as exact cover, because you can always transform them into three set as we discussed before, Ramsey coloring and and on these problems, even algorithms like survey propagation will will fail. But this doesn't mean that P equals NP because what you have first of all, if you were toe implement these equations in a device whose behavior is described by these, uh, the keys. Then, of course, T the continue style variable becomes a physical work off. Time on that will be polynomial is scaling, but you have another other variables. Oxidative variables, which structured in an exponential manner. So if they represent currents or voltages in your realization and it would be an exponential cost Al Qaeda. But this is some kind of trade between time and energy, while I know how toe generate energy or I don't know how to generate time. But I know how to generate energy so it could use for it. But there's other issues as well, especially if you're trying toe do this son and digital machine but also happens. Problems happen appear. Other problems appear on in physical devices as well as we discuss later. So if you implement this in GPU, you can. Then you can get in order off to magnitude. Speed up. And you can also modify this to solve Max sad problems. Uh, quite efficiently. You are competitive with the best heuristic solvers. This is a weather problems. In 2016 Max set competition eso so this this is this is definitely this seems like a good approach, but there's off course interesting limitations, I would say interesting, because it kind of makes you think about what it means and how you can exploit this thes observations in understanding better on a low continues time complexity. If you monitored the discrete number the number of discrete steps. Don't buy the room, Dakota integrator. When you solve this on a digital machine, you're using some kind of integrator. Um and you're using the same approach. But now you measure the number off problems you haven't sold by given number of this kid, uh, steps taken by the integrator. You find out you have exponential, discrete time, complexity and, of course, thistles. A problem. And if you look closely, what happens even though the analog mathematical trajectory, that's the record here. If you monitor what happens in discrete time, uh, the integrator frustrates very little. So this is like, you know, third or for the disposition, but fluctuates like crazy. So it really is like the intervention frees us out. And this is because of the phenomenon of stiffness that are I'll talk a little bit a more about little bit layer eso. >>You know, it might look >>like an integration issue on digital machines that you could improve and could definitely improve. But actually issues bigger than that. It's It's deeper than that, because on a digital machine there is no time energy conversion. So the outside variables are efficiently representing a digital machine. So there's no exponential fluctuating current of wattage in your computer when you do this. Eso If it is not equal NP then the exponential time, complexity or exponential costs complexity has to hit you somewhere. And this is how um, but, you know, one would be tempted to think maybe this wouldn't be an issue in a analog device, and to some extent is true on our devices can be ordered to maintain faster, but they also suffer from their own problems because he not gonna be affect. That classes soldiers as well. So, indeed, if you look at other systems like Mirandizing machine measurement feedback, probably talk on the grass or selected networks. They're all hinge on some kind off our ability to control your variables in arbitrary, high precision and a certain networks you want toe read out across frequencies in case off CM's. You required identical and program because which is hard to keep, and they kind of fluctuate away from one another, shift away from one another. And if you control that, of course that you can control the performance. So actually one can ask if whether or not this is a universal bottleneck and it seems so aside, I will argue next. Um, we can recall a fundamental result by by showing harder in reaction Target from 1978. Who says that it's a purely computer science proof that if you are able toe, compute the addition multiplication division off riel variables with infinite precision, then you could solve any complete problems in polynomial time. It doesn't actually proposals all where he just chose mathematically that this would be the case. Now, of course, in Real warned, you have also precision. So the next question is, how does that affect the competition about problems? This is what you're after. Lots of precision means information also, or entropy production. Eso what you're really looking at the relationship between hardness and cost of computing off a problem. Uh, and according to Sean Hagar, there's this left branch which in principle could be polynomial time. But the question whether or not this is achievable that is not achievable, but something more cheerful. That's on the right hand side. There's always going to be some information loss, so mental degeneration that could keep you away from possibly from point normal time. So this is what we like to understand, and this information laws the source off. This is not just always I will argue, uh, in any physical system, but it's also off algorithm nature, so that is a questionable area or approach. But China gets results. Security theoretical. No, actual solar is proposed. So we can ask, you know, just theoretically get out off. Curiosity would in principle be such soldiers because it is not proposing a soldier with such properties. In principle, if if you want to look mathematically precisely what the solar does would have the right properties on, I argue. Yes, I don't have a mathematical proof, but I have some arguments that that would be the case. And this is the case for actually our city there solver that if you could calculate its trajectory in a loss this way, then it would be, uh, would solve epic complete problems in polynomial continuous time. Now, as a matter of fact, this a bit more difficult question, because time in all these can be re scared however you want. So what? Burns says that you actually have to measure the length of the trajectory, which is a new variant off the dynamical system or property dynamical system, not off its parameters ization. And we did that. So Suba Corral, my student did that first, improving on the stiffness off the problem off the integrations, using implicit solvers and some smart tricks such that you actually are closer to the actual trajectory and using the same approach. You know what fraction off problems you can solve? We did not give the length of the trajectory. You find that it is putting on nearly scaling the problem sites we have putting on your skin complexity. That means that our solar is both Polly length and, as it is, defined it also poorly time analog solver. But if you look at as a discreet algorithm, if you measure the discrete steps on a digital machine, it is an exponential solver. And the reason is because off all these stiffness, every integrator has tow truck it digitizing truncate the equations, and what it has to do is to keep the integration between the so called stability region for for that scheme, and you have to keep this product within a grimace of Jacoby in and the step size read in this region. If you use explicit methods. You want to stay within this region? Uh, but what happens that some off the Eigen values grow fast for Steve problems, and then you're you're forced to reduce that t so the product stays in this bonded domain, which means that now you have to you're forced to take smaller and smaller times, So you're you're freezing out the integration and what I will show you. That's the case. Now you can move to increase its soldiers, which is which is a tree. In this case, you have to make domain is actually on the outside. But what happens in this case is some of the Eigen values of the Jacobean, also, for six systems, start to move to zero. As they're moving to zero, they're going to enter this instability region, so your soul is going to try to keep it out, so it's going to increase the data T. But if you increase that to increase the truncation hours, so you get randomized, uh, in the large search space, so it's it's really not, uh, not going to work out. Now, one can sort off introduce a theory or language to discuss computational and are computational complexity, using the language from dynamical systems theory. But basically I I don't have time to go into this, but you have for heart problems. Security object the chaotic satellite Ouch! In the middle of the search space somewhere, and that dictates how the dynamics happens and variant properties off the dynamics. Of course, off that saddle is what the targets performance and many things, so a new, important measure that we find that it's also helpful in describing thesis. Another complexity is the so called called Makarov, or metric entropy and basically what this does in an intuitive A eyes, uh, to describe the rate at which the uncertainty containing the insignificant digits off a trajectory in the back, the flow towards the significant ones as you lose information because off arrows being, uh grown or are developed in tow. Larger errors in an exponential at an exponential rate because you have positively up north spawning. But this is an in variant property. It's the property of the set of all. This is not how you compute them, and it's really the interesting create off accuracy philosopher dynamical system. A zay said that you have in such a high dimensional that I'm consistent were positive and negatively upon of exponents. Aziz Many The total is the dimension of space and user dimension, the number off unstable manifold dimensions and as Saddam was stable, manifold direction. And there's an interesting and I think, important passion, equality, equality called the passion, equality that connect the information theoretic aspect the rate off information loss with the geometric rate of which trajectory separate minus kappa, which is the escape rate that I already talked about. Now one can actually prove a simple theorems like back off the envelope calculation. The idea here is that you know the rate at which the largest rated, which closely started trajectory separate from one another. So now you can say that, uh, that is fine, as long as my trajectory finds the solution before the projective separate too quickly. In that case, I can have the hope that if I start from some region off the face base, several close early started trajectories, they kind of go into the same solution orphaned and and that's that's That's this upper bound of this limit, and it is really showing that it has to be. It's an exponentially small number. What? It depends on the end dependence off the exponents right here, which combines information loss rate and the social time performance. So these, if this exponents here or that has a large independence or river linear independence, then you then you really have to start, uh, trajectories exponentially closer to one another in orderto end up in the same order. So this is sort off like the direction that you're going in tow, and this formulation is applicable toe all dynamical systems, uh, deterministic dynamical systems. And I think we can We can expand this further because, uh, there is, ah, way off getting the expression for the escaped rate in terms off n the number of variables from cycle expansions that I don't have time to talk about. What? It's kind of like a program that you can try toe pursuit, and this is it. So the conclusions I think of self explanatory I think there is a lot of future in in, uh, in an allo. Continue start computing. Um, they can be efficient by orders of magnitude and digital ones in solving empty heart problems because, first of all, many of the systems you like the phone line and bottleneck. There's parallelism involved, and and you can also have a large spectrum or continues time, time dynamical algorithms than discrete ones. And you know. But we also have to be mindful off. What are the possibility of what are the limits? And 11 open question is very important. Open question is, you know, what are these limits? Is there some kind off no go theory? And that tells you that you can never perform better than this limit or that limit? And I think that's that's the exciting part toe to derive thes thes this levian 10.

>>Hello. I miss Caribbean, the principal Off Lens Open Source project and senior director off Engineering at Mirandes. I'm excited to be here today at launch back 2020 Virtual conference. I will be your guide, helping you to navigate the rough waters off opportunities and containers and show you the way how to take full advantage off this great new technology with help off lens. The Coburn Edie's idea. It's happening all around us. Containers and Coburn ET is everywhere. Every day, hundreds of thousands off people create new clusters. Develop containerized application on they deploy those applications on top of Cuban Edie's. It has become the golden standard for container orchestration. How did we get here? The industry has been very creative and innovative in ways how to burn it is has been marketed with the help off develops movement, empowering individual development teams leveraging 12 factor model on infrastructure. As a code principles, we have created the need for a system that is able to obstruct everything. That's one a single system to rule them all. Cooper needs has become this system. It has become the operating system for cloud. But hey, people say Coburn Ages is difficult and complex. Absolutely many people on organizations are struggling to adopt kubernetes at scale terrorist complexity on complexity on top of complexity. On top off this, you might need to unlearn some of the things you have used to do in the past. Having had chance to speak with hundreds off, Cooper needs users on operators, from beginners to ninja level hackers. I feel Coburn Edie's is not too difficult or complex. People will get this perception on Lee when they are using primitive or were limited tools for job, or if they have failed to address the needs off all different stakeholders. By using proper quality tools and products, we can truly harness the power of communities on radically improved the speed of business To get there. In my mind, we have deserved at least two important stakeholders. First, mhm. We have hopes and idea means who want to use system for centralized kubernetes cluster creation operations and management in a listen take care a lot about underlying infrastructure, security and conformance. The industry has been serving teas people very well. He has an amazing products for this segment. Dr Enterprise Container Cloud. It's a great example off such a product. Secondly, we have developers who are, in fact the consumers off. The clusters provided by the ops and I T at means they are the people who actually access the clusters on daily basis. Take deploy, run, managed, debug, inspect on observed the workloads running on top of communities. The availability and quality off tools and products for this segment has been lacking. See, very luckily, that's not the case anymore. And that's the focus off my talk today to take away. I want you to have from this simple unless we have quality tools and products for both off these important stakeholders, we might not get all the benefits we were looking for. Docker Enterprise Container Cloud. We'll get you on top and when combined with the product, I'm about to talk. Next. We'll take you where you wanna be. I'm so excited about this lens. The Cooper needs I D. I. D stands for integrated development environment. We could call it in the credit operations environment as well, but let's stick with I D for a little bit longer. No, If you would be doing non virtual conference, I would be as asking how many off you have heard or actually tried using less >>before. It's okay, Let's make make it interactive. We can still do it all right. I'm probably I would see something to 20% of people raising their hands. To be honest, I'm amazed how many people have started using lens already. It's been out on Lee for just six months or so. Lens combines all a sense of tools and technologies >>required for streamlining cloud native applicants and development on Day two operations. It's all you need to take control off Coburn. Edie's clusters on workloads running on top, for example, you might have find hard time trying to understand what is really going on in your clusters with lens. You will have complete situational awareness off all your clusters on work clothes, and you will understand what's going on on quickly. Take actions if needed. Lenses designed for developers who need to work with Cooper needs on a daily basis. If you have somebody who is just getting started, lens will lower the barrier of entry because it will let you explore your clusters on workloads very easy. Take action to try out different things on diesel eyes, everything in a way that makes sense on provides full context. If you are very experienced ninja level heavy user, you will get things done fast. In essence, by using lens, you will become more productive on the quality off life is improved a lot lenses. A stand alone desktop application for Mac OS Windows and Linux operating systems. It's free and fully open. Source under Emmett license. If you want to get started, simply download the lens application from Lens website and start adding your clusters. Now you might wonder. How does lend play together with Mirandes >>offering sheep code faster at Mirandes, we want to convert open source innovation in the customer value. We want to be best in the world. At this. We want to increase developer velocity to continuously deliver code faster for public and private clouds. And in order to do that, we want to put capable person in the center. We want to invest in products and technologies that will improve the developer productivity that speed sheep gold faster. To have speed, we got to get right amount off simplicity. Choice on security simplicity does not mean less features. It means amazing usability on developer experience for using complex on feature rich systems Under the hood. Security means invisible security, something that is built into the system from >>beginning on its part of its DNA, something that is automatically applied to the underlying infrastructure and software running on top without need for developers to worry about too much choice. It's include chance. You should be able to choose the parts you want to use, for example, choice of the infrastructure, cloud providers or even host operating system running on your machines. Everything in here comes to life with talker in the price container cloud. Combined with lens, it's the end to end solution for harnessing the power of kubernetes and radically improving the speed of business. >>All right, I hope you got the idea how lens will play together with Mirandes offering on a highly law. Now I'd like to talk more about lens features in detail. Let's kick off with multi cluster management. Unlike multi cluster management systems designed for hopes and ideas, New people peace is the Monte Cluster management from the developers point of view, take a nap. Any number >>of cabernet, these clusters to provide quick and easy way to switch cluster context on access workloads Running on top thes clusters may be the ones provide provided by their hopes and ideas mean people, but they might be clusters running locally, used in some other projects or use for hobby purposes. As an example, the clusters are added simply simply by importing the cube conflict file and selecting the cluster context. Once added, it's fast and easy to switch between clusters. Since the requirement for acting a cluster is just a cube. Conflict file lens works with any any certified Cooper needs distributions where user might have obtained to keep conflict. Five. For example, Documented price Container Cloud. You see T e. K s G. K. A. K s rancher opens it. Minnick YouTube many, many other flavors off uber Nitties They all work straight out off the box. The creating above lens is that you will get one unified I e across all your clusters. >>No matter what's the flavor on. There is absolutely nothing that you need to install in. Cluster is in itself is great because most off the developers we're talking about in here do not have sufficient right to install anything like this in their clusters. Since we're now talking about access control, let's discuss how the role based access control is taken in account with lens. It's all about uber needs built in role based access control. As you know, clusters may be configured to use any supported identity providers, since lens will authenticate uses the Cooper needs with Cuba conflict file Cooper needs are back is automatically enforced. This is also reflected on the user interface user. Will Onley see those resources they are allowed to access? Lens do not need admin level privileges, service accounts or any other solution that would by bus. The Cooper needs are back. Next. We have a smart terminal less has a built in smart terminal. It comes with bundled common line tools such as cube cattle on help. It's different from your native terminal because the smart terminal will always have cube cattle command available on bond. It will automatically >>switch the version off cube cattle to match the currently selected Cooper Needs Cluster a P I. If FBI compatible version is not found, it will be downloaded automatically in the background. In addition to making sure you are always using the right version off cube kuttel the Smart Terminal will automatically assigned the Cube conflict context to match your currently selected co Bernie. This cluster as a summary. When you use lens with building Smart Terminal, you are always using the right version off cube cattle and context. I feel there is still something more I want to share with you. Visualizations lenses Very diesel on There is a lot of detail in the user experience. One of the great features in Lens is that building in the creation with Prometheus to visualize everything. As you might know, people working on the ups and i d at me inside of things have learned to write complex Primedia Square ease. Most likely, they have created beautiful death sport to look at data. Looking at the cluster's from the bird's eye perspective. If you are a developer, you are interested in your own stuff. Bird side perspective might be nice, but it doesn't help you to debug and trouble. Suit your own application. You don't necessarily have access to or want to learn Prometheus to write your own queries on out of context that sports. That is why lens will provide automatically civilization for all supportive resource types including the aggregated Use it, >>David Little person. Or, to be honest, ops on Idea Means to will get all the data they need, always in the right context. The basic metrics include CPU memory on disk with total capacity actual use. It requests on limits. The unrest metrics include bytes sent success, failure on request and response to race. Both statistics also include network bytes sent and received. Persistent pulling. Unclaimed metrics include disk usage and capacity. Wow, that was a lot on. To be honest, we are just barely scratching the surface off the available features. Let's move on and talk about lens from the community on open source project perspective. We'll start with statistic, not because I like statistics in particular, but because this project has some mind blowing stats to share. Let's remind ourselves that lens was made open source just a half a year ago. Since then, over 600,000 downloads over 50,000 users over >>8000 star gazers on get top. The users come from all around the world. It's one off the fastest training open source projects on git hub and definitely in Cuba needs ecosystem. It's the number one e or u I or whatever you wanna call it for Cuban, it is on. If you are not using it yet, you're probably missing out some something great. What's coming on next? We are working hard every day to make lens better. Our focus as a leader in this open source project is to remain vendor Notre Look Ways for collaboration with other vendors in the cloud Native technology ecosystem on focus on making features that directing most value for our users. Against this background, the near future roadmap includes exciting features like extensive a P I. While the building features off, lens might feel great. It's just the beginning. Lens extensive a p I that is going to be a new feature released as part off Lens 4.0, we'll let you at custom visualizations on functionality to support your preferred development. Work flaws. The Extensions AP I will provide options for extensive creators to but directly into the lens You I we are already working with the number off cloud Native technology ecosystem vendors to get their technology is deeply integrated on therefore more accessible true lens, for example, on extension for a container >>image scanning technology vendor, I might add a warning icon next to a port or a deployment where vulnerable image is detected in a decent. This extension might provide more details about this vulnerability when the port or deployment is clicked. This is just a simple example, but I hope you get the idea on Really, this is just beginning. We want to >>bring entire Coburn Edie's ecosystem together in a listen to extensions. A p I. We will work on features to enhance Cooper needs Developer were close, both locally on remote, enable teamwork and naturally improve the usability on fixed box reported by our users. There are so many great things coming. It's impossible to list everything in here. If you are interested, please take a look at the epics listed on our guitar free ball. Once again, if you're not using lens already, you're probably missing out on something great. Download and get started today. For the most amazing entrant experience, check out the Docker Enterprise Container Cloud as well. I wish you all a great time with Coburn. Edie's I'm looking forward to meet you all in person someday. Take care. Bye bye